Textile Company A: Business Intelligence System
IS 4030 - Business Intelligence
University of Cincinnati
Introduction
Textile Company A is a small business in North Carolina that is currently struggling due to the
changing global environment. The organization is engaged in the purchase of textiles in
Europe, the Middle and Far East, as well as their distribution in the United States. The
complication of global supply chains leads to a constant increase in the cost of purchasing
textiles, which negatively affects the company’s profits. To overcome this crisis, Textile
Company A plans to expand its operations to South America and Canada. In this regard, the
company needs to upgrade its information infrastructure for better data analytics within the
company.
Prior to the implementation of the project to create a business intelligence (BI) system, the
organization used an outdated approach to storing and analyzing data. It was minimally
automated and did not allow working effectively with a large amount of data. In particular, the
company used several separate data sources (CRM, ERP, individual databases, and external
sources) and generated individual reports based on the data received from each of them. This
approach made holistic data analytics and information-driven decision-making more difficult.
A diagram illustrating the old data analytics system at Textile Company A is presented in Figure
1.
Figure 1: Data Analytics System in Textile Company A before the Project Completion.
As part of the project, it was possible to create a new BI infrastructure in the organization,
which fully automated the company’s interaction with data. It was possible to create a structure
consisting of ETL tools (Extract, Transform, Load), a data warehouse, online analytical
processing (OLAP), and data marts. A detailed scheme of the BI infrastructure created for
Textile Company A is illustrated in Figure 2. As a result, this infrastructure uses all the
company’s data sources for analytics and generates the most informative reports for different
organization reports. Additionally, BI helps in structuring data and filtering out unnecessary
information. Textile Company A can now use the framework for descriptive and predictive
analytics. This allows the company to streamline existing processes and make better decisions
for future expansion planning.
Figure 2: BI Infrastructure for Data Analytics in Textile Company A after the Project
Completion.
The project started with an evaluation of the data analytics structure that existed in the
company. After assessing the available resources and the needs of the organization, decisions
were made regarding suitable storage solutions as well as BI tools. Further, the BI infrastructure
was planned, and the steps for its implementation into the existing information structure of the
company took into account all the necessary changes. After that, BI was implemented and
integrated into the analytics structure of Textile Company A. At the last stage, tests were carried
out to evaluate performance, and employees were trained to work with the end-user interface.
Review of Other Work
The materials considered in the project proposal made it possible to effectively plan the
development and implementation of the BI solution. However, during the implementation of
the project, additional research was required to identify points for improving the existing
design. The paper by Dieni et al. (2021) describes the process of creating a BI infrastructure
for unemployment rate analysis and management for the Central Bureau of Statistics of
Indonesia. Although this article does not belong to the scope of SMEs, it provides a detailed
description of all stages of the process of developing and implementing a BI infrastructure.
Dieni et al. (2021) pay great attention to the development stage of the ETL tool, which, as
emphasized, is the most significant for the correct operation of the BI structure. Based on the
analysis presented in the article, it was decided to use the Pentaho BI solution as the main ETL
tool, as well as a resource for the cloud data warehouse. This approach can both reduce the cost
of creating a BI infrastructure and provide a company with additional options for the future
expansion of an information-analytical system.
During the implementation of the project, the possible use of alternative solutions to create the
most balanced infrastructure was also considered. At the planning stage, it was decided to use
a hybrid approach to the data warehouse: both cloud-based and physical storage. Garani et al.
(2019) analyzed the case of implementing the BI solution in a telecommunication company.
They focus on the design of a data warehouse that would be effective for all the business tasks
required by the organization. As part of the research, Garani et al. (2019) propose the scheme
of the data warehouse containing five dimensions of data sets divided into different categories
(customers, calls, contracts, employment, and product). Most importantly, they explain how
each of those data sets is related to OLAP structures, which are used in the current project as
well. This study showed how to structure a data warehouse information system in the most
efficient way in order for it to work best with data OLAP. Additionally, the article confirmed
the need to use cloud-based storage for interaction with OLAP in conjunction with a physical
warehouse.
On the whole, OLAP and data warehouse design and implementation are the most challenging
parts of the project. At the stage of developing these parts of the infrastructure, many questions
arose regarding how best to organize these elements relative to each other. Al-Aqrabi et al.
(2019) present a case study on the integration of OLAP into the BI infrastructure. Most valuable
is that in this article, the researchers offer a detailed description of the OLAP framework and
describe the elements that interact most effectively with various types of data warehouse
solutions. The use of cloud-based BI data storage when integrating OLAP requires “compliance
of the BI application with web services architectural standards (and the standards defined by
the SaaS or PaaS provider, like Google Apps standards)” (Al-Aqrabi et al., 2019, p. 81). This
information is extremely important for the implementation of the project, as it allows you to
take into account the compatibility of infrastructure elements.
Thus, the literature reviewed in the project proposal helped in the analysis and evaluation of
the existing analytical structure of the company, as well as in the selection and design of a
relevant BI system. However, during the implementation of the project, it was necessary to
optimize the design of the infrastructure in the most efficient way so that it meets the needs of
the company and uses a limited amount of resources. The materials considered during the
implementation and described in this section made it possible to take into account the subtleties
of the design of BI infrastructures that were not obvious during preliminary planning. The study
of these sources will help create the most effective structure and avoid possible difficulties with
its operation.
Changes to the Project Environment
Before the implementation of the project, the analytical infrastructure of Textile Company A
was extremely outdated and required an upgrade in accordance with modern technical
standards. The company used manual data analysis of the companies listed in the databases,
which was carried out by the analytical department. After that, based on the analytics, reports
were compiled for the company as a whole without taking into account the needs of individual
departments. This structure was slow and inflexible and could also contain a large number of
errors and irrelevant information. This situation made it impossible to effectively optimize the
company’s operations, as well as plan for expansion.
The BI infrastructure implementation project has significantly changed the company’s
decision-making process based on the received analytical data. Most importantly, data analytics
has become as automated as possible, which has increased the efficiency of using the
information in the company at times. In particular, the ETL tool made it possible to filter out
irrelevant data, which minimized the number of errors. The inclusion of March data in the BI
infrastructure has allowed departments to receive personalized analytical reports based on their
needs. Additionally, the use of OPLA cubes has made the structure extremely stable in the event
of a future increase in the number of databases and an increase in the number of queries.
Overall, the implementation of the BI infrastructure has allowed Textile Company A to use
descriptive analytics to optimize its operations and predictive analytics to plan the company’s
expansion. It is also important that employees of various departments have access to
multidimensional information for better decision-making. Data analytics in the company has
become efficient, eliminating errors and providing the most relevant and important
information. This system can become the basis for the future development of the company and
a significant increase in its performance.
Methodology
ADDIE methodology model was chosen for the project execution as it presents the most
appropriate approach. This model includes five crucial steps: analysis, design, development,
implementation, and evaluation. The main advantage of this structure is the sequence of steps
and flexibility in the implementation of the project. In particular, ADDIE involves evaluation
after each of the major stages of project execution, which allows you to adjust its development
depending on the emerging conditions.
The analysis stage includes goal-setting and the resources available for the project. This step
also identifies the problem the business has and offers a set of potential solutions that could
address it. At the design stage, the specific tools that will be used within the project are
determined, deliverables are developed, as well as project evaluation criteria. Those two steps
have already been completed under this proposal and are described in the project overview
sections, which contain a description of the problem and the relevant IT solution.
The development stage started with the assignment of the analytical team, which will work
with ready-made BI. This step also includes the choice of the warehouse, the analytical
architecture of the system, and the end-user interface, as well as setting data integration tools.
The implementation of the project will include the creation of a fully functional BI structure
for Textile Company A and the completion of the onboarding of the analytical team. The final
stage of the project is evaluation, which requires tests to create informational reports for various
departments.
Project Goals and Objectives
Goal
Supporting objectives
Deliverables enabling the project
objectives
Met/Unmet
1. BI infrastructure features
assessment and planning
1.a. BI implementation
strategy development
1.a.i. Data sources and analytical tools
review
Met
1.a.ii. Setting objectives
Met
Goal
Supporting objectives
Deliverables enabling the project
objectives
Met/Unmet
1.a.iii. Tools assessment
Met
1.b. Defining key BI
characteristics
1.b.i. BI team assignment
Met
1.b.ii. Define KPIs
Met
2. BI infrastructure design and
implementation
2.a. Setting data integration
tools
2.a.i. Setting data warehouse
Met
2.a.ii. Setting data integration tools or
ETL (Extract, Transform, Load)
Met
2.b. Choosing an approach for
architecture design
2.b.i. Data warehouse connection
Met
2.b.ii. Setting online analytical
processing (OLAP)
Met
2.b.iii. Setting data marts
Met
2.c. The end-user interface
implementation
2.c.i. Setting ad hoc reporting systems
Met
2.c.ii. Conduct end-user training
Met
Goal 1
BI infrastructure features assessment and planning. At this stage, it is necessary to evaluate the
existing resources, infrastructure, goals, and responsible persons, as well as a potential vendor
for the implementation of BI. This goal has been met when all key aspects of the BI solution
are selected and clearly defined based on available data and based on the objectives of the
organization. This goal included two objectives:
Objective a
BI implementation strategy development. This objective includes activities to evaluate the
company’s current resources and infrastructures and appropriate changes that have to be made
in order to achieve the company’s goals. The objective was considered successful when all the
decisions regarding BI infrastructure design were made, including the choice of relevant and
available tools.
Objective b
Defining key BI characteristics. This objective allows the project to scale the required BI
structure and exclude features that the company does not need. At this stage, it is important to
assess the competencies available to the organization, as well as the KPIs that are key to
monitoring. The objective was considered successful when the complete diagram of the BI
infrastructure was created.
Goal 2
BI infrastructure design and implementation. This goal is key within the framework of this
project, as it involves the creation of a functioning BI infrastructure as a result. This objective
is supported by three objectives, which include setting data integration tools, BI architecture
design, and end-user interface implementation. These objectives describe the key aspects of
the software architecture BI solution and constitute a fully functional structure.
Objective a
Setting data integration tools. For the correct operation of BI, tools for collecting, processing,
and storing data are required, which requires the choice of data warehouse and data integration
tools. At the moment, cloud data storages are the most common, which is especially important
for SMEs. The objective was considered successful when the data integration tool was installed
and connected to the company’s databases.
Objective b
Choosing an approach for architecture design. At this stage, it is necessary to decide, based on
the needs and resources of the company, which BI architecture is the most relevant. In this
situation, the company plans to expand, so it would be logical to use a hybrid setup (warehouse
+ OLAP + data marts) to increase the potential duration of the BI solution. The objective was
considered successful when the data warehouse, OLAPs, and data marts were installed and
connected to the ETL tool.
Objective c
The end-user interface implementation. At this stage, it is important to choose a tool that will
allow the company to effectively represent the collected data in the form of analytical reports
for employees. The objective was considered successful when the end-user interface was
installed and connected to the BI structure.
Project Timeline
Milestone or deliverable
Planned
duration
(hours or
days)
Actual
duration
(hours or
days)
Projected
start date
Anticipated
end date
Actual start
date
Anticipated
start date
Project goals and objectives
discussion meeting
1 day
1 day
9.18.2024
9.18.2024
9.18.2024
9.18.2024
Determine existing data
analytics structure
2 days
2 days
9.19.2024
9.20.2024
9.19.2024
9.20.2024
Identify the problems existing
in the analytics structure
2 days
2 days
9.21.2024
9.22.2024
9.21.2024
9.22.2024
Identify adjustments needed
based on company’s goals and
resources
2 days
2 days
9.23.2024
9.24.2024
9.23.2024
9.24.2024
BI team assignment and
training
5 days
5 days
9.24.2024
9.28.2024
9.24.2024
9.28.2024
BI infrastructure design with
identification of appropriate
tools
5 days
5 days
9.29.2024
10.3.2024
9.29.2024
10.3.2024
BI infrastructure setting
10 days
12 days
10.4.2024
10.13.2024
10.04.2024
10.15.2024
BI infrastructure testing
2 days
2 days
10.14.2024
10.15.2024
10.16.2024
10.17.2024
End-users training
5 days
5 days
10.16.2024
10.20.2024
10.17.2024
10.21.2024
The main stages of the project implementation took place according to the schedule, as the
project team worked well-coordinated and according to a well-thought-out preliminary plan.
However, the delay occurred during the BI infrastructure setting phase, which was scheduled
to be completed in 10 days. Instead, the process took 12 days, as there were difficulties
connecting the company’s databases to the ETL tool, which required additional research and
planning. Difficulties arose due to the incompatibility of existing databases with the selected
ETL tool. After reviewing the materials described in the review of the work section, it was
decided to switch to Pentaho ETL solution, which required additional settings. Thus, the project
implementation ended on 10.22.2024 instead of 10.20.2024 with a delay of less than 2 days.
Unanticipated Requirements
An unanticipated requirement that arose during the implementation of the project was the need
to choose a more relevant ETL tool. The problem arose during setting up the BI infrastructure
and testing its operation. Initially, it was planned to use the Apache Spark tool as an ETL since
it is free and easy to implement. However, later it was discovered that this Apache Spark does
not correctly interact with various file formats, including PDF, which are widely used in
company databases. This problem led to incorrect data filtering, which drastically reduced the
effectiveness of the tool. Thus, it was decided to choose a more suitable instrument, which
resulted in the need for additional research and a slight delay. As a result, it was decided to use
Pentaho because this structure is able to work with a variety of files, and it is also widely
considered in the literature and is popular in case studies. The main reason for choosing this
solution was the widespread use of Pentaho among SMEs, which is most relevant for this
project.
Conclusions
The main result of the project implementation is the creation of a fully functional BI
infrastructure in Textile Company A. The potential effect of the project is the ability to
effectively use descriptive and predictive analytics in the organization to optimize current
operations and plan for expansion. Among the immediately observed effects, one can single
out the complete automation of the process of storing, analyzing data, and generating
information reports. Most importantly, the ETL tool integrated into the infrastructure allows
the company to minimize the amount of irrelevant information through the sorting of data in
the databases used. The project is considered successful because, as a result of the
implementation of the BI infrastructure, it became possible to generate multidimensional
informational reports that various departments of the company can use for a more efficient
decision-making process.
Project Deliverables
Figure 1 shows a diagram of the data analytics structure that existed at Textile Company A
before the project was implemented. Previously, the company used manual data analysis and
the generation of information reports, which were handled by employees of the analytical
department, which resulted in low efficiency of data analysis. Figure 2 illustrates the BI
infrastructure diagram that was created at Textile Company A as part of the project
implementation. This scheme describes all the key elements of the system and their interaction.
Based on this illustration, it can be judged that the project made it possible to automate the
process of data analytics in the company in order to increase the efficiency of the decision-
making process based on reports. Finally, Figure 3 contains an example of a multidimensional
report that is generated by the BI infrastructure created within the project. This illustration is
the most important deliverable of the project, as it shows the actual result of its implementation.
Figure 3: The Example of Multidimensional Report Generated by BI Infrastructure
Visualization Tool.
Textile Company A is a small business in North Carolina that is currently struggling due to the
changing global environment. The organization is engaged in the purchase of textiles in
Europe, the Middle and Far East, as well as their distribution in the United States. The
complication of global supply chains leads to a constant increase in the cost of purchasing
textiles, which negatively affects the company’s profits. To overcome this crisis, Textile
Company A plans to expand its operations to South America and Canada. In this regard, the
company needs to upgrade its information infrastructure for better data analytics within the
company.
Prior to the implementation of the project to create a business intelligence (BI) system, the
organization used an outdated approach to storing and analyzing data. It was minimally
automated and did not allow working effectively with a large amount of data. In particular, the
company used several separate data sources (CRM, ERP, individual databases, and external
sources) and generated individual reports based on the data received from each of them. This
approach made holistic data analytics and information-driven decision-making more difficult.
A diagram illustrating the old data analytics system at Textile Company A is presented in Figure
1.
Figure 1: Data Analytics System in Textile Company A before the Project Completion.
As part of the project, it was possible to create a new BI infrastructure in the organization,
which fully automated the company’s interaction with data. It was possible to create a structure
consisting of ETL tools (Extract, Transform, Load), a data warehouse, online analytical
processing (OLAP), and data marts. A detailed scheme of the BI infrastructure created for
Textile Company A is illustrated in Figure 2. As a result, this infrastructure uses all the
company’s data sources for analytics and generates the most informative reports for different
organization reports. Additionally, BI helps in structuring data and filtering out unnecessary
information. Textile Company A can now use the framework for descriptive and predictive
analytics. This allows the company to streamline existing processes and make better decisions
for future expansion planning.
Figure 2: BI Infrastructure for Data Analytics in Textile Company A after the Project
Completion.
The project started with an evaluation of the data analytics structure that existed in the
company. After assessing the available resources and the needs of the organization, decisions
were made regarding suitable storage solutions as well as BI tools. Further, the BI infrastructure
was planned, and the steps for its implementation into the existing information structure of the
company took into account all the necessary changes. After that, BI was implemented and
integrated into the analytics structure of Textile Company A. At the last stage, tests were carried
out to evaluate performance, and employees were trained to work with the end-user interface.
Review of Other Work
The materials considered in the project proposal made it possible to effectively plan the
development and implementation of the BI solution. However, during the implementation of
the project, additional research was required to identify points for improving the existing
design. The paper by Dieni et al. (2021) describes the process of creating a BI infrastructure
for unemployment rate analysis and management for the Central Bureau of Statistics of
Indonesia. Although this article does not belong to the scope of SMEs, it provides a detailed
description of all stages of the process of developing and implementing a BI infrastructure.
Dieni et al. (2021) pay great attention to the development stage of the ETL tool, which, as
emphasized, is the most significant for the correct operation of the BI structure. Based on the
analysis presented in the article, it was decided to use the Pentaho BI solution as the main ETL
tool, as well as a resource for the cloud data warehouse. This approach can both reduce the cost
of creating a BI infrastructure and provide a company with additional options for the future
expansion of an information-analytical system.
During the implementation of the project, the possible use of alternative solutions to create the
most balanced infrastructure was also considered. At the planning stage, it was decided to use
a hybrid approach to the data warehouse: both cloud-based and physical storage. Garani et al.
(2019) analyzed the case of implementing the BI solution in a telecommunication company.
They focus on the design of a data warehouse that would be effective for all the business tasks
required by the organization. As part of the research, Garani et al. (2019) propose the scheme
of the data warehouse containing five dimensions of data sets divided into different categories
(customers, calls, contracts, employment, and product). Most importantly, they explain how
each of those data sets is related to OLAP structures, which are used in the current project as
well. This study showed how to structure a data warehouse information system in the most
efficient way in order for it to work best with data OLAP. Additionally, the article confirmed
the need to use cloud-based storage for interaction with OLAP in conjunction with a physical
warehouse.
On the whole, OLAP and data warehouse design and implementation are the most challenging
parts of the project. At the stage of developing these parts of the infrastructure, many questions
arose regarding how best to organize these elements relative to each other. Al-Aqrabi et al.
(2019) present a case study on the integration of OLAP into the BI infrastructure. Most valuable
is that in this article, the researchers offer a detailed description of the OLAP framework and
describe the elements that interact most effectively with various types of data warehouse
solutions. The use of cloud-based BI data storage when integrating OLAP requires “compliance
of the BI application with web services architectural standards (and the standards defined by
the SaaS or PaaS provider, like Google Apps standards)” (Al-Aqrabi et al., 2019, p. 81). This
information is extremely important for the implementation of the project, as it allows you to
take into account the compatibility of infrastructure elements.
Thus, the literature reviewed in the project proposal helped in the analysis and evaluation of
the existing analytical structure of the company, as well as in the selection and design of a
relevant BI system. However, during the implementation of the project, it was necessary to
optimize the design of the infrastructure in the most efficient way so that it meets the needs of
the company and uses a limited amount of resources. The materials considered during the
implementation and described in this section made it possible to take into account the subtleties
of the design of BI infrastructures that were not obvious during preliminary planning. The study
of these sources will help create the most effective structure and avoid possible difficulties with
its operation.
Changes to the Project Environment
Before the implementation of the project, the analytical infrastructure of Textile Company A
was extremely outdated and required an upgrade in accordance with modern technical
standards. The company used manual data analysis of the companies listed in the databases,
which was carried out by the analytical department. After that, based on the analytics, reports
were compiled for the company as a whole without taking into account the needs of individual
departments. This structure was slow and inflexible and could also contain a large number of
errors and irrelevant information. This situation made it impossible to effectively optimize the
company’s operations, as well as plan for expansion.
The BI infrastructure implementation project has significantly changed the company’s
decision-making process based on the received analytical data. Most importantly, data analytics
has become as automated as possible, which has increased the efficiency of using the
information in the company at times. In particular, the ETL tool made it possible to filter out
irrelevant data, which minimized the number of errors. The inclusion of March data in the BI
infrastructure has allowed departments to receive personalized analytical reports based on their
needs. Additionally, the use of OPLA cubes has made the structure extremely stable in the event
of a future increase in the number of databases and an increase in the number of queries.
Overall, the implementation of the BI infrastructure has allowed Textile Company A to use
descriptive analytics to optimize its operations and predictive analytics to plan the company’s
expansion. It is also important that employees of various departments have access to
multidimensional information for better decision-making. Data analytics in the company has
become efficient, eliminating errors and providing the most relevant and important
information. This system can become the basis for the future development of the company and
a significant increase in its performance.
Methodology
ADDIE methodology model was chosen for the project execution as it presents the most
appropriate approach. This model includes five crucial steps: analysis, design, development,
implementation, and evaluation. The main advantage of this structure is the sequence of steps
and flexibility in the implementation of the project. In particular, ADDIE involves evaluation
after each of the major stages of project execution, which allows you to adjust its development
depending on the emerging conditions.
The analysis stage includes goal-setting and the resources available for the project. This step
also identifies the problem the business has and offers a set of potential solutions that could
address it. At the design stage, the specific tools that will be used within the project are
determined, deliverables are developed, as well as project evaluation criteria. Those two steps
have already been completed under this proposal and are described in the project overview
sections, which contain a description of the problem and the relevant IT solution.
The development stage started with the assignment of the analytical team, which will work
with ready-made BI. This step also includes the choice of the warehouse, the analytical
architecture of the system, and the end-user interface, as well as setting data integration tools.
The implementation of the project will include the creation of a fully functional BI structure
for Textile Company A and the completion of the onboarding of the analytical team. The final
stage of the project is evaluation, which requires tests to create informational reports for various
departments.
Project Goals and Objectives
Goal
Supporting objectives
Deliverables enabling the project
objectives
Met/Unmet
1. BI infrastructure features
assessment and planning
1.a. BI implementation
strategy development
1.a.i. Data sources and analytical tools
review
Met
1.a.ii. Setting objectives
Met
1.a.iii. Tools assessment
Met
1.b. Defining key BI
characteristics
1.b.i. BI team assignment
Met
1.b.ii. Define KPIs
Met
2. BI infrastructure design and
implementation
2.a. Setting data integration
tools
2.a.i. Setting data warehouse
Met
2.a.ii. Setting data integration tools or
ETL (Extract, Transform, Load)
Met
Goal
Supporting objectives
Deliverables enabling the project
objectives
Met/Unmet
2.b. Choosing an approach for
architecture design
2.b.i. Data warehouse connection
Met
2.b.ii. Setting online analytical
processing (OLAP)
Met
2.b.iii. Setting data marts
Met
2.c. The end-user interface
implementation
2.c.i. Setting ad hoc reporting systems
Met
2.c.ii. Conduct end-user training
Met
Goal 1
BI infrastructure features assessment and planning. At this stage, it is necessary to evaluate the
existing resources, infrastructure, goals, and responsible persons, as well as a potential vendor
for the implementation of BI. This goal has been met when all key aspects of the BI solution
are selected and clearly defined based on available data and based on the objectives of the
organization. This goal included two objectives:
Objective a
BI implementation strategy development. This objective includes activities to evaluate the
company’s current resources and infrastructures and appropriate changes that have to be made
in order to achieve the company’s goals. The objective was considered successful when all the
decisions regarding BI infrastructure design were made, including the choice of relevant and
available tools.
Objective b
Defining key BI characteristics. This objective allows the project to scale the required BI
structure and exclude features that the company does not need. At this stage, it is important to
assess the competencies available to the organization, as well as the KPIs that are key to
monitoring. The objective was considered successful when the complete diagram of the BI
infrastructure was created.
Goal 2
BI infrastructure design and implementation. This goal is key within the framework of this
project, as it involves the creation of a functioning BI infrastructure as a result. This objective
is supported by three objectives, which include setting data integration tools, BI architecture
design, and end-user interface implementation. These objectives describe the key aspects of
the software architecture BI solution and constitute a fully functional structure.
Objective a
Setting data integration tools. For the correct operation of BI, tools for collecting, processing,
and storing data are required, which requires the choice of data warehouse and data integration
tools. At the moment, cloud data storages are the most common, which is especially important
for SMEs. The objective was considered successful when the data integration tool was installed
and connected to the company’s databases.
Objective b
Choosing an approach for architecture design. At this stage, it is necessary to decide, based on
the needs and resources of the company, which BI architecture is the most relevant. In this
situation, the company plans to expand, so it would be logical to use a hybrid setup (warehouse
+ OLAP + data marts) to increase the potential duration of the BI solution. The objective was
considered successful when the data warehouse, OLAPs, and data marts were installed and
connected to the ETL tool.
Objective c
The end-user interface implementation. At this stage, it is important to choose a tool that will
allow the company to effectively represent the collected data in the form of analytical reports
for employees. The objective was considered successful when the end-user interface was
installed and connected to the BI structure.
Project Timeline
Milestone or deliverable
Planned
duration
(hours or
days)
Actual
duration
(hours or
days)
Projected
start date
Anticipated
end date
Actual start
date
Anticipated
start date
Project goals and objectives
discussion meeting
1 day
1 day
9.18.2024
9.18.2024
9.18.2024
9.18.2024
Determine existing data
analytics structure
2 days
2 days
9.19.2024
9.20.2024
9.19.2024
9.20.2024
Identify the problems existing
in the analytics structure
2 days
2 days
9.21.2024
9.22.2024
9.21.2024
9.22.2024
Identify adjustments needed
based on company’s goals and
resources
2 days
2 days
9.23.2024
9.24.2024
9.23.2024
9.24.2024
BI team assignment and
training
5 days
5 days
9.24.2024
9.28.2024
9.24.2024
9.28.2024
BI infrastructure design with
identification of appropriate
tools
5 days
5 days
9.29.2024
10.3.2024
9.29.2024
10.3.2024
BI infrastructure setting
10 days
12 days
10.4.2024
10.13.2024
10.04.2024
10.15.2024
BI infrastructure testing
2 days
2 days
10.14.2024
10.15.2024
10.16.2024
10.17.2024
End-users training
5 days
5 days
10.16.2024
10.20.2024
10.17.2024
10.21.2024
The main stages of the project implementation took place according to the schedule, as the
project team worked well-coordinated and according to a well-thought-out preliminary plan.
However, the delay occurred during the BI infrastructure setting phase, which was scheduled
to be completed in 10 days. Instead, the process took 12 days, as there were difficulties
connecting the company’s databases to the ETL tool, which required additional research and
planning. Difficulties arose due to the incompatibility of existing databases with the selected
ETL tool. After reviewing the materials described in the review of the work section, it was
decided to switch to Pentaho ETL solution, which required additional settings. Thus, the project
implementation ended on 10.22.2024 instead of 10.20.2024 with a delay of less than 2 days.
Unanticipated Requirements
An unanticipated requirement that arose during the implementation of the project was the need
to choose a more relevant ETL tool. The problem arose during setting up the BI infrastructure
and testing its operation. Initially, it was planned to use the Apache Spark tool as an ETL since
it is free and easy to implement. However, later it was discovered that this Apache Spark does
not correctly interact with various file formats, including PDF, which are widely used in
company databases. This problem led to incorrect data filtering, which drastically reduced the
effectiveness of the tool. Thus, it was decided to choose a more suitable instrument, which
resulted in the need for additional research and a slight delay. As a result, it was decided to use
Pentaho because this structure is able to work with a variety of files, and it is also widely
considered in the literature and is popular in case studies. The main reason for choosing this
solution was the widespread use of Pentaho among SMEs, which is most relevant for this
project.
Conclusions
The main result of the project implementation is the creation of a fully functional BI
infrastructure in Textile Company A. The potential effect of the project is the ability to
effectively use descriptive and predictive analytics in the organization to optimize current
operations and plan for expansion. Among the immediately observed effects, one can single
out the complete automation of the process of storing, analyzing data, and generating
information reports. Most importantly, the ETL tool integrated into the infrastructure allows
the company to minimize the amount of irrelevant information through the sorting of data in
the databases used. The project is considered successful because, as a result of the
implementation of the BI infrastructure, it became possible to generate multidimensional
informational reports that various departments of the company can use for a more efficient
decision-making process.
Project Deliverables
Figure 1 shows a diagram of the data analytics structure that existed at Textile Company A
before the project was implemented. Previously, the company used manual data analysis and
the generation of information reports, which were handled by employees of the analytical
department, which resulted in low efficiency of data analysis. Figure 2 illustrates the BI
infrastructure diagram that was created at Textile Company A as part of the project
implementation. This scheme describes all the key elements of the system and their interaction.
Based on this illustration, it can be judged that the project made it possible to automate the
process of data analytics in the company in order to increase the efficiency of the decision-
making process based on reports. Finally, Figure 3 contains an example of a multidimensional
report that is generated by the BI infrastructure created within the project. This illustration is
the most important deliverable of the project, as it shows the actual result of its implementation.
Figure 3: The Example of Multidimensional Report Generated by BI Infrastructure
Visualization Tool.
Textile Company A is a small business in North Carolina that is currently struggling due to the
changing global environment. The organization is engaged in the purchase of textiles in
Europe, the Middle and Far East, as well as their distribution in the United States. The
complication of global supply chains leads to a constant increase in the cost of purchasing
textiles, which negatively affects the company’s profits. To overcome this crisis, Textile
Company A plans to expand its operations to South America and Canada. In this regard, the
company needs to upgrade its information infrastructure for better data analytics within the
company.
Prior to the implementation of the project to create a business intelligence (BI) system, the
organization used an outdated approach to storing and analyzing data. It was minimally
automated and did not allow working effectively with a large amount of data. In particular, the
company used several separate data sources (CRM, ERP, individual databases, and external
sources) and generated individual reports based on the data received from each of them. This
approach made holistic data analytics and information-driven decision-making more difficult.
A diagram illustrating the old data analytics system at Textile Company A is presented in Figure
1.
Figure 1: Data Analytics System in Textile Company A before the Project Completion.
As part of the project, it was possible to create a new BI infrastructure in the organization,
which fully automated the company’s interaction with data. It was possible to create a structure
consisting of ETL tools (Extract, Transform, Load), a data warehouse, online analytical
processing (OLAP), and data marts. A detailed scheme of the BI infrastructure created for
Textile Company A is illustrated in Figure 2. As a result, this infrastructure uses all the
company’s data sources for analytics and generates the most informative reports for different
organization reports. Additionally, BI helps in structuring data and filtering out unnecessary
information. Textile Company A can now use the framework for descriptive and predictive
analytics. This allows the company to streamline existing processes and make better decisions
for future expansion planning.
Figure 2: BI Infrastructure for Data Analytics in Textile Company A after the Project
Completion.
The project started with an evaluation of the data analytics structure that existed in the
company. After assessing the available resources and the needs of the organization, decisions
were made regarding suitable storage solutions as well as BI tools. Further, the BI infrastructure
was planned, and the steps for its implementation into the existing information structure of the
company took into account all the necessary changes. After that, BI was implemented and
integrated into the analytics structure of Textile Company A. At the last stage, tests were carried
out to evaluate performance, and employees were trained to work with the end-user interface.
Review of Other Work
The materials considered in the project proposal made it possible to effectively plan the
development and implementation of the BI solution. However, during the implementation of
the project, additional research was required to identify points for improving the existing
design. The paper by Dieni et al. (2021) describes the process of creating a BI infrastructure
for unemployment rate analysis and management for the Central Bureau of Statistics of
Indonesia. Although this article does not belong to the scope of SMEs, it provides a detailed
description of all stages of the process of developing and implementing a BI infrastructure.
Dieni et al. (2021) pay great attention to the development stage of the ETL tool, which, as
emphasized, is the most significant for the correct operation of the BI structure. Based on the
analysis presented in the article, it was decided to use the Pentaho BI solution as the main ETL
tool, as well as a resource for the cloud data warehouse. This approach can both reduce the cost
of creating a BI infrastructure and provide a company with additional options for the future
expansion of an information-analytical system.
During the implementation of the project, the possible use of alternative solutions to create the
most balanced infrastructure was also considered. At the planning stage, it was decided to use
a hybrid approach to the data warehouse: both cloud-based and physical storage. Garani et al.
(2019) analyzed the case of implementing the BI solution in a telecommunication company.
They focus on the design of a data warehouse that would be effective for all the business tasks
required by the organization. As part of the research, Garani et al. (2019) propose the scheme
of the data warehouse containing five dimensions of data sets divided into different categories
(customers, calls, contracts, employment, and product). Most importantly, they explain how
each of those data sets is related to OLAP structures, which are used in the current project as
well. This study showed how to structure a data warehouse information system in the most
efficient way in order for it to work best with data OLAP. Additionally, the article confirmed
the need to use cloud-based storage for interaction with OLAP in conjunction with a physical
warehouse.
On the whole, OLAP and data warehouse design and implementation are the most challenging
parts of the project. At the stage of developing these parts of the infrastructure, many questions
arose regarding how best to organize these elements relative to each other. Al-Aqrabi et al.
(2019) present a case study on the integration of OLAP into the BI infrastructure. Most valuable
is that in this article, the researchers offer a detailed description of the OLAP framework and
describe the elements that interact most effectively with various types of data warehouse
solutions. The use of cloud-based BI data storage when integrating OLAP requires “compliance
of the BI application with web services architectural standards (and the standards defined by
the SaaS or PaaS provider, like Google Apps standards)” (Al-Aqrabi et al., 2019, p. 81). This
information is extremely important for the implementation of the project, as it allows you to
take into account the compatibility of infrastructure elements.
Thus, the literature reviewed in the project proposal helped in the analysis and evaluation of
the existing analytical structure of the company, as well as in the selection and design of a
relevant BI system. However, during the implementation of the project, it was necessary to
optimize the design of the infrastructure in the most efficient way so that it meets the needs of
the company and uses a limited amount of resources. The materials considered during the
implementation and described in this section made it possible to take into account the subtleties
of the design of BI infrastructures that were not obvious during preliminary planning. The study
of these sources will help create the most effective structure and avoid possible difficulties with
its operation.
Changes to the Project Environment
Before the implementation of the project, the analytical infrastructure of Textile Company A
was extremely outdated and required an upgrade in accordance with modern technical
standards. The company used manual data analysis of the companies listed in the databases,
which was carried out by the analytical department. After that, based on the analytics, reports
were compiled for the company as a whole without taking into account the needs of individual
departments. This structure was slow and inflexible and could also contain a large number of
errors and irrelevant information. This situation made it impossible to effectively optimize the
company’s operations, as well as plan for expansion.
The BI infrastructure implementation project has significantly changed the company’s
decision-making process based on the received analytical data. Most importantly, data analytics
has become as automated as possible, which has increased the efficiency of using the
information in the company at times. In particular, the ETL tool made it possible to filter out
irrelevant data, which minimized the number of errors. The inclusion of March data in the BI
infrastructure has allowed departments to receive personalized analytical reports based on their
needs. Additionally, the use of OPLA cubes has made the structure extremely stable in the event
of a future increase in the number of databases and an increase in the number of queries.
Overall, the implementation of the BI infrastructure has allowed Textile Company A to use
descriptive analytics to optimize its operations and predictive analytics to plan the company’s
expansion. It is also important that employees of various departments have access to
multidimensional information for better decision-making. Data analytics in the company has
become efficient, eliminating errors and providing the most relevant and important
information. This system can become the basis for the future development of the company and
a significant increase in its performance.
Methodology
ADDIE methodology model was chosen for the project execution as it presents the most
appropriate approach. This model includes five crucial steps: analysis, design, development,
implementation, and evaluation. The main advantage of this structure is the sequence of steps
and flexibility in the implementation of the project. In particular, ADDIE involves evaluation
after each of the major stages of project execution, which allows you to adjust its development
depending on the emerging conditions.
The analysis stage includes goal-setting and the resources available for the project. This step
also identifies the problem the business has and offers a set of potential solutions that could
address it. At the design stage, the specific tools that will be used within the project are
determined, deliverables are developed, as well as project evaluation criteria. Those two steps
have already been completed under this proposal and are described in the project overview
sections, which contain a description of the problem and the relevant IT solution.
The development stage started with the assignment of the analytical team, which will work
with ready-made BI. This step also includes the choice of the warehouse, the analytical
architecture of the system, and the end-user interface, as well as setting data integration tools.
The implementation of the project will include the creation of a fully functional BI structure
for Textile Company A and the completion of the onboarding of the analytical team. The final
stage of the project is evaluation, which requires tests to create informational reports for various
departments.
Project Goals and Objectives
Goal
Supporting objectives
Deliverables enabling the project
objectives
Met/Unmet
1. BI infrastructure features
assessment and planning
1.a. BI implementation
strategy development
1.a.i. Data sources and analytical tools
review
Met
1.a.ii. Setting objectives
Met
1.a.iii. Tools assessment
Met
1.b. Defining key BI
characteristics
1.b.i. BI team assignment
Met
1.b.ii. Define KPIs
Met
2. BI infrastructure design and
implementation
2.a. Setting data integration
tools
2.a.i. Setting data warehouse
Met
2.a.ii. Setting data integration tools or
ETL (Extract, Transform, Load)
Met
Goal
Supporting objectives
Deliverables enabling the project
objectives
Met/Unmet
2.b. Choosing an approach for
architecture design
2.b.i. Data warehouse connection
Met
2.b.ii. Setting online analytical
processing (OLAP)
Met
2.b.iii. Setting data marts
Met
2.c. The end-user interface
implementation
2.c.i. Setting ad hoc reporting systems
Met
2.c.ii. Conduct end-user training
Met
Goal 1
BI infrastructure features assessment and planning. At this stage, it is necessary to evaluate the
existing resources, infrastructure, goals, and responsible persons, as well as a potential vendor
for the implementation of BI. This goal has been met when all key aspects of the BI solution
are selected and clearly defined based on available data and based on the objectives of the
organization. This goal included two objectives:
Objective a
BI implementation strategy development. This objective includes activities to evaluate the
company’s current resources and infrastructures and appropriate changes that have to be made
in order to achieve the company’s goals. The objective was considered successful when all the
decisions regarding BI infrastructure design were made, including the choice of relevant and
available tools.
Objective b
Defining key BI characteristics. This objective allows the project to scale the required BI
structure and exclude features that the company does not need. At this stage, it is important to
assess the competencies available to the organization, as well as the KPIs that are key to
monitoring. The objective was considered successful when the complete diagram of the BI
infrastructure was created.
Goal 2
BI infrastructure design and implementation. This goal is key within the framework of this
project, as it involves the creation of a functioning BI infrastructure as a result. This objective
is supported by three objectives, which include setting data integration tools, BI architecture
design, and end-user interface implementation. These objectives describe the key aspects of
the software architecture BI solution and constitute a fully functional structure.
Objective a
Setting data integration tools. For the correct operation of BI, tools for collecting, processing,
and storing data are required, which requires the choice of data warehouse and data integration
tools. At the moment, cloud data storages are the most common, which is especially important
for SMEs. The objective was considered successful when the data integration tool was installed
and connected to the company’s databases.
Objective b
Choosing an approach for architecture design. At this stage, it is necessary to decide, based on
the needs and resources of the company, which BI architecture is the most relevant. In this
situation, the company plans to expand, so it would be logical to use a hybrid setup (warehouse
+ OLAP + data marts) to increase the potential duration of the BI solution. The objective was
considered successful when the data warehouse, OLAPs, and data marts were installed and
connected to the ETL tool.
Objective c
The end-user interface implementation. At this stage, it is important to choose a tool that will
allow the company to effectively represent the collected data in the form of analytical reports
for employees. The objective was considered successful when the end-user interface was
installed and connected to the BI structure.
Project Timeline
Milestone or deliverable
Planned
duration
(hours or
days)
Actual
duration
(hours or
days)
Projected
start date
Anticipated
end date
Actual start
date
Anticipated
start date
Project goals and objectives
discussion meeting
1 day
1 day
9.18.2024
9.18.2024
9.18.2024
9.18.2024
Determine existing data
analytics structure
2 days
2 days
9.19.2024
9.20.2024
9.19.2024
9.20.2024
Identify the problems existing
in the analytics structure
2 days
2 days
9.21.2024
9.22.2024
9.21.2024
9.22.2024
Identify adjustments needed
based on company’s goals and
resources
2 days
2 days
9.23.2024
9.24.2024
9.23.2024
9.24.2024
BI team assignment and
training
5 days
5 days
9.24.2024
9.28.2024
9.24.2024
9.28.2024
BI infrastructure design with
identification of appropriate
tools
5 days
5 days
9.29.2024
10.3.2024
9.29.2024
10.3.2024
BI infrastructure setting
10 days
12 days
10.4.2024
10.13.2024
10.04.2024
10.15.2024
BI infrastructure testing
2 days
2 days
10.14.2024
10.15.2024
10.16.2024
10.17.2024
End-users training
5 days
5 days
10.16.2024
10.20.2024
10.17.2024
10.21.2024
The main stages of the project implementation took place according to the schedule, as the
project team worked well-coordinated and according to a well-thought-out preliminary plan.
However, the delay occurred during the BI infrastructure setting phase, which was scheduled
to be completed in 10 days. Instead, the process took 12 days, as there were difficulties
connecting the company’s databases to the ETL tool, which required additional research and
planning. Difficulties arose due to the incompatibility of existing databases with the selected
ETL tool. After reviewing the materials described in the review of the work section, it was
decided to switch to Pentaho ETL solution, which required additional settings. Thus, the project
implementation ended on 10.22.2024 instead of 10.20.2024 with a delay of less than 2 days.
Unanticipated Requirements
An unanticipated requirement that arose during the implementation of the project was the need
to choose a more relevant ETL tool. The problem arose during setting up the BI infrastructure
and testing its operation. Initially, it was planned to use the Apache Spark tool as an ETL since
it is free and easy to implement. However, later it was discovered that this Apache Spark does
not correctly interact with various file formats, including PDF, which are widely used in
company databases. This problem led to incorrect data filtering, which drastically reduced the
effectiveness of the tool. Thus, it was decided to choose a more suitable instrument, which
resulted in the need for additional research and a slight delay. As a result, it was decided to use
Pentaho because this structure is able to work with a variety of files, and it is also widely
considered in the literature and is popular in case studies. The main reason for choosing this
solution was the widespread use of Pentaho among SMEs, which is most relevant for this
project.
Conclusions
The main result of the project implementation is the creation of a fully functional BI
infrastructure in Textile Company A. The potential effect of the project is the ability to
effectively use descriptive and predictive analytics in the organization to optimize current
operations and plan for expansion. Among the immediately observed effects, one can single
out the complete automation of the process of storing, analyzing data, and generating
information reports. Most importantly, the ETL tool integrated into the infrastructure allows
the company to minimize the amount of irrelevant information through the sorting of data in
the databases used. The project is considered successful because, as a result of the
implementation of the BI infrastructure, it became possible to generate multidimensional
informational reports that various departments of the company can use for a more efficient
decision-making process.
Project Deliverables
Figure 1 shows a diagram of the data analytics structure that existed at Textile Company A
before the project was implemented. Previously, the company used manual data analysis and
the generation of information reports, which were handled by employees of the analytical
department, which resulted in low efficiency of data analysis. Figure 2 illustrates the BI
infrastructure diagram that was created at Textile Company A as part of the project
implementation. This scheme describes all the key elements of the system and their interaction.
Based on this illustration, it can be judged that the project made it possible to automate the
process of data analytics in the company in order to increase the efficiency of the decision-
making process based on reports. Finally, Figure 3 contains an example of a multidimensional
report that is generated by the BI infrastructure created within the project. This illustration is
the most important deliverable of the project, as it shows the actual result of its implementation.
Figure 3: The Example of Multidimensional Report Generated by BI Infrastructure
Visualization Tool.
Textile Company A is a small business in North Carolina that is currently struggling due to the
changing global environment. The organization is engaged in the purchase of textiles in
Europe, the Middle and Far East, as well as their distribution in the United States. The
complication of global supply chains leads to a constant increase in the cost of purchasing
textiles, which negatively affects the company’s profits. To overcome this crisis, Textile
Company A plans to expand its operations to South America and Canada. In this regard, the
company needs to upgrade its information infrastructure for better data analytics within the
company.
Prior to the implementation of the project to create a business intelligence (BI) system, the
organization used an outdated approach to storing and analyzing data. It was minimally
automated and did not allow working effectively with a large amount of data. In particular, the
company used several separate data sources (CRM, ERP, individual databases, and external
sources) and generated individual reports based on the data received from each of them. This
approach made holistic data analytics and information-driven decision-making more difficult.
A diagram illustrating the old data analytics system at Textile Company A is presented in Figure
1.
Figure 1: Data Analytics System in Textile Company A before the Project Completion.
As part of the project, it was possible to create a new BI infrastructure in the organization,
which fully automated the company’s interaction with data. It was possible to create a structure
consisting of ETL tools (Extract, Transform, Load), a data warehouse, online analytical
processing (OLAP), and data marts. A detailed scheme of the BI infrastructure created for
Textile Company A is illustrated in Figure 2. As a result, this infrastructure uses all the
company’s data sources for analytics and generates the most informative reports for different
organization reports. Additionally, BI helps in structuring data and filtering out unnecessary
information. Textile Company A can now use the framework for descriptive and predictive
analytics. This allows the company to streamline existing processes and make better decisions
for future expansion planning.
Figure 2: BI Infrastructure for Data Analytics in Textile Company A after the Project
Completion.
The project started with an evaluation of the data analytics structure that existed in the
company. After assessing the available resources and the needs of the organization, decisions
were made regarding suitable storage solutions as well as BI tools. Further, the BI infrastructure
was planned, and the steps for its implementation into the existing information structure of the
company took into account all the necessary changes. After that, BI was implemented and
integrated into the analytics structure of Textile Company A. At the last stage, tests were carried
out to evaluate performance, and employees were trained to work with the end-user interface.
Review of Other Work
The materials considered in the project proposal made it possible to effectively plan the
development and implementation of the BI solution. However, during the implementation of
the project, additional research was required to identify points for improving the existing
design. The paper by Dieni et al. (2021) describes the process of creating a BI infrastructure
for unemployment rate analysis and management for the Central Bureau of Statistics of
Indonesia. Although this article does not belong to the scope of SMEs, it provides a detailed
description of all stages of the process of developing and implementing a BI infrastructure.
Dieni et al. (2021) pay great attention to the development stage of the ETL tool, which, as
emphasized, is the most significant for the correct operation of the BI structure. Based on the
analysis presented in the article, it was decided to use the Pentaho BI solution as the main ETL
tool, as well as a resource for the cloud data warehouse. This approach can both reduce the cost
of creating a BI infrastructure and provide a company with additional options for the future
expansion of an information-analytical system.
During the implementation of the project, the possible use of alternative solutions to create the
most balanced infrastructure was also considered. At the planning stage, it was decided to use
a hybrid approach to the data warehouse: both cloud-based and physical storage. Garani et al.
(2019) analyzed the case of implementing the BI solution in a telecommunication company.
They focus on the design of a data warehouse that would be effective for all the business tasks
required by the organization. As part of the research, Garani et al. (2019) propose the scheme
of the data warehouse containing five dimensions of data sets divided into different categories
(customers, calls, contracts, employment, and product). Most importantly, they explain how
each of those data sets is related to OLAP structures, which are used in the current project as
well. This study showed how to structure a data warehouse information system in the most
efficient way in order for it to work best with data OLAP. Additionally, the article confirmed
the need to use cloud-based storage for interaction with OLAP in conjunction with a physical
warehouse.
On the whole, OLAP and data warehouse design and implementation are the most challenging
parts of the project. At the stage of developing these parts of the infrastructure, many questions
arose regarding how best to organize these elements relative to each other. Al-Aqrabi et al.
(2019) present a case study on the integration of OLAP into the BI infrastructure. Most valuable
is that in this article, the researchers offer a detailed description of the OLAP framework and
describe the elements that interact most effectively with various types of data warehouse
solutions. The use of cloud-based BI data storage when integrating OLAP requires “compliance
of the BI application with web services architectural standards (and the standards defined by
the SaaS or PaaS provider, like Google Apps standards)” (Al-Aqrabi et al., 2019, p. 81). This
information is extremely important for the implementation of the project, as it allows you to
take into account the compatibility of infrastructure elements.
Thus, the literature reviewed in the project proposal helped in the analysis and evaluation of
the existing analytical structure of the company, as well as in the selection and design of a
relevant BI system. However, during the implementation of the project, it was necessary to
optimize the design of the infrastructure in the most efficient way so that it meets the needs of
the company and uses a limited amount of resources. The materials considered during the
implementation and described in this section made it possible to take into account the subtleties
of the design of BI infrastructures that were not obvious during preliminary planning. The study
of these sources will help create the most effective structure and avoid possible difficulties with
its operation.
Changes to the Project Environment
Before the implementation of the project, the analytical infrastructure of Textile Company A
was extremely outdated and required an upgrade in accordance with modern technical
standards. The company used manual data analysis of the companies listed in the databases,
which was carried out by the analytical department. After that, based on the analytics, reports
were compiled for the company as a whole without taking into account the needs of individual
departments. This structure was slow and inflexible and could also contain a large number of
errors and irrelevant information. This situation made it impossible to effectively optimize the
company’s operations, as well as plan for expansion.
The BI infrastructure implementation project has significantly changed the company’s
decision-making process based on the received analytical data. Most importantly, data analytics
has become as automated as possible, which has increased the efficiency of using the
information in the company at times. In particular, the ETL tool made it possible to filter out
irrelevant data, which minimized the number of errors. The inclusion of March data in the BI
infrastructure has allowed departments to receive personalized analytical reports based on their
needs. Additionally, the use of OPLA cubes has made the structure extremely stable in the event
of a future increase in the number of databases and an increase in the number of queries.
Overall, the implementation of the BI infrastructure has allowed Textile Company A to use
descriptive analytics to optimize its operations and predictive analytics to plan the company’s
expansion. It is also important that employees of various departments have access to
multidimensional information for better decision-making. Data analytics in the company has
become efficient, eliminating errors and providing the most relevant and important
information. This system can become the basis for the future development of the company and
a significant increase in its performance.
Methodology
ADDIE methodology model was chosen for the project execution as it presents the most
appropriate approach. This model includes five crucial steps: analysis, design, development,
implementation, and evaluation. The main advantage of this structure is the sequence of steps
and flexibility in the implementation of the project. In particular, ADDIE involves evaluation
after each of the major stages of project execution, which allows you to adjust its development
depending on the emerging conditions.
The analysis stage includes goal-setting and the resources available for the project. This step
also identifies the problem the business has and offers a set of potential solutions that could
address it. At the design stage, the specific tools that will be used within the project are
determined, deliverables are developed, as well as project evaluation criteria. Those two steps
have already been completed under this proposal and are described in the project overview
sections, which contain a description of the problem and the relevant IT solution.
The development stage started with the assignment of the analytical team, which will work
with ready-made BI. This step also includes the choice of the warehouse, the analytical
architecture of the system, and the end-user interface, as well as setting data integration tools.
The implementation of the project will include the creation of a fully functional BI structure
for Textile Company A and the completion of the onboarding of the analytical team. The final
stage of the project is evaluation, which requires tests to create informational reports for various
departments.
Project Goals and Objectives
Goal
Supporting objectives
Deliverables enabling the project
objectives
Met/Unmet
1. BI infrastructure features
assessment and planning
1.a. BI implementation
strategy development
1.a.i. Data sources and analytical tools
review
Met
1.a.ii. Setting objectives
Met
1.a.iii. Tools assessment
Met
1.b. Defining key BI
characteristics
1.b.i. BI team assignment
Met
1.b.ii. Define KPIs
Met
2. BI infrastructure design and
implementation
2.a. Setting data integration
tools
2.a.i. Setting data warehouse
Met
2.a.ii. Setting data integration tools or
ETL (Extract, Transform, Load)
Met
Goal
Supporting objectives
Deliverables enabling the project
objectives
Met/Unmet
2.b. Choosing an approach for
architecture design
2.b.i. Data warehouse connection
Met
2.b.ii. Setting online analytical
processing (OLAP)
Met
2.b.iii. Setting data marts
Met
2.c. The end-user interface
implementation
2.c.i. Setting ad hoc reporting systems
Met
2.c.ii. Conduct end-user training
Met
Goal 1
BI infrastructure features assessment and planning. At this stage, it is necessary to evaluate the
existing resources, infrastructure, goals, and responsible persons, as well as a potential vendor
for the implementation of BI. This goal has been met when all key aspects of the BI solution
are selected and clearly defined based on available data and based on the objectives of the
organization. This goal included two objectives:
Objective a
BI implementation strategy development. This objective includes activities to evaluate the
company’s current resources and infrastructures and appropriate changes that have to be made
in order to achieve the company’s goals. The objective was considered successful when all the
decisions regarding BI infrastructure design were made, including the choice of relevant and
available tools.
Objective b
Defining key BI characteristics. This objective allows the project to scale the required BI
structure and exclude features that the company does not need. At this stage, it is important to
assess the competencies available to the organization, as well as the KPIs that are key to
monitoring. The objective was considered successful when the complete diagram of the BI
infrastructure was created.
Goal 2
BI infrastructure design and implementation. This goal is key within the framework of this
project, as it involves the creation of a functioning BI infrastructure as a result. This objective
is supported by three objectives, which include setting data integration tools, BI architecture
design, and end-user interface implementation. These objectives describe the key aspects of
the software architecture BI solution and constitute a fully functional structure.
Objective a
Setting data integration tools. For the correct operation of BI, tools for collecting, processing,
and storing data are required, which requires the choice of data warehouse and data integration
tools. At the moment, cloud data storages are the most common, which is especially important
for SMEs. The objective was considered successful when the data integration tool was installed
and connected to the company’s databases.
Objective b
Choosing an approach for architecture design. At this stage, it is necessary to decide, based on
the needs and resources of the company, which BI architecture is the most relevant. In this
situation, the company plans to expand, so it would be logical to use a hybrid setup (warehouse
+ OLAP + data marts) to increase the potential duration of the BI solution. The objective was
considered successful when the data warehouse, OLAPs, and data marts were installed and
connected to the ETL tool.
Objective c
The end-user interface implementation. At this stage, it is important to choose a tool that will
allow the company to effectively represent the collected data in the form of analytical reports
for employees. The objective was considered successful when the end-user interface was
installed and connected to the BI structure.
Project Timeline
Milestone or deliverable
Planned
duration
(hours or
days)
Actual
duration
(hours or
days)
Projected
start date
Anticipated
end date
Actual start
date
Anticipated
start date
Project goals and objectives
discussion meeting
1 day
1 day
9.18.2024
9.18.2024
9.18.2024
9.18.2024
Determine existing data
analytics structure
2 days
2 days
9.19.2024
9.20.2024
9.19.2024
9.20.2024
Identify the problems existing
in the analytics structure
2 days
2 days
9.21.2024
9.22.2024
9.21.2024
9.22.2024
Identify adjustments needed
based on company’s goals and
resources
2 days
2 days
9.23.2024
9.24.2024
9.23.2024
9.24.2024
BI team assignment and
training
5 days
5 days
9.24.2024
9.28.2024
9.24.2024
9.28.2024
BI infrastructure design with
identification of appropriate
tools
5 days
5 days
9.29.2024
10.3.2024
9.29.2024
10.3.2024
BI infrastructure setting
10 days
12 days
10.4.2024
10.13.2024
10.04.2024
10.15.2024
BI infrastructure testing
2 days
2 days
10.14.2024
10.15.2024
10.16.2024
10.17.2024
End-users training
5 days
5 days
10.16.2024
10.20.2024
10.17.2024
10.21.2024
The main stages of the project implementation took place according to the schedule, as the
project team worked well-coordinated and according to a well-thought-out preliminary plan.
However, the delay occurred during the BI infrastructure setting phase, which was scheduled
to be completed in 10 days. Instead, the process took 12 days, as there were difficulties
connecting the company’s databases to the ETL tool, which required additional research and
planning. Difficulties arose due to the incompatibility of existing databases with the selected
ETL tool. After reviewing the materials described in the review of the work section, it was
decided to switch to Pentaho ETL solution, which required additional settings. Thus, the project
implementation ended on 10.22.2024 instead of 10.20.2024 with a delay of less than 2 days.
Unanticipated Requirements
An unanticipated requirement that arose during the implementation of the project was the need
to choose a more relevant ETL tool. The problem arose during setting up the BI infrastructure
and testing its operation. Initially, it was planned to use the Apache Spark tool as an ETL since
it is free and easy to implement. However, later it was discovered that this Apache Spark does
not correctly interact with various file formats, including PDF, which are widely used in
company databases. This problem led to incorrect data filtering, which drastically reduced the
effectiveness of the tool. Thus, it was decided to choose a more suitable instrument, which
resulted in the need for additional research and a slight delay. As a result, it was decided to use
Pentaho because this structure is able to work with a variety of files, and it is also widely
considered in the literature and is popular in case studies. The main reason for choosing this
solution was the widespread use of Pentaho among SMEs, which is most relevant for this
project.
Conclusions
The main result of the project implementation is the creation of a fully functional BI
infrastructure in Textile Company A. The potential effect of the project is the ability to
effectively use descriptive and predictive analytics in the organization to optimize current
operations and plan for expansion. Among the immediately observed effects, one can single
out the complete automation of the process of storing, analyzing data, and generating
information reports. Most importantly, the ETL tool integrated into the infrastructure allows
the company to minimize the amount of irrelevant information through the sorting of data in
the databases used. The project is considered successful because, as a result of the
implementation of the BI infrastructure, it became possible to generate multidimensional
informational reports that various departments of the company can use for a more efficient
decision-making process.
Project Deliverables
Figure 1 shows a diagram of the data analytics structure that existed at Textile Company A
before the project was implemented. Previously, the company used manual data analysis and
the generation of information reports, which were handled by employees of the analytical
department, which resulted in low efficiency of data analysis. Figure 2 illustrates the BI
infrastructure diagram that was created at Textile Company A as part of the project
implementation. This scheme describes all the key elements of the system and their interaction.
Based on this illustration, it can be judged that the project made it possible to automate the
process of data analytics in the company in order to increase the efficiency of the decision-
making process based on reports. Finally, Figure 3 contains an example of a multidimensional
report that is generated by the BI infrastructure created within the project. This illustration is
the most important deliverable of the project, as it shows the actual result of its implementation.
Figure 3: The Example of Multidimensional Report Generated by BI Infrastructure
Visualization Tool.
Textile Company A is a small business in North Carolina that is currently struggling due to the
changing global environment. The organization is engaged in the purchase of textiles in
Europe, the Middle and Far East, as well as their distribution in the United States. The
complication of global supply chains leads to a constant increase in the cost of purchasing
textiles, which negatively affects the company’s profits. To overcome this crisis, Textile
Company A plans to expand its operations to South America and Canada. In this regard, the
company needs to upgrade its information infrastructure for better data analytics within the
company.
Prior to the implementation of the project to create a business intelligence (BI) system, the
organization used an outdated approach to storing and analyzing data. It was minimally
automated and did not allow working effectively with a large amount of data. In particular, the
company used several separate data sources (CRM, ERP, individual databases, and external
sources) and generated individual reports based on the data received from each of them. This
approach made holistic data analytics and information-driven decision-making more difficult.
A diagram illustrating the old data analytics system at Textile Company A is presented in Figure
1.
Figure 1: Data Analytics System in Textile Company A before the Project Completion.
As part of the project, it was possible to create a new BI infrastructure in the organization,
which fully automated the company’s interaction with data. It was possible to create a structure
consisting of ETL tools (Extract, Transform, Load), a data warehouse, online analytical
processing (OLAP), and data marts. A detailed scheme of the BI infrastructure created for
Textile Company A is illustrated in Figure 2. As a result, this infrastructure uses all the
company’s data sources for analytics and generates the most informative reports for different
organization reports. Additionally, BI helps in structuring data and filtering out unnecessary
information. Textile Company A can now use the framework for descriptive and predictive
analytics. This allows the company to streamline existing processes and make better decisions
for future expansion planning.
Figure 2: BI Infrastructure for Data Analytics in Textile Company A after the Project
Completion.
The project started with an evaluation of the data analytics structure that existed in the
company. After assessing the available resources and the needs of the organization, decisions
were made regarding suitable storage solutions as well as BI tools. Further, the BI infrastructure
was planned, and the steps for its implementation into the existing information structure of the
company took into account all the necessary changes. After that, BI was implemented and
integrated into the analytics structure of Textile Company A. At the last stage, tests were carried
out to evaluate performance, and employees were trained to work with the end-user interface.
Review of Other Work
The materials considered in the project proposal made it possible to effectively plan the
development and implementation of the BI solution. However, during the implementation of
the project, additional research was required to identify points for improving the existing
design. The paper by Dieni et al. (2021) describes the process of creating a BI infrastructure
for unemployment rate analysis and management for the Central Bureau of Statistics of
Indonesia. Although this article does not belong to the scope of SMEs, it provides a detailed
description of all stages of the process of developing and implementing a BI infrastructure.
Dieni et al. (2021) pay great attention to the development stage of the ETL tool, which, as
emphasized, is the most significant for the correct operation of the BI structure. Based on the
analysis presented in the article, it was decided to use the Pentaho BI solution as the main ETL
tool, as well as a resource for the cloud data warehouse. This approach can both reduce the cost
of creating a BI infrastructure and provide a company with additional options for the future
expansion of an information-analytical system.
During the implementation of the project, the possible use of alternative solutions to create the
most balanced infrastructure was also considered. At the planning stage, it was decided to use
a hybrid approach to the data warehouse: both cloud-based and physical storage. Garani et al.
(2019) analyzed the case of implementing the BI solution in a telecommunication company.
They focus on the design of a data warehouse that would be effective for all the business tasks
required by the organization. As part of the research, Garani et al. (2019) propose the scheme
of the data warehouse containing five dimensions of data sets divided into different categories
(customers, calls, contracts, employment, and product). Most importantly, they explain how
each of those data sets is related to OLAP structures, which are used in the current project as
well. This study showed how to structure a data warehouse information system in the most
efficient way in order for it to work best with data OLAP. Additionally, the article confirmed
the need to use cloud-based storage for interaction with OLAP in conjunction with a physical
warehouse.
On the whole, OLAP and data warehouse design and implementation are the most challenging
parts of the project. At the stage of developing these parts of the infrastructure, many questions
arose regarding how best to organize these elements relative to each other. Al-Aqrabi et al.
(2019) present a case study on the integration of OLAP into the BI infrastructure. Most valuable
is that in this article, the researchers offer a detailed description of the OLAP framework and
describe the elements that interact most effectively with various types of data warehouse
solutions. The use of cloud-based BI data storage when integrating OLAP requires “compliance
of the BI application with web services architectural standards (and the standards defined by
the SaaS or PaaS provider, like Google Apps standards)” (Al-Aqrabi et al., 2019, p. 81). This
information is extremely important for the implementation of the project, as it allows you to
take into account the compatibility of infrastructure elements.
Thus, the literature reviewed in the project proposal helped in the analysis and evaluation of
the existing analytical structure of the company, as well as in the selection and design of a
relevant BI system. However, during the implementation of the project, it was necessary to
optimize the design of the infrastructure in the most efficient way so that it meets the needs of
the company and uses a limited amount of resources. The materials considered during the
implementation and described in this section made it possible to take into account the subtleties
of the design of BI infrastructures that were not obvious during preliminary planning. The study
of these sources will help create the most effective structure and avoid possible difficulties with
its operation.
Changes to the Project Environment
Before the implementation of the project, the analytical infrastructure of Textile Company A
was extremely outdated and required an upgrade in accordance with modern technical
standards. The company used manual data analysis of the companies listed in the databases,
which was carried out by the analytical department. After that, based on the analytics, reports
were compiled for the company as a whole without taking into account the needs of individual
departments. This structure was slow and inflexible and could also contain a large number of
errors and irrelevant information. This situation made it impossible to effectively optimize the
company’s operations, as well as plan for expansion.
The BI infrastructure implementation project has significantly changed the company’s
decision-making process based on the received analytical data. Most importantly, data analytics
has become as automated as possible, which has increased the efficiency of using the
information in the company at times. In particular, the ETL tool made it possible to filter out
irrelevant data, which minimized the number of errors. The inclusion of March data in the BI
infrastructure has allowed departments to receive personalized analytical reports based on their
needs. Additionally, the use of OPLA cubes has made the structure extremely stable in the event
of a future increase in the number of databases and an increase in the number of queries.
Overall, the implementation of the BI infrastructure has allowed Textile Company A to use
descriptive analytics to optimize its operations and predictive analytics to plan the company’s
expansion. It is also important that employees of various departments have access to
multidimensional information for better decision-making. Data analytics in the company has
become efficient, eliminating errors and providing the most relevant and important
information. This system can become the basis for the future development of the company and
a significant increase in its performance.
Methodology
ADDIE methodology model was chosen for the project execution as it presents the most
appropriate approach. This model includes five crucial steps: analysis, design, development,
implementation, and evaluation. The main advantage of this structure is the sequence of steps
and flexibility in the implementation of the project. In particular, ADDIE involves evaluation
after each of the major stages of project execution, which allows you to adjust its development
depending on the emerging conditions.
The analysis stage includes goal-setting and the resources available for the project. This step
also identifies the problem the business has and offers a set of potential solutions that could
address it. At the design stage, the specific tools that will be used within the project are
determined, deliverables are developed, as well as project evaluation criteria. Those two steps
have already been completed under this proposal and are described in the project overview
sections, which contain a description of the problem and the relevant IT solution.
The development stage started with the assignment of the analytical team, which will work
with ready-made BI. This step also includes the choice of the warehouse, the analytical
architecture of the system, and the end-user interface, as well as setting data integration tools.
The implementation of the project will include the creation of a fully functional BI structure
for Textile Company A and the completion of the onboarding of the analytical team. The final
stage of the project is evaluation, which requires tests to create informational reports for various
departments.
Project Goals and Objectives
Goal
Supporting objectives
Deliverables enabling the project
objectives
Met/Unmet
1. BI infrastructure features
assessment and planning
1.a. BI implementation
strategy development
1.a.i. Data sources and analytical tools
review
Met
1.a.ii. Setting objectives
Met
1.a.iii. Tools assessment
Met
1.b. Defining key BI
characteristics
1.b.i. BI team assignment
Met
1.b.ii. Define KPIs
Met
2. BI infrastructure design and
implementation
2.a. Setting data integration
tools
2.a.i. Setting data warehouse
Met
2.a.ii. Setting data integration tools or
ETL (Extract, Transform, Load)
Met
Goal
Supporting objectives
Deliverables enabling the project
objectives
Met/Unmet
2.b. Choosing an approach for
architecture design
2.b.i. Data warehouse connection
Met
2.b.ii. Setting online analytical
processing (OLAP)
Met
2.b.iii. Setting data marts
Met
2.c. The end-user interface
implementation
2.c.i. Setting ad hoc reporting systems
Met
2.c.ii. Conduct end-user training
Met
Goal 1
BI infrastructure features assessment and planning. At this stage, it is necessary to evaluate the
existing resources, infrastructure, goals, and responsible persons, as well as a potential vendor
for the implementation of BI. This goal has been met when all key aspects of the BI solution
are selected and clearly defined based on available data and based on the objectives of the
organization. This goal included two objectives:
Objective a
BI implementation strategy development. This objective includes activities to evaluate the
company’s current resources and infrastructures and appropriate changes that have to be made
in order to achieve the company’s goals. The objective was considered successful when all the
decisions regarding BI infrastructure design were made, including the choice of relevant and
available tools.
Objective b
Defining key BI characteristics. This objective allows the project to scale the required BI
structure and exclude features that the company does not need. At this stage, it is important to
assess the competencies available to the organization, as well as the KPIs that are key to
monitoring. The objective was considered successful when the complete diagram of the BI
infrastructure was created.
Goal 2
BI infrastructure design and implementation. This goal is key within the framework of this
project, as it involves the creation of a functioning BI infrastructure as a result. This objective
is supported by three objectives, which include setting data integration tools, BI architecture
design, and end-user interface implementation. These objectives describe the key aspects of
the software architecture BI solution and constitute a fully functional structure.
Objective a
Setting data integration tools. For the correct operation of BI, tools for collecting, processing,
and storing data are required, which requires the choice of data warehouse and data integration
tools. At the moment, cloud data storages are the most common, which is especially important
for SMEs. The objective was considered successful when the data integration tool was installed
and connected to the company’s databases.
Objective b
Choosing an approach for architecture design. At this stage, it is necessary to decide, based on
the needs and resources of the company, which BI architecture is the most relevant. In this
situation, the company plans to expand, so it would be logical to use a hybrid setup (warehouse
+ OLAP + data marts) to increase the potential duration of the BI solution. The objective was
considered successful when the data warehouse, OLAPs, and data marts were installed and
connected to the ETL tool.
Objective c
The end-user interface implementation. At this stage, it is important to choose a tool that will
allow the company to effectively represent the collected data in the form of analytical reports
for employees. The objective was considered successful when the end-user interface was
installed and connected to the BI structure.
Project Timeline
Milestone or deliverable
Planned
duration
(hours or
days)
Actual
duration
(hours or
days)
Projected
start date
Anticipated
end date
Actual start
date
Anticipated
start date
Project goals and objectives
discussion meeting
1 day
1 day
9.18.2024
9.18.2024
9.18.2024
9.18.2024
Determine existing data
analytics structure
2 days
2 days
9.19.2024
9.20.2024
9.19.2024
9.20.2024
Identify the problems existing
in the analytics structure
2 days
2 days
9.21.2024
9.22.2024
9.21.2024
9.22.2024
Identify adjustments needed
based on company’s goals and
resources
2 days
2 days
9.23.2024
9.24.2024
9.23.2024
9.24.2024
BI team assignment and
training
5 days
5 days
9.24.2024
9.28.2024
9.24.2024
9.28.2024
BI infrastructure design with
identification of appropriate
tools
5 days
5 days
9.29.2024
10.3.2024
9.29.2024
10.3.2024
BI infrastructure setting
10 days
12 days
10.4.2024
10.13.2024
10.04.2024
10.15.2024
BI infrastructure testing
2 days
2 days
10.14.2024
10.15.2024
10.16.2024
10.17.2024
End-users training
5 days
5 days
10.16.2024
10.20.2024
10.17.2024
10.21.2024
The main stages of the project implementation took place according to the schedule, as the
project team worked well-coordinated and according to a well-thought-out preliminary plan.
However, the delay occurred during the BI infrastructure setting phase, which was scheduled
to be completed in 10 days. Instead, the process took 12 days, as there were difficulties
connecting the company’s databases to the ETL tool, which required additional research and
planning. Difficulties arose due to the incompatibility of existing databases with the selected
ETL tool. After reviewing the materials described in the review of the work section, it was
decided to switch to Pentaho ETL solution, which required additional settings. Thus, the project
implementation ended on 10.22.2024 instead of 10.20.2024 with a delay of less than 2 days.
Unanticipated Requirements
An unanticipated requirement that arose during the implementation of the project was the need
to choose a more relevant ETL tool. The problem arose during setting up the BI infrastructure
and testing its operation. Initially, it was planned to use the Apache Spark tool as an ETL since
it is free and easy to implement. However, later it was discovered that this Apache Spark does
not correctly interact with various file formats, including PDF, which are widely used in
company databases. This problem led to incorrect data filtering, which drastically reduced the
effectiveness of the tool. Thus, it was decided to choose a more suitable instrument, which
resulted in the need for additional research and a slight delay. As a result, it was decided to use
Pentaho because this structure is able to work with a variety of files, and it is also widely
considered in the literature and is popular in case studies. The main reason for choosing this
solution was the widespread use of Pentaho among SMEs, which is most relevant for this
project.
Conclusions
The main result of the project implementation is the creation of a fully functional BI
infrastructure in Textile Company A. The potential effect of the project is the ability to
effectively use descriptive and predictive analytics in the organization to optimize current
operations and plan for expansion. Among the immediately observed effects, one can single
out the complete automation of the process of storing, analyzing data, and generating
information reports. Most importantly, the ETL tool integrated into the infrastructure allows
the company to minimize the amount of irrelevant information through the sorting of data in
the databases used. The project is considered successful because, as a result of the
implementation of the BI infrastructure, it became possible to generate multidimensional
informational reports that various departments of the company can use for a more efficient
decision-making process.
Project Deliverables
Figure 1 shows a diagram of the data analytics structure that existed at Textile Company A
before the project was implemented. Previously, the company used manual data analysis and
the generation of information reports, which were handled by employees of the analytical
department, which resulted in low efficiency of data analysis. Figure 2 illustrates the BI
infrastructure diagram that was created at Textile Company A as part of the project
implementation. This scheme describes all the key elements of the system and their interaction.
Based on this illustration, it can be judged that the project made it possible to automate the
process of data analytics in the company in order to increase the efficiency of the decision-
making process based on reports. Finally, Figure 3 contains an example of a multidimensional
report that is generated by the BI infrastructure created within the project. This illustration is
the most important deliverable of the project, as it shows the actual result of its implementation.
Figure 3: The Example of Multidimensional Report Generated by BI Infrastructure
Visualization Tool.
Textile Company A is a small business in North Carolina that is currently struggling due to the
changing global environment. The organization is engaged in the purchase of textiles in
Europe, the Middle and Far East, as well as their distribution in the United States. The
complication of global supply chains leads to a constant increase in the cost of purchasing
textiles, which negatively affects the company’s profits. To overcome this crisis, Textile
Company A plans to expand its operations to South America and Canada. In this regard, the
company needs to upgrade its information infrastructure for better data analytics within the
company.
Prior to the implementation of the project to create a business intelligence (BI) system, the
organization used an outdated approach to storing and analyzing data. It was minimally
automated and did not allow working effectively with a large amount of data. In particular, the
company used several separate data sources (CRM, ERP, individual databases, and external
sources) and generated individual reports based on the data received from each of them. This
approach made holistic data analytics and information-driven decision-making more difficult.
A diagram illustrating the old data analytics system at Textile Company A is presented in Figure
1.
Figure 1: Data Analytics System in Textile Company A before the Project Completion.
As part of the project, it was possible to create a new BI infrastructure in the organization,
which fully automated the company’s interaction with data. It was possible to create a structure
consisting of ETL tools (Extract, Transform, Load), a data warehouse, online analytical
processing (OLAP), and data marts. A detailed scheme of the BI infrastructure created for
Textile Company A is illustrated in Figure 2. As a result, this infrastructure uses all the
company’s data sources for analytics and generates the most informative reports for different
organization reports. Additionally, BI helps in structuring data and filtering out unnecessary
information. Textile Company A can now use the framework for descriptive and predictive
analytics. This allows the company to streamline existing processes and make better decisions
for future expansion planning.
Figure 2: BI Infrastructure for Data Analytics in Textile Company A after the Project
Completion.
The project started with an evaluation of the data analytics structure that existed in the
company. After assessing the available resources and the needs of the organization, decisions
were made regarding suitable storage solutions as well as BI tools. Further, the BI infrastructure
was planned, and the steps for its implementation into the existing information structure of the
company took into account all the necessary changes. After that, BI was implemented and
integrated into the analytics structure of Textile Company A. At the last stage, tests were carried
out to evaluate performance, and employees were trained to work with the end-user interface.
Review of Other Work
The materials considered in the project proposal made it possible to effectively plan the
development and implementation of the BI solution. However, during the implementation of
the project, additional research was required to identify points for improving the existing
design. The paper by Dieni et al. (2021) describes the process of creating a BI infrastructure
for unemployment rate analysis and management for the Central Bureau of Statistics of
Indonesia. Although this article does not belong to the scope of SMEs, it provides a detailed
description of all stages of the process of developing and implementing a BI infrastructure.
Dieni et al. (2021) pay great attention to the development stage of the ETL tool, which, as
emphasized, is the most significant for the correct operation of the BI structure. Based on the
analysis presented in the article, it was decided to use the Pentaho BI solution as the main ETL
tool, as well as a resource for the cloud data warehouse. This approach can both reduce the cost
of creating a BI infrastructure and provide a company with additional options for the future
expansion of an information-analytical system.
During the implementation of the project, the possible use of alternative solutions to create the
most balanced infrastructure was also considered. At the planning stage, it was decided to use
a hybrid approach to the data warehouse: both cloud-based and physical storage. Garani et al.
(2019) analyzed the case of implementing the BI solution in a telecommunication company.
They focus on the design of a data warehouse that would be effective for all the business tasks
required by the organization. As part of the research, Garani et al. (2019) propose the scheme
of the data warehouse containing five dimensions of data sets divided into different categories
(customers, calls, contracts, employment, and product). Most importantly, they explain how
each of those data sets is related to OLAP structures, which are used in the current project as
well. This study showed how to structure a data warehouse information system in the most
efficient way in order for it to work best with data OLAP. Additionally, the article confirmed
the need to use cloud-based storage for interaction with OLAP in conjunction with a physical
warehouse.
On the whole, OLAP and data warehouse design and implementation are the most challenging
parts of the project. At the stage of developing these parts of the infrastructure, many questions
arose regarding how best to organize these elements relative to each other. Al-Aqrabi et al.
(2019) present a case study on the integration of OLAP into the BI infrastructure. Most valuable
is that in this article, the researchers offer a detailed description of the OLAP framework and
describe the elements that interact most effectively with various types of data warehouse
solutions. The use of cloud-based BI data storage when integrating OLAP requires “compliance
of the BI application with web services architectural standards (and the standards defined by
the SaaS or PaaS provider, like Google Apps standards)” (Al-Aqrabi et al., 2019, p. 81). This
information is extremely important for the implementation of the project, as it allows you to
take into account the compatibility of infrastructure elements.
Thus, the literature reviewed in the project proposal helped in the analysis and evaluation of
the existing analytical structure of the company, as well as in the selection and design of a
relevant BI system. However, during the implementation of the project, it was necessary to
optimize the design of the infrastructure in the most efficient way so that it meets the needs of
the company and uses a limited amount of resources. The materials considered during the
implementation and described in this section made it possible to take into account the subtleties
of the design of BI infrastructures that were not obvious during preliminary planning. The study
of these sources will help create the most effective structure and avoid possible difficulties with
its operation.
Changes to the Project Environment
Before the implementation of the project, the analytical infrastructure of Textile Company A
was extremely outdated and required an upgrade in accordance with modern technical
standards. The company used manual data analysis of the companies listed in the databases,
which was carried out by the analytical department. After that, based on the analytics, reports
were compiled for the company as a whole without taking into account the needs of individual
departments. This structure was slow and inflexible and could also contain a large number of
errors and irrelevant information. This situation made it impossible to effectively optimize the
company’s operations, as well as plan for expansion.
The BI infrastructure implementation project has significantly changed the company’s
decision-making process based on the received analytical data. Most importantly, data analytics
has become as automated as possible, which has increased the efficiency of using the
information in the company at times. In particular, the ETL tool made it possible to filter out
irrelevant data, which minimized the number of errors. The inclusion of March data in the BI
infrastructure has allowed departments to receive personalized analytical reports based on their
needs. Additionally, the use of OPLA cubes has made the structure extremely stable in the event
of a future increase in the number of databases and an increase in the number of queries.
Overall, the implementation of the BI infrastructure has allowed Textile Company A to use
descriptive analytics to optimize its operations and predictive analytics to plan the company’s
expansion. It is also important that employees of various departments have access to
multidimensional information for better decision-making. Data analytics in the company has
become efficient, eliminating errors and providing the most relevant and important
information. This system can become the basis for the future development of the company and
a significant increase in its performance.
Methodology
ADDIE methodology model was chosen for the project execution as it presents the most
appropriate approach. This model includes five crucial steps: analysis, design, development,
implementation, and evaluation. The main advantage of this structure is the sequence of steps
and flexibility in the implementation of the project. In particular, ADDIE involves evaluation
after each of the major stages of project execution, which allows you to adjust its development
depending on the emerging conditions.
The analysis stage includes goal-setting and the resources available for the project. This step
also identifies the problem the business has and offers a set of potential solutions that could
address it. At the design stage, the specific tools that will be used within the project are
determined, deliverables are developed, as well as project evaluation criteria. Those two steps
have already been completed under this proposal and are described in the project overview
sections, which contain a description of the problem and the relevant IT solution.
The development stage started with the assignment of the analytical team, which will work
with ready-made BI. This step also includes the choice of the warehouse, the analytical
architecture of the system, and the end-user interface, as well as setting data integration tools.
The implementation of the project will include the creation of a fully functional BI structure
for Textile Company A and the completion of the onboarding of the analytical team. The final
stage of the project is evaluation, which requires tests to create informational reports for various
departments.
Project Goals and Objectives
Goal
Supporting objectives
Deliverables enabling the project
objectives
Met/Unmet
1. BI infrastructure features
assessment and planning
1.a. BI implementation
strategy development
1.a.i. Data sources and analytical tools
review
Met
1.a.ii. Setting objectives
Met
1.a.iii. Tools assessment
Met
1.b. Defining key BI
characteristics
1.b.i. BI team assignment
Met
1.b.ii. Define KPIs
Met
2. BI infrastructure design and
implementation
2.a. Setting data integration
tools
2.a.i. Setting data warehouse
Met
2.a.ii. Setting data integration tools or
ETL (Extract, Transform, Load)
Met
Goal
Supporting objectives
Deliverables enabling the project
objectives
Met/Unmet
2.b. Choosing an approach for
architecture design
2.b.i. Data warehouse connection
Met
2.b.ii. Setting online analytical
processing (OLAP)
Met
2.b.iii. Setting data marts
Met
2.c. The end-user interface
implementation
2.c.i. Setting ad hoc reporting systems
Met
2.c.ii. Conduct end-user training
Met
Goal 1
BI infrastructure features assessment and planning. At this stage, it is necessary to evaluate the
existing resources, infrastructure, goals, and responsible persons, as well as a potential vendor
for the implementation of BI. This goal has been met when all key aspects of the BI solution
are selected and clearly defined based on available data and based on the objectives of the
organization. This goal included two objectives:
Objective a
BI implementation strategy development. This objective includes activities to evaluate the
company’s current resources and infrastructures and appropriate changes that have to be made
in order to achieve the company’s goals. The objective was considered successful when all the
decisions regarding BI infrastructure design were made, including the choice of relevant and
available tools.
Objective b
Defining key BI characteristics. This objective allows the project to scale the required BI
structure and exclude features that the company does not need. At this stage, it is important to
assess the competencies available to the organization, as well as the KPIs that are key to
monitoring. The objective was considered successful when the complete diagram of the BI
infrastructure was created.
Goal 2
BI infrastructure design and implementation. This goal is key within the framework of this
project, as it involves the creation of a functioning BI infrastructure as a result. This objective
is supported by three objectives, which include setting data integration tools, BI architecture
design, and end-user interface implementation. These objectives describe the key aspects of
the software architecture BI solution and constitute a fully functional structure.
Objective a
Setting data integration tools. For the correct operation of BI, tools for collecting, processing,
and storing data are required, which requires the choice of data warehouse and data integration
tools. At the moment, cloud data storages are the most common, which is especially important
for SMEs. The objective was considered successful when the data integration tool was installed
and connected to the company’s databases.
Objective b
Choosing an approach for architecture design. At this stage, it is necessary to decide, based on
the needs and resources of the company, which BI architecture is the most relevant. In this
situation, the company plans to expand, so it would be logical to use a hybrid setup (warehouse
+ OLAP + data marts) to increase the potential duration of the BI solution. The objective was
considered successful when the data warehouse, OLAPs, and data marts were installed and
connected to the ETL tool.
Objective c
The end-user interface implementation. At this stage, it is important to choose a tool that will
allow the company to effectively represent the collected data in the form of analytical reports
for employees. The objective was considered successful when the end-user interface was
installed and connected to the BI structure.
Project Timeline
Milestone or deliverable
Planned
duration
(hours or
days)
Actual
duration
(hours or
days)
Projected
start date
Anticipated
end date
Actual start
date
Anticipated
start date
Project goals and objectives
discussion meeting
1 day
1 day
9.18.2024
9.18.2024
9.18.2024
9.18.2024
Determine existing data
analytics structure
2 days
2 days
9.19.2024
9.20.2024
9.19.2024
9.20.2024
Identify the problems existing
in the analytics structure
2 days
2 days
9.21.2024
9.22.2024
9.21.2024
9.22.2024
Identify adjustments needed
based on company’s goals and
resources
2 days
2 days
9.23.2024
9.24.2024
9.23.2024
9.24.2024
BI team assignment and
training
5 days
5 days
9.24.2024
9.28.2024
9.24.2024
9.28.2024
BI infrastructure design with
identification of appropriate
tools
5 days
5 days
9.29.2024
10.3.2024
9.29.2024
10.3.2024
BI infrastructure setting
10 days
12 days
10.4.2024
10.13.2024
10.04.2024
10.15.2024
BI infrastructure testing
2 days
2 days
10.14.2024
10.15.2024
10.16.2024
10.17.2024
End-users training
5 days
5 days
10.16.2024
10.20.2024
10.17.2024
10.21.2024
The main stages of the project implementation took place according to the schedule, as the
project team worked well-coordinated and according to a well-thought-out preliminary plan.
However, the delay occurred during the BI infrastructure setting phase, which was scheduled
to be completed in 10 days. Instead, the process took 12 days, as there were difficulties
connecting the company’s databases to the ETL tool, which required additional research and
planning. Difficulties arose due to the incompatibility of existing databases with the selected
ETL tool. After reviewing the materials described in the review of the work section, it was
decided to switch to Pentaho ETL solution, which required additional settings. Thus, the project
implementation ended on 10.22.2024 instead of 10.20.2024 with a delay of less than 2 days.
Unanticipated Requirements
An unanticipated requirement that arose during the implementation of the project was the need
to choose a more relevant ETL tool. The problem arose during setting up the BI infrastructure
and testing its operation. Initially, it was planned to use the Apache Spark tool as an ETL since
it is free and easy to implement. However, later it was discovered that this Apache Spark does
not correctly interact with various file formats, including PDF, which are widely used in
company databases. This problem led to incorrect data filtering, which drastically reduced the
effectiveness of the tool. Thus, it was decided to choose a more suitable instrument, which
resulted in the need for additional research and a slight delay. As a result, it was decided to use
Pentaho because this structure is able to work with a variety of files, and it is also widely
considered in the literature and is popular in case studies. The main reason for choosing this
solution was the widespread use of Pentaho among SMEs, which is most relevant for this
project.
Conclusions
The main result of the project implementation is the creation of a fully functional BI
infrastructure in Textile Company A. The potential effect of the project is the ability to
effectively use descriptive and predictive analytics in the organization to optimize current
operations and plan for expansion. Among the immediately observed effects, one can single
out the complete automation of the process of storing, analyzing data, and generating
information reports. Most importantly, the ETL tool integrated into the infrastructure allows
the company to minimize the amount of irrelevant information through the sorting of data in
the databases used. The project is considered successful because, as a result of the
implementation of the BI infrastructure, it became possible to generate multidimensional
informational reports that various departments of the company can use for a more efficient
decision-making process.
Project Deliverables
Figure 1 shows a diagram of the data analytics structure that existed at Textile Company A
before the project was implemented. Previously, the company used manual data analysis and
the generation of information reports, which were handled by employees of the analytical
department, which resulted in low efficiency of data analysis. Figure 2 illustrates the BI
infrastructure diagram that was created at Textile Company A as part of the project
implementation. This scheme describes all the key elements of the system and their interaction.
Based on this illustration, it can be judged that the project made it possible to automate the
process of data analytics in the company in order to increase the efficiency of the decision-
making process based on reports. Finally, Figure 3 contains an example of a multidimensional
report that is generated by the BI infrastructure created within the project. This illustration is
the most important deliverable of the project, as it shows the actual result of its implementation.
Figure 3: The Example of Multidimensional Report Generated by BI Infrastructure
Visualization Tool.
Textile Company A is a small business in North Carolina that is currently struggling due to the
changing global environment. The organization is engaged in the purchase of textiles in
Europe, the Middle and Far East, as well as their distribution in the United States. The
complication of global supply chains leads to a constant increase in the cost of purchasing
textiles, which negatively affects the company’s profits. To overcome this crisis, Textile
Company A plans to expand its operations to South America and Canada. In this regard, the
company needs to upgrade its information infrastructure for better data analytics within the
company.
Prior to the implementation of the project to create a business intelligence (BI) system, the
organization used an outdated approach to storing and analyzing data. It was minimally
automated and did not allow working effectively with a large amount of data. In particular, the
company used several separate data sources (CRM, ERP, individual databases, and external
sources) and generated individual reports based on the data received from each of them. This
approach made holistic data analytics and information-driven decision-making more difficult.
A diagram illustrating the old data analytics system at Textile Company A is presented in Figure
1.
Figure 1: Data Analytics System in Textile Company A before the Project Completion.
As part of the project, it was possible to create a new BI infrastructure in the organization,
which fully automated the company’s interaction with data. It was possible to create a structure
consisting of ETL tools (Extract, Transform, Load), a data warehouse, online analytical
processing (OLAP), and data marts. A detailed scheme of the BI infrastructure created for
Textile Company A is illustrated in Figure 2. As a result, this infrastructure uses all the
company’s data sources for analytics and generates the most informative reports for different
organization reports. Additionally, BI helps in structuring data and filtering out unnecessary
information. Textile Company A can now use the framework for descriptive and predictive
analytics. This allows the company to streamline existing processes and make better decisions
for future expansion planning.
Figure 2: BI Infrastructure for Data Analytics in Textile Company A after the Project
Completion.
The project started with an evaluation of the data analytics structure that existed in the
company. After assessing the available resources and the needs of the organization, decisions
were made regarding suitable storage solutions as well as BI tools. Further, the BI infrastructure
was planned, and the steps for its implementation into the existing information structure of the
company took into account all the necessary changes. After that, BI was implemented and
integrated into the analytics structure of Textile Company A. At the last stage, tests were carried
out to evaluate performance, and employees were trained to work with the end-user interface.
Review of Other Work
The materials considered in the project proposal made it possible to effectively plan the
development and implementation of the BI solution. However, during the implementation of
the project, additional research was required to identify points for improving the existing
design. The paper by Dieni et al. (2021) describes the process of creating a BI infrastructure
for unemployment rate analysis and management for the Central Bureau of Statistics of
Indonesia. Although this article does not belong to the scope of SMEs, it provides a detailed
description of all stages of the process of developing and implementing a BI infrastructure.
Dieni et al. (2021) pay great attention to the development stage of the ETL tool, which, as
emphasized, is the most significant for the correct operation of the BI structure. Based on the
analysis presented in the article, it was decided to use the Pentaho BI solution as the main ETL
tool, as well as a resource for the cloud data warehouse. This approach can both reduce the cost
of creating a BI infrastructure and provide a company with additional options for the future
expansion of an information-analytical system.
During the implementation of the project, the possible use of alternative solutions to create the
most balanced infrastructure was also considered. At the planning stage, it was decided to use
a hybrid approach to the data warehouse: both cloud-based and physical storage. Garani et al.
(2019) analyzed the case of implementing the BI solution in a telecommunication company.
They focus on the design of a data warehouse that would be effective for all the business tasks
required by the organization. As part of the research, Garani et al. (2019) propose the scheme
of the data warehouse containing five dimensions of data sets divided into different categories
(customers, calls, contracts, employment, and product). Most importantly, they explain how
each of those data sets is related to OLAP structures, which are used in the current project as
well. This study showed how to structure a data warehouse information system in the most
efficient way in order for it to work best with data OLAP. Additionally, the article confirmed
the need to use cloud-based storage for interaction with OLAP in conjunction with a physical
warehouse.
On the whole, OLAP and data warehouse design and implementation are the most challenging
parts of the project. At the stage of developing these parts of the infrastructure, many questions
arose regarding how best to organize these elements relative to each other. Al-Aqrabi et al.
(2019) present a case study on the integration of OLAP into the BI infrastructure. Most valuable
is that in this article, the researchers offer a detailed description of the OLAP framework and
describe the elements that interact most effectively with various types of data warehouse
solutions. The use of cloud-based BI data storage when integrating OLAP requires “compliance
of the BI application with web services architectural standards (and the standards defined by
the SaaS or PaaS provider, like Google Apps standards)” (Al-Aqrabi et al., 2019, p. 81). This
information is extremely important for the implementation of the project, as it allows you to
take into account the compatibility of infrastructure elements.
Thus, the literature reviewed in the project proposal helped in the analysis and evaluation of
the existing analytical structure of the company, as well as in the selection and design of a
relevant BI system. However, during the implementation of the project, it was necessary to
optimize the design of the infrastructure in the most efficient way so that it meets the needs of
the company and uses a limited amount of resources. The materials considered during the
implementation and described in this section made it possible to take into account the subtleties
of the design of BI infrastructures that were not obvious during preliminary planning. The study
of these sources will help create the most effective structure and avoid possible difficulties with
its operation.
Changes to the Project Environment
Before the implementation of the project, the analytical infrastructure of Textile Company A
was extremely outdated and required an upgrade in accordance with modern technical
standards. The company used manual data analysis of the companies listed in the databases,
which was carried out by the analytical department. After that, based on the analytics, reports
were compiled for the company as a whole without taking into account the needs of individual
departments. This structure was slow and inflexible and could also contain a large number of
errors and irrelevant information. This situation made it impossible to effectively optimize the
company’s operations, as well as plan for expansion.
The BI infrastructure implementation project has significantly changed the company’s
decision-making process based on the received analytical data. Most importantly, data analytics
has become as automated as possible, which has increased the efficiency of using the
information in the company at times. In particular, the ETL tool made it possible to filter out
irrelevant data, which minimized the number of errors. The inclusion of March data in the BI
infrastructure has allowed departments to receive personalized analytical reports based on their
needs. Additionally, the use of OPLA cubes has made the structure extremely stable in the event
of a future increase in the number of databases and an increase in the number of queries.
Overall, the implementation of the BI infrastructure has allowed Textile Company A to use
descriptive analytics to optimize its operations and predictive analytics to plan the company’s
expansion. It is also important that employees of various departments have access to
multidimensional information for better decision-making. Data analytics in the company has
become efficient, eliminating errors and providing the most relevant and important
information. This system can become the basis for the future development of the company and
a significant increase in its performance.
Methodology
ADDIE methodology model was chosen for the project execution as it presents the most
appropriate approach. This model includes five crucial steps: analysis, design, development,
implementation, and evaluation. The main advantage of this structure is the sequence of steps
and flexibility in the implementation of the project. In particular, ADDIE involves evaluation
after each of the major stages of project execution, which allows you to adjust its development
depending on the emerging conditions.
The analysis stage includes goal-setting and the resources available for the project. This step
also identifies the problem the business has and offers a set of potential solutions that could
address it. At the design stage, the specific tools that will be used within the project are
determined, deliverables are developed, as well as project evaluation criteria. Those two steps
have already been completed under this proposal and are described in the project overview
sections, which contain a description of the problem and the relevant IT solution.
The development stage started with the assignment of the analytical team, which will work
with ready-made BI. This step also includes the choice of the warehouse, the analytical
architecture of the system, and the end-user interface, as well as setting data integration tools.
The implementation of the project will include the creation of a fully functional BI structure
for Textile Company A and the completion of the onboarding of the analytical team. The final
stage of the project is evaluation, which requires tests to create informational reports for various
departments.
Project Goals and Objectives
Goal
Supporting objectives
Deliverables enabling the project
objectives
Met/Unmet
1. BI infrastructure features
assessment and planning
1.a. BI implementation
strategy development
1.a.i. Data sources and analytical tools
review
Met
1.a.ii. Setting objectives
Met
1.a.iii. Tools assessment
Met
1.b. Defining key BI
characteristics
1.b.i. BI team assignment
Met
1.b.ii. Define KPIs
Met
2. BI infrastructure design and
implementation
2.a. Setting data integration
tools
2.a.i. Setting data warehouse
Met
2.a.ii. Setting data integration tools or
ETL (Extract, Transform, Load)
Met
Goal
Supporting objectives
Deliverables enabling the project
objectives
Met/Unmet
2.b. Choosing an approach for
architecture design
2.b.i. Data warehouse connection
Met
2.b.ii. Setting online analytical
processing (OLAP)
Met
2.b.iii. Setting data marts
Met
2.c. The end-user interface
implementation
2.c.i. Setting ad hoc reporting systems
Met
2.c.ii. Conduct end-user training
Met
Goal 1
BI infrastructure features assessment and planning. At this stage, it is necessary to evaluate the
existing resources, infrastructure, goals, and responsible persons, as well as a potential vendor
for the implementation of BI. This goal has been met when all key aspects of the BI solution
are selected and clearly defined based on available data and based on the objectives of the
organization. This goal included two objectives:
Objective a
BI implementation strategy development. This objective includes activities to evaluate the
company’s current resources and infrastructures and appropriate changes that have to be made
in order to achieve the company’s goals. The objective was considered successful when all the
decisions regarding BI infrastructure design were made, including the choice of relevant and
available tools.
Objective b
Defining key BI characteristics. This objective allows the project to scale the required BI
structure and exclude features that the company does not need. At this stage, it is important to
assess the competencies available to the organization, as well as the KPIs that are key to
monitoring. The objective was considered successful when the complete diagram of the BI
infrastructure was created.
Goal 2
BI infrastructure design and implementation. This goal is key within the framework of this
project, as it involves the creation of a functioning BI infrastructure as a result. This objective
is supported by three objectives, which include setting data integration tools, BI architecture
design, and end-user interface implementation. These objectives describe the key aspects of
the software architecture BI solution and constitute a fully functional structure.
Objective a
Setting data integration tools. For the correct operation of BI, tools for collecting, processing,
and storing data are required, which requires the choice of data warehouse and data integration
tools. At the moment, cloud data storages are the most common, which is especially important
for SMEs. The objective was considered successful when the data integration tool was installed
and connected to the company’s databases.
Objective b
Choosing an approach for architecture design. At this stage, it is necessary to decide, based on
the needs and resources of the company, which BI architecture is the most relevant. In this
situation, the company plans to expand, so it would be logical to use a hybrid setup (warehouse
+ OLAP + data marts) to increase the potential duration of the BI solution. The objective was
considered successful when the data warehouse, OLAPs, and data marts were installed and
connected to the ETL tool.
Objective c
The end-user interface implementation. At this stage, it is important to choose a tool that will
allow the company to effectively represent the collected data in the form of analytical reports
for employees. The objective was considered successful when the end-user interface was
installed and connected to the BI structure.
Project Timeline
Milestone or deliverable
Planned
duration
(hours or
days)
Actual
duration
(hours or
days)
Projected
start date
Anticipated
end date
Actual start
date
Anticipated
start date
Project goals and objectives
discussion meeting
1 day
1 day
9.18.2024
9.18.2024
9.18.2024
9.18.2024
Determine existing data
analytics structure
2 days
2 days
9.19.2024
9.20.2024
9.19.2024
9.20.2024
Identify the problems existing
in the analytics structure
2 days
2 days
9.21.2024
9.22.2024
9.21.2024
9.22.2024
Identify adjustments needed
based on company’s goals and
resources
2 days
2 days
9.23.2024
9.24.2024
9.23.2024
9.24.2024
BI team assignment and
training
5 days
5 days
9.24.2024
9.28.2024
9.24.2024
9.28.2024
BI infrastructure design with
identification of appropriate
tools
5 days
5 days
9.29.2024
10.3.2024
9.29.2024
10.3.2024
BI infrastructure setting
10 days
12 days
10.4.2024
10.13.2024
10.04.2024
10.15.2024
BI infrastructure testing
2 days
2 days
10.14.2024
10.15.2024
10.16.2024
10.17.2024
End-users training
5 days
5 days
10.16.2024
10.20.2024
10.17.2024
10.21.2024
The main stages of the project implementation took place according to the schedule, as the
project team worked well-coordinated and according to a well-thought-out preliminary plan.
However, the delay occurred during the BI infrastructure setting phase, which was scheduled
to be completed in 10 days. Instead, the process took 12 days, as there were difficulties
connecting the company’s databases to the ETL tool, which required additional research and
planning. Difficulties arose due to the incompatibility of existing databases with the selected
ETL tool. After reviewing the materials described in the review of the work section, it was
decided to switch to Pentaho ETL solution, which required additional settings. Thus, the project
implementation ended on 10.22.2024 instead of 10.20.2024 with a delay of less than 2 days.
Unanticipated Requirements
An unanticipated requirement that arose during the implementation of the project was the need
to choose a more relevant ETL tool. The problem arose during setting up the BI infrastructure
and testing its operation. Initially, it was planned to use the Apache Spark tool as an ETL since
it is free and easy to implement. However, later it was discovered that this Apache Spark does
not correctly interact with various file formats, including PDF, which are widely used in
company databases. This problem led to incorrect data filtering, which drastically reduced the
effectiveness of the tool. Thus, it was decided to choose a more suitable instrument, which
resulted in the need for additional research and a slight delay. As a result, it was decided to use
Pentaho because this structure is able to work with a variety of files, and it is also widely
considered in the literature and is popular in case studies. The main reason for choosing this
solution was the widespread use of Pentaho among SMEs, which is most relevant for this
project.
Conclusions
The main result of the project implementation is the creation of a fully functional BI
infrastructure in Textile Company A. The potential effect of the project is the ability to
effectively use descriptive and predictive analytics in the organization to optimize current
operations and plan for expansion. Among the immediately observed effects, one can single
out the complete automation of the process of storing, analyzing data, and generating
information reports. Most importantly, the ETL tool integrated into the infrastructure allows
the company to minimize the amount of irrelevant information through the sorting of data in
the databases used. The project is considered successful because, as a result of the
implementation of the BI infrastructure, it became possible to generate multidimensional
informational reports that various departments of the company can use for a more efficient
decision-making process.
Project Deliverables
Figure 1 shows a diagram of the data analytics structure that existed at Textile Company A
before the project was implemented. Previously, the company used manual data analysis and
the generation of information reports, which were handled by employees of the analytical
department, which resulted in low efficiency of data analysis. Figure 2 illustrates the BI
infrastructure diagram that was created at Textile Company A as part of the project
implementation. This scheme describes all the key elements of the system and their interaction.
Based on this illustration, it can be judged that the project made it possible to automate the
process of data analytics in the company in order to increase the efficiency of the decision-
making process based on reports. Finally, Figure 3 contains an example of a multidimensional
report that is generated by the BI infrastructure created within the project. This illustration is
the most important deliverable of the project, as it shows the actual result of its implementation.
Figure 3: The Example of Multidimensional Report Generated by BI Infrastructure
Visualization Tool.
Textile Company A is a small business in North Carolina that is currently struggling due to the
changing global environment. The organization is engaged in the purchase of textiles in
Europe, the Middle and Far East, as well as their distribution in the United States. The
complication of global supply chains leads to a constant increase in the cost of purchasing
textiles, which negatively affects the company’s profits. To overcome this crisis, Textile
Company A plans to expand its operations to South America and Canada. In this regard, the
company needs to upgrade its information infrastructure for better data analytics within the
company.
Prior to the implementation of the project to create a business intelligence (BI) system, the
organization used an outdated approach to storing and analyzing data. It was minimally
automated and did not allow working effectively with a large amount of data. In particular, the
company used several separate data sources (CRM, ERP, individual databases, and external
sources) and generated individual reports based on the data received from each of them. This
approach made holistic data analytics and information-driven decision-making more difficult.
A diagram illustrating the old data analytics system at Textile Company A is presented in Figure
1.
Figure 1: Data Analytics System in Textile Company A before the Project Completion.
As part of the project, it was possible to create a new BI infrastructure in the organization,
which fully automated the company’s interaction with data. It was possible to create a structure
consisting of ETL tools (Extract, Transform, Load), a data warehouse, online analytical
processing (OLAP), and data marts. A detailed scheme of the BI infrastructure created for
Textile Company A is illustrated in Figure 2. As a result, this infrastructure uses all the
company’s data sources for analytics and generates the most informative reports for different
organization reports. Additionally, BI helps in structuring data and filtering out unnecessary
information. Textile Company A can now use the framework for descriptive and predictive
analytics. This allows the company to streamline existing processes and make better decisions
for future expansion planning.
Figure 2: BI Infrastructure for Data Analytics in Textile Company A after the Project
Completion.
The project started with an evaluation of the data analytics structure that existed in the
company. After assessing the available resources and the needs of the organization, decisions
were made regarding suitable storage solutions as well as BI tools. Further, the BI infrastructure
was planned, and the steps for its implementation into the existing information structure of the
company took into account all the necessary changes. After that, BI was implemented and
integrated into the analytics structure of Textile Company A. At the last stage, tests were carried
out to evaluate performance, and employees were trained to work with the end-user interface.
Review of Other Work
The materials considered in the project proposal made it possible to effectively plan the
development and implementation of the BI solution. However, during the implementation of
the project, additional research was required to identify points for improving the existing
design. The paper by Dieni et al. (2021) describes the process of creating a BI infrastructure
for unemployment rate analysis and management for the Central Bureau of Statistics of
Indonesia. Although this article does not belong to the scope of SMEs, it provides a detailed
description of all stages of the process of developing and implementing a BI infrastructure.
Dieni et al. (2021) pay great attention to the development stage of the ETL tool, which, as
emphasized, is the most significant for the correct operation of the BI structure. Based on the
analysis presented in the article, it was decided to use the Pentaho BI solution as the main ETL
tool, as well as a resource for the cloud data warehouse. This approach can both reduce the cost
of creating a BI infrastructure and provide a company with additional options for the future
expansion of an information-analytical system.
During the implementation of the project, the possible use of alternative solutions to create the
most balanced infrastructure was also considered. At the planning stage, it was decided to use
a hybrid approach to the data warehouse: both cloud-based and physical storage. Garani et al.
(2019) analyzed the case of implementing the BI solution in a telecommunication company.
They focus on the design of a data warehouse that would be effective for all the business tasks
required by the organization. As part of the research, Garani et al. (2019) propose the scheme
of the data warehouse containing five dimensions of data sets divided into different categories
(customers, calls, contracts, employment, and product). Most importantly, they explain how
each of those data sets is related to OLAP structures, which are used in the current project as
well. This study showed how to structure a data warehouse information system in the most
efficient way in order for it to work best with data OLAP. Additionally, the article confirmed
the need to use cloud-based storage for interaction with OLAP in conjunction with a physical
warehouse.
On the whole, OLAP and data warehouse design and implementation are the most challenging
parts of the project. At the stage of developing these parts of the infrastructure, many questions
arose regarding how best to organize these elements relative to each other. Al-Aqrabi et al.
(2019) present a case study on the integration of OLAP into the BI infrastructure. Most valuable
is that in this article, the researchers offer a detailed description of the OLAP framework and
describe the elements that interact most effectively with various types of data warehouse
solutions. The use of cloud-based BI data storage when integrating OLAP requires “compliance
of the BI application with web services architectural standards (and the standards defined by
the SaaS or PaaS provider, like Google Apps standards)” (Al-Aqrabi et al., 2019, p. 81). This
information is extremely important for the implementation of the project, as it allows you to
take into account the compatibility of infrastructure elements.
Thus, the literature reviewed in the project proposal helped in the analysis and evaluation of
the existing analytical structure of the company, as well as in the selection and design of a
relevant BI system. However, during the implementation of the project, it was necessary to
optimize the design of the infrastructure in the most efficient way so that it meets the needs of
the company and uses a limited amount of resources. The materials considered during the
implementation and described in this section made it possible to take into account the subtleties
of the design of BI infrastructures that were not obvious during preliminary planning. The study
of these sources will help create the most effective structure and avoid possible difficulties with
its operation.
Changes to the Project Environment
Before the implementation of the project, the analytical infrastructure of Textile Company A
was extremely outdated and required an upgrade in accordance with modern technical
standards. The company used manual data analysis of the companies listed in the databases,
which was carried out by the analytical department. After that, based on the analytics, reports
were compiled for the company as a whole without taking into account the needs of individual
departments. This structure was slow and inflexible and could also contain a large number of
errors and irrelevant information. This situation made it impossible to effectively optimize the
company’s operations, as well as plan for expansion.
The BI infrastructure implementation project has significantly changed the company’s
decision-making process based on the received analytical data. Most importantly, data analytics
has become as automated as possible, which has increased the efficiency of using the
information in the company at times. In particular, the ETL tool made it possible to filter out
irrelevant data, which minimized the number of errors. The inclusion of March data in the BI
infrastructure has allowed departments to receive personalized analytical reports based on their
needs. Additionally, the use of OPLA cubes has made the structure extremely stable in the event
of a future increase in the number of databases and an increase in the number of queries.
Overall, the implementation of the BI infrastructure has allowed Textile Company A to use
descriptive analytics to optimize its operations and predictive analytics to plan the company’s
expansion. It is also important that employees of various departments have access to
multidimensional information for better decision-making. Data analytics in the company has
become efficient, eliminating errors and providing the most relevant and important
information. This system can become the basis for the future development of the company and
a significant increase in its performance.
Methodology
ADDIE methodology model was chosen for the project execution as it presents the most
appropriate approach. This model includes five crucial steps: analysis, design, development,
implementation, and evaluation. The main advantage of this structure is the sequence of steps
and flexibility in the implementation of the project. In particular, ADDIE involves evaluation
after each of the major stages of project execution, which allows you to adjust its development
depending on the emerging conditions.
The analysis stage includes goal-setting and the resources available for the project. This step
also identifies the problem the business has and offers a set of potential solutions that could
address it. At the design stage, the specific tools that will be used within the project are
determined, deliverables are developed, as well as project evaluation criteria. Those two steps
have already been completed under this proposal and are described in the project overview
sections, which contain a description of the problem and the relevant IT solution.
The development stage started with the assignment of the analytical team, which will work
with ready-made BI. This step also includes the choice of the warehouse, the analytical
architecture of the system, and the end-user interface, as well as setting data integration tools.
The implementation of the project will include the creation of a fully functional BI structure
for Textile Company A and the completion of the onboarding of the analytical team. The final
stage of the project is evaluation, which requires tests to create informational reports for various
departments.
Project Goals and Objectives
Goal
Supporting objectives
Deliverables enabling the project
objectives
Met/Unmet
1. BI infrastructure features
assessment and planning
1.a. BI implementation
strategy development
1.a.i. Data sources and analytical tools
review
Met
1.a.ii. Setting objectives
Met
1.a.iii. Tools assessment
Met
1.b. Defining key BI
characteristics
1.b.i. BI team assignment
Met
1.b.ii. Define KPIs
Met
2. BI infrastructure design and
implementation
2.a. Setting data integration
tools
2.a.i. Setting data warehouse
Met
2.a.ii. Setting data integration tools or
ETL (Extract, Transform, Load)
Met
Goal
Supporting objectives
Deliverables enabling the project
objectives
Met/Unmet
2.b. Choosing an approach for
architecture design
2.b.i. Data warehouse connection
Met
2.b.ii. Setting online analytical
processing (OLAP)
Met
2.b.iii. Setting data marts
Met
2.c. The end-user interface
implementation
2.c.i. Setting ad hoc reporting systems
Met
2.c.ii. Conduct end-user training
Met
Goal 1
BI infrastructure features assessment and planning. At this stage, it is necessary to evaluate the
existing resources, infrastructure, goals, and responsible persons, as well as a potential vendor
for the implementation of BI. This goal has been met when all key aspects of the BI solution
are selected and clearly defined based on available data and based on the objectives of the
organization. This goal included two objectives:
Objective a
BI implementation strategy development. This objective includes activities to evaluate the
company’s current resources and infrastructures and appropriate changes that have to be made
in order to achieve the company’s goals. The objective was considered successful when all the
decisions regarding BI infrastructure design were made, including the choice of relevant and
available tools.
Objective b
Defining key BI characteristics. This objective allows the project to scale the required BI
structure and exclude features that the company does not need. At this stage, it is important to
assess the competencies available to the organization, as well as the KPIs that are key to
monitoring. The objective was considered successful when the complete diagram of the BI
infrastructure was created.
Goal 2
BI infrastructure design and implementation. This goal is key within the framework of this
project, as it involves the creation of a functioning BI infrastructure as a result. This objective
is supported by three objectives, which include setting data integration tools, BI architecture
design, and end-user interface implementation. These objectives describe the key aspects of
the software architecture BI solution and constitute a fully functional structure.
Objective a
Setting data integration tools. For the correct operation of BI, tools for collecting, processing,
and storing data are required, which requires the choice of data warehouse and data integration
tools. At the moment, cloud data storages are the most common, which is especially important
for SMEs. The objective was considered successful when the data integration tool was installed
and connected to the company’s databases.
Objective b
Choosing an approach for architecture design. At this stage, it is necessary to decide, based on
the needs and resources of the company, which BI architecture is the most relevant. In this
situation, the company plans to expand, so it would be logical to use a hybrid setup (warehouse
+ OLAP + data marts) to increase the potential duration of the BI solution. The objective was
considered successful when the data warehouse, OLAPs, and data marts were installed and
connected to the ETL tool.
Objective c
The end-user interface implementation. At this stage, it is important to choose a tool that will
allow the company to effectively represent the collected data in the form of analytical reports
for employees. The objective was considered successful when the end-user interface was
installed and connected to the BI structure.
Project Timeline
Milestone or deliverable
Planned
duration
(hours or
days)
Actual
duration
(hours or
days)
Projected
start date
Anticipated
end date
Actual start
date
Anticipated
start date
Project goals and objectives
discussion meeting
1 day
1 day
9.18.2024
9.18.2024
9.18.2024
9.18.2024
Determine existing data
analytics structure
2 days
2 days
9.19.2024
9.20.2024
9.19.2024
9.20.2024
Identify the problems existing
in the analytics structure
2 days
2 days
9.21.2024
9.22.2024
9.21.2024
9.22.2024
Identify adjustments needed
based on company’s goals and
resources
2 days
2 days
9.23.2024
9.24.2024
9.23.2024
9.24.2024
BI team assignment and
training
5 days
5 days
9.24.2024
9.28.2024
9.24.2024
9.28.2024
BI infrastructure design with
identification of appropriate
tools
5 days
5 days
9.29.2024
10.3.2024
9.29.2024
10.3.2024
BI infrastructure setting
10 days
12 days
10.4.2024
10.13.2024
10.04.2024
10.15.2024
BI infrastructure testing
2 days
2 days
10.14.2024
10.15.2024
10.16.2024
10.17.2024
End-users training
5 days
5 days
10.16.2024
10.20.2024
10.17.2024
10.21.2024
The main stages of the project implementation took place according to the schedule, as the
project team worked well-coordinated and according to a well-thought-out preliminary plan.
However, the delay occurred during the BI infrastructure setting phase, which was scheduled
to be completed in 10 days. Instead, the process took 12 days, as there were difficulties
connecting the company’s databases to the ETL tool, which required additional research and
planning. Difficulties arose due to the incompatibility of existing databases with the selected
ETL tool. After reviewing the materials described in the review of the work section, it was
decided to switch to Pentaho ETL solution, which required additional settings. Thus, the project
implementation ended on 10.22.2024 instead of 10.20.2024 with a delay of less than 2 days.
Unanticipated Requirements
An unanticipated requirement that arose during the implementation of the project was the need
to choose a more relevant ETL tool. The problem arose during setting up the BI infrastructure
and testing its operation. Initially, it was planned to use the Apache Spark tool as an ETL since
it is free and easy to implement. However, later it was discovered that this Apache Spark does
not correctly interact with various file formats, including PDF, which are widely used in
company databases. This problem led to incorrect data filtering, which drastically reduced the
effectiveness of the tool. Thus, it was decided to choose a more suitable instrument, which
resulted in the need for additional research and a slight delay. As a result, it was decided to use
Pentaho because this structure is able to work with a variety of files, and it is also widely
considered in the literature and is popular in case studies. The main reason for choosing this
solution was the widespread use of Pentaho among SMEs, which is most relevant for this
project.
Conclusions
The main result of the project implementation is the creation of a fully functional BI
infrastructure in Textile Company A. The potential effect of the project is the ability to
effectively use descriptive and predictive analytics in the organization to optimize current
operations and plan for expansion. Among the immediately observed effects, one can single
out the complete automation of the process of storing, analyzing data, and generating
information reports. Most importantly, the ETL tool integrated into the infrastructure allows
the company to minimize the amount of irrelevant information through the sorting of data in
the databases used. The project is considered successful because, as a result of the
implementation of the BI infrastructure, it became possible to generate multidimensional
informational reports that various departments of the company can use for a more efficient
decision-making process.
Project Deliverables
Figure 1 shows a diagram of the data analytics structure that existed at Textile Company A
before the project was implemented. Previously, the company used manual data analysis and
the generation of information reports, which were handled by employees of the analytical
department, which resulted in low efficiency of data analysis. Figure 2 illustrates the BI
infrastructure diagram that was created at Textile Company A as part of the project
implementation. This scheme describes all the key elements of the system and their interaction.
Based on this illustration, it can be judged that the project made it possible to automate the
process of data analytics in the company in order to increase the efficiency of the decision-
making process based on reports. Finally, Figure 3 contains an example of a multidimensional
report that is generated by the BI infrastructure created within the project. This illustration is
the most important deliverable of the project, as it shows the actual result of its implementation.
Figure 3: The Example of Multidimensional Report Generated by BI Infrastructure
Visualization Tool.
Textile Company A is a small business in North Carolina that is currently struggling due to the
changing global environment. The organization is engaged in the purchase of textiles in
Europe, the Middle and Far East, as well as their distribution in the United States. The
complication of global supply chains leads to a constant increase in the cost of purchasing
textiles, which negatively affects the company’s profits. To overcome this crisis, Textile
Company A plans to expand its operations to South America and Canada. In this regard, the
company needs to upgrade its information infrastructure for better data analytics within the
company.
Prior to the implementation of the project to create a business intelligence (BI) system, the
organization used an outdated approach to storing and analyzing data. It was minimally
automated and did not allow working effectively with a large amount of data. In particular, the
company used several separate data sources (CRM, ERP, individual databases, and external
sources) and generated individual reports based on the data received from each of them. This
approach made holistic data analytics and information-driven decision-making more difficult.
A diagram illustrating the old data analytics system at Textile Company A is presented in Figure
1.
Figure 1: Data Analytics System in Textile Company A before the Project Completion.
As part of the project, it was possible to create a new BI infrastructure in the organization,
which fully automated the company’s interaction with data. It was possible to create a structure
consisting of ETL tools (Extract, Transform, Load), a data warehouse, online analytical
processing (OLAP), and data marts. A detailed scheme of the BI infrastructure created for
Textile Company A is illustrated in Figure 2. As a result, this infrastructure uses all the
company’s data sources for analytics and generates the most informative reports for different
organization reports. Additionally, BI helps in structuring data and filtering out unnecessary
information. Textile Company A can now use the framework for descriptive and predictive
analytics. This allows the company to streamline existing processes and make better decisions
for future expansion planning.
Figure 2: BI Infrastructure for Data Analytics in Textile Company A after the Project
Completion.
The project started with an evaluation of the data analytics structure that existed in the
company. After assessing the available resources and the needs of the organization, decisions
were made regarding suitable storage solutions as well as BI tools. Further, the BI infrastructure
was planned, and the steps for its implementation into the existing information structure of the
company took into account all the necessary changes. After that, BI was implemented and
integrated into the analytics structure of Textile Company A. At the last stage, tests were carried
out to evaluate performance, and employees were trained to work with the end-user interface.
Review of Other Work
The materials considered in the project proposal made it possible to effectively plan the
development and implementation of the BI solution. However, during the implementation of
the project, additional research was required to identify points for improving the existing
design. The paper by Dieni et al. (2021) describes the process of creating a BI infrastructure
for unemployment rate analysis and management for the Central Bureau of Statistics of
Indonesia. Although this article does not belong to the scope of SMEs, it provides a detailed
description of all stages of the process of developing and implementing a BI infrastructure.
Dieni et al. (2021) pay great attention to the development stage of the ETL tool, which, as
emphasized, is the most significant for the correct operation of the BI structure. Based on the
analysis presented in the article, it was decided to use the Pentaho BI solution as the main ETL
tool, as well as a resource for the cloud data warehouse. This approach can both reduce the cost
of creating a BI infrastructure and provide a company with additional options for the future
expansion of an information-analytical system.
During the implementation of the project, the possible use of alternative solutions to create the
most balanced infrastructure was also considered. At the planning stage, it was decided to use
a hybrid approach to the data warehouse: both cloud-based and physical storage. Garani et al.
(2019) analyzed the case of implementing the BI solution in a telecommunication company.
They focus on the design of a data warehouse that would be effective for all the business tasks
required by the organization. As part of the research, Garani et al. (2019) propose the scheme
of the data warehouse containing five dimensions of data sets divided into different categories
(customers, calls, contracts, employment, and product). Most importantly, they explain how
each of those data sets is related to OLAP structures, which are used in the current project as
well. This study showed how to structure a data warehouse information system in the most
efficient way in order for it to work best with data OLAP. Additionally, the article confirmed
the need to use cloud-based storage for interaction with OLAP in conjunction with a physical
warehouse.
On the whole, OLAP and data warehouse design and implementation are the most challenging
parts of the project. At the stage of developing these parts of the infrastructure, many questions
arose regarding how best to organize these elements relative to each other. Al-Aqrabi et al.
(2019) present a case study on the integration of OLAP into the BI infrastructure. Most valuable
is that in this article, the researchers offer a detailed description of the OLAP framework and
describe the elements that interact most effectively with various types of data warehouse
solutions. The use of cloud-based BI data storage when integrating OLAP requires “compliance
of the BI application with web services architectural standards (and the standards defined by
the SaaS or PaaS provider, like Google Apps standards)” (Al-Aqrabi et al., 2019, p. 81). This
information is extremely important for the implementation of the project, as it allows you to
take into account the compatibility of infrastructure elements.
Thus, the literature reviewed in the project proposal helped in the analysis and evaluation of
the existing analytical structure of the company, as well as in the selection and design of a
relevant BI system. However, during the implementation of the project, it was necessary to
optimize the design of the infrastructure in the most efficient way so that it meets the needs of
the company and uses a limited amount of resources. The materials considered during the
implementation and described in this section made it possible to take into account the subtleties
of the design of BI infrastructures that were not obvious during preliminary planning. The study
of these sources will help create the most effective structure and avoid possible difficulties with
its operation.
Changes to the Project Environment
Before the implementation of the project, the analytical infrastructure of Textile Company A
was extremely outdated and required an upgrade in accordance with modern technical
standards. The company used manual data analysis of the companies listed in the databases,
which was carried out by the analytical department. After that, based on the analytics, reports
were compiled for the company as a whole without taking into account the needs of individual
departments. This structure was slow and inflexible and could also contain a large number of
errors and irrelevant information. This situation made it impossible to effectively optimize the
company’s operations, as well as plan for expansion.
The BI infrastructure implementation project has significantly changed the company’s
decision-making process based on the received analytical data. Most importantly, data analytics
has become as automated as possible, which has increased the efficiency of using the
information in the company at times. In particular, the ETL tool made it possible to filter out
irrelevant data, which minimized the number of errors. The inclusion of March data in the BI
infrastructure has allowed departments to receive personalized analytical reports based on their
needs. Additionally, the use of OPLA cubes has made the structure extremely stable in the event
of a future increase in the number of databases and an increase in the number of queries.
Overall, the implementation of the BI infrastructure has allowed Textile Company A to use
descriptive analytics to optimize its operations and predictive analytics to plan the company’s
expansion. It is also important that employees of various departments have access to
multidimensional information for better decision-making. Data analytics in the company has
become efficient, eliminating errors and providing the most relevant and important
information. This system can become the basis for the future development of the company and
a significant increase in its performance.
Methodology
ADDIE methodology model was chosen for the project execution as it presents the most
appropriate approach. This model includes five crucial steps: analysis, design, development,
implementation, and evaluation. The main advantage of this structure is the sequence of steps
and flexibility in the implementation of the project. In particular, ADDIE involves evaluation
after each of the major stages of project execution, which allows you to adjust its development
depending on the emerging conditions.
The analysis stage includes goal-setting and the resources available for the project. This step
also identifies the problem the business has and offers a set of potential solutions that could
address it. At the design stage, the specific tools that will be used within the project are
determined, deliverables are developed, as well as project evaluation criteria. Those two steps
have already been completed under this proposal and are described in the project overview
sections, which contain a description of the problem and the relevant IT solution.
The development stage started with the assignment of the analytical team, which will work
with ready-made BI. This step also includes the choice of the warehouse, the analytical
architecture of the system, and the end-user interface, as well as setting data integration tools.
The implementation of the project will include the creation of a fully functional BI structure
for Textile Company A and the completion of the onboarding of the analytical team. The final
stage of the project is evaluation, which requires tests to create informational reports for various
departments.
Project Goals and Objectives
Goal
Supporting objectives
Deliverables enabling the project
objectives
Met/Unmet
1. BI infrastructure features
assessment and planning
1.a. BI implementation
strategy development
1.a.i. Data sources and analytical tools
review
Met
1.a.ii. Setting objectives
Met
1.a.iii. Tools assessment
Met
1.b. Defining key BI
characteristics
1.b.i. BI team assignment
Met
1.b.ii. Define KPIs
Met
2. BI infrastructure design and
implementation
2.a. Setting data integration
tools
2.a.i. Setting data warehouse
Met
2.a.ii. Setting data integration tools or
ETL (Extract, Transform, Load)
Met
Goal
Supporting objectives
Deliverables enabling the project
objectives
Met/Unmet
2.b. Choosing an approach for
architecture design
2.b.i. Data warehouse connection
Met
2.b.ii. Setting online analytical
processing (OLAP)
Met
2.b.iii. Setting data marts
Met
2.c. The end-user interface
implementation
2.c.i. Setting ad hoc reporting systems
Met
2.c.ii. Conduct end-user training
Met
Goal 1
BI infrastructure features assessment and planning. At this stage, it is necessary to evaluate the
existing resources, infrastructure, goals, and responsible persons, as well as a potential vendor
for the implementation of BI. This goal has been met when all key aspects of the BI solution
are selected and clearly defined based on available data and based on the objectives of the
organization. This goal included two objectives:
Objective a
BI implementation strategy development. This objective includes activities to evaluate the
company’s current resources and infrastructures and appropriate changes that have to be made
in order to achieve the company’s goals. The objective was considered successful when all the
decisions regarding BI infrastructure design were made, including the choice of relevant and
available tools.
Objective b
Defining key BI characteristics. This objective allows the project to scale the required BI
structure and exclude features that the company does not need. At this stage, it is important to
assess the competencies available to the organization, as well as the KPIs that are key to
monitoring. The objective was considered successful when the complete diagram of the BI
infrastructure was created.
Goal 2
BI infrastructure design and implementation. This goal is key within the framework of this
project, as it involves the creation of a functioning BI infrastructure as a result. This objective
is supported by three objectives, which include setting data integration tools, BI architecture
design, and end-user interface implementation. These objectives describe the key aspects of
the software architecture BI solution and constitute a fully functional structure.
Objective a
Setting data integration tools. For the correct operation of BI, tools for collecting, processing,
and storing data are required, which requires the choice of data warehouse and data integration
tools. At the moment, cloud data storages are the most common, which is especially important
for SMEs. The objective was considered successful when the data integration tool was installed
and connected to the company’s databases.
Objective b
Choosing an approach for architecture design. At this stage, it is necessary to decide, based on
the needs and resources of the company, which BI architecture is the most relevant. In this
situation, the company plans to expand, so it would be logical to use a hybrid setup (warehouse
+ OLAP + data marts) to increase the potential duration of the BI solution. The objective was
considered successful when the data warehouse, OLAPs, and data marts were installed and
connected to the ETL tool.
Objective c
The end-user interface implementation. At this stage, it is important to choose a tool that will
allow the company to effectively represent the collected data in the form of analytical reports
for employees. The objective was considered successful when the end-user interface was
installed and connected to the BI structure.
Project Timeline
Milestone or deliverable
Planned
duration
(hours or
days)
Actual
duration
(hours or
days)
Projected
start date
Anticipated
end date
Actual start
date
Anticipated
start date
Project goals and objectives
discussion meeting
1 day
1 day
9.18.2024
9.18.2024
9.18.2024
9.18.2024
Determine existing data
analytics structure
2 days
2 days
9.19.2024
9.20.2024
9.19.2024
9.20.2024
Identify the problems existing
in the analytics structure
2 days
2 days
9.21.2024
9.22.2024
9.21.2024
9.22.2024
Identify adjustments needed
based on company’s goals and
resources
2 days
2 days
9.23.2024
9.24.2024
9.23.2024
9.24.2024
BI team assignment and
training
5 days
5 days
9.24.2024
9.28.2024
9.24.2024
9.28.2024
BI infrastructure design with
identification of appropriate
tools
5 days
5 days
9.29.2024
10.3.2024
9.29.2024
10.3.2024
BI infrastructure setting
10 days
12 days
10.4.2024
10.13.2024
10.04.2024
10.15.2024
BI infrastructure testing
2 days
2 days
10.14.2024
10.15.2024
10.16.2024
10.17.2024
End-users training
5 days
5 days
10.16.2024
10.20.2024
10.17.2024
10.21.2024
The main stages of the project implementation took place according to the schedule, as the
project team worked well-coordinated and according to a well-thought-out preliminary plan.
However, the delay occurred during the BI infrastructure setting phase, which was scheduled
to be completed in 10 days. Instead, the process took 12 days, as there were difficulties
connecting the company’s databases to the ETL tool, which required additional research and
planning. Difficulties arose due to the incompatibility of existing databases with the selected
ETL tool. After reviewing the materials described in the review of the work section, it was
decided to switch to Pentaho ETL solution, which required additional settings. Thus, the project
implementation ended on 10.22.2024 instead of 10.20.2024 with a delay of less than 2 days.
Unanticipated Requirements
An unanticipated requirement that arose during the implementation of the project was the need
to choose a more relevant ETL tool. The problem arose during setting up the BI infrastructure
and testing its operation. Initially, it was planned to use the Apache Spark tool as an ETL since
it is free and easy to implement. However, later it was discovered that this Apache Spark does
not correctly interact with various file formats, including PDF, which are widely used in
company databases. This problem led to incorrect data filtering, which drastically reduced the
effectiveness of the tool. Thus, it was decided to choose a more suitable instrument, which
resulted in the need for additional research and a slight delay. As a result, it was decided to use
Pentaho because this structure is able to work with a variety of files, and it is also widely
considered in the literature and is popular in case studies. The main reason for choosing this
solution was the widespread use of Pentaho among SMEs, which is most relevant for this
project.
Conclusions
The main result of the project implementation is the creation of a fully functional BI
infrastructure in Textile Company A. The potential effect of the project is the ability to
effectively use descriptive and predictive analytics in the organization to optimize current
operations and plan for expansion. Among the immediately observed effects, one can single
out the complete automation of the process of storing, analyzing data, and generating
information reports. Most importantly, the ETL tool integrated into the infrastructure allows
the company to minimize the amount of irrelevant information through the sorting of data in
the databases used. The project is considered successful because, as a result of the
implementation of the BI infrastructure, it became possible to generate multidimensional
informational reports that various departments of the company can use for a more efficient
decision-making process.
Project Deliverables
Figure 1 shows a diagram of the data analytics structure that existed at Textile Company A
before the project was implemented. Previously, the company used manual data analysis and
the generation of information reports, which were handled by employees of the analytical
department, which resulted in low efficiency of data analysis. Figure 2 illustrates the BI
infrastructure diagram that was created at Textile Company A as part of the project
implementation. This scheme describes all the key elements of the system and their interaction.
Based on this illustration, it can be judged that the project made it possible to automate the
process of data analytics in the company in order to increase the efficiency of the decision-
making process based on reports. Finally, Figure 3 contains an example of a multidimensional
report that is generated by the BI infrastructure created within the project. This illustration is
the most important deliverable of the project, as it shows the actual result of its implementation.
Figure 3: The Example of Multidimensional Report Generated by BI Infrastructure
Visualization Tool.
Textile Company A is a small business in North Carolina that is currently struggling due to the
changing global environment. The organization is engaged in the purchase of textiles in
Europe, the Middle and Far East, as well as their distribution in the United States. The
complication of global supply chains leads to a constant increase in the cost of purchasing
textiles, which negatively affects the company’s profits. To overcome this crisis, Textile
Company A plans to expand its operations to South America and Canada. In this regard, the
company needs to upgrade its information infrastructure for better data analytics within the
company.
Prior to the implementation of the project to create a business intelligence (BI) system, the
organization used an outdated approach to storing and analyzing data. It was minimally
automated and did not allow working effectively with a large amount of data. In particular, the
company used several separate data sources (CRM, ERP, individual databases, and external
sources) and generated individual reports based on the data received from each of them. This
approach made holistic data analytics and information-driven decision-making more difficult.
A diagram illustrating the old data analytics system at Textile Company A is presented in Figure
1.
Figure 1: Data Analytics System in Textile Company A before the Project Completion.
As part of the project, it was possible to create a new BI infrastructure in the organization,
which fully automated the company’s interaction with data. It was possible to create a structure
consisting of ETL tools (Extract, Transform, Load), a data warehouse, online analytical
processing (OLAP), and data marts. A detailed scheme of the BI infrastructure created for
Textile Company A is illustrated in Figure 2. As a result, this infrastructure uses all the
company’s data sources for analytics and generates the most informative reports for different
organization reports. Additionally, BI helps in structuring data and filtering out unnecessary
information. Textile Company A can now use the framework for descriptive and predictive
analytics. This allows the company to streamline existing processes and make better decisions
for future expansion planning.
Figure 2: BI Infrastructure for Data Analytics in Textile Company A after the Project
Completion.
The project started with an evaluation of the data analytics structure that existed in the
company. After assessing the available resources and the needs of the organization, decisions
were made regarding suitable storage solutions as well as BI tools. Further, the BI infrastructure
was planned, and the steps for its implementation into the existing information structure of the
company took into account all the necessary changes. After that, BI was implemented and
integrated into the analytics structure of Textile Company A. At the last stage, tests were carried
out to evaluate performance, and employees were trained to work with the end-user interface.
Review of Other Work
The materials considered in the project proposal made it possible to effectively plan the
development and implementation of the BI solution. However, during the implementation of
the project, additional research was required to identify points for improving the existing
design. The paper by Dieni et al. (2021) describes the process of creating a BI infrastructure
for unemployment rate analysis and management for the Central Bureau of Statistics of
Indonesia. Although this article does not belong to the scope of SMEs, it provides a detailed
description of all stages of the process of developing and implementing a BI infrastructure.
Dieni et al. (2021) pay great attention to the development stage of the ETL tool, which, as
emphasized, is the most significant for the correct operation of the BI structure. Based on the
analysis presented in the article, it was decided to use the Pentaho BI solution as the main ETL
tool, as well as a resource for the cloud data warehouse. This approach can both reduce the cost
of creating a BI infrastructure and provide a company with additional options for the future
expansion of an information-analytical system.
During the implementation of the project, the possible use of alternative solutions to create the
most balanced infrastructure was also considered. At the planning stage, it was decided to use
a hybrid approach to the data warehouse: both cloud-based and physical storage. Garani et al.
(2019) analyzed the case of implementing the BI solution in a telecommunication company.
They focus on the design of a data warehouse that would be effective for all the business tasks
required by the organization. As part of the research, Garani et al. (2019) propose the scheme
of the data warehouse containing five dimensions of data sets divided into different categories
(customers, calls, contracts, employment, and product). Most importantly, they explain how
each of those data sets is related to OLAP structures, which are used in the current project as
well. This study showed how to structure a data warehouse information system in the most
efficient way in order for it to work best with data OLAP. Additionally, the article confirmed
the need to use cloud-based storage for interaction with OLAP in conjunction with a physical
warehouse.
On the whole, OLAP and data warehouse design and implementation are the most challenging
parts of the project. At the stage of developing these parts of the infrastructure, many questions
arose regarding how best to organize these elements relative to each other. Al-Aqrabi et al.
(2019) present a case study on the integration of OLAP into the BI infrastructure. Most valuable
is that in this article, the researchers offer a detailed description of the OLAP framework and
describe the elements that interact most effectively with various types of data warehouse
solutions. The use of cloud-based BI data storage when integrating OLAP requires “compliance
of the BI application with web services architectural standards (and the standards defined by
the SaaS or PaaS provider, like Google Apps standards)” (Al-Aqrabi et al., 2019, p. 81). This
information is extremely important for the implementation of the project, as it allows you to
take into account the compatibility of infrastructure elements.
Thus, the literature reviewed in the project proposal helped in the analysis and evaluation of
the existing analytical structure of the company, as well as in the selection and design of a
relevant BI system. However, during the implementation of the project, it was necessary to
optimize the design of the infrastructure in the most efficient way so that it meets the needs of
the company and uses a limited amount of resources. The materials considered during the
implementation and described in this section made it possible to take into account the subtleties
of the design of BI infrastructures that were not obvious during preliminary planning. The study
of these sources will help create the most effective structure and avoid possible difficulties with
its operation.
Changes to the Project Environment
Before the implementation of the project, the analytical infrastructure of Textile Company A
was extremely outdated and required an upgrade in accordance with modern technical
standards. The company used manual data analysis of the companies listed in the databases,
which was carried out by the analytical department. After that, based on the analytics, reports
were compiled for the company as a whole without taking into account the needs of individual
departments. This structure was slow and inflexible and could also contain a large number of
errors and irrelevant information. This situation made it impossible to effectively optimize the
company’s operations, as well as plan for expansion.
The BI infrastructure implementation project has significantly changed the company’s
decision-making process based on the received analytical data. Most importantly, data analytics
has become as automated as possible, which has increased the efficiency of using the
information in the company at times. In particular, the ETL tool made it possible to filter out
irrelevant data, which minimized the number of errors. The inclusion of March data in the BI
infrastructure has allowed departments to receive personalized analytical reports based on their
needs. Additionally, the use of OPLA cubes has made the structure extremely stable in the event
of a future increase in the number of databases and an increase in the number of queries.
Overall, the implementation of the BI infrastructure has allowed Textile Company A to use
descriptive analytics to optimize its operations and predictive analytics to plan the company’s
expansion. It is also important that employees of various departments have access to
multidimensional information for better decision-making. Data analytics in the company has
become efficient, eliminating errors and providing the most relevant and important
information. This system can become the basis for the future development of the company and
a significant increase in its performance.
Methodology
ADDIE methodology model was chosen for the project execution as it presents the most
appropriate approach. This model includes five crucial steps: analysis, design, development,
implementation, and evaluation. The main advantage of this structure is the sequence of steps
and flexibility in the implementation of the project. In particular, ADDIE involves evaluation
after each of the major stages of project execution, which allows you to adjust its development
depending on the emerging conditions.
The analysis stage includes goal-setting and the resources available for the project. This step
also identifies the problem the business has and offers a set of potential solutions that could
address it. At the design stage, the specific tools that will be used within the project are
determined, deliverables are developed, as well as project evaluation criteria. Those two steps
have already been completed under this proposal and are described in the project overview
sections, which contain a description of the problem and the relevant IT solution.
The development stage started with the assignment of the analytical team, which will work
with ready-made BI. This step also includes the choice of the warehouse, the analytical
architecture of the system, and the end-user interface, as well as setting data integration tools.
The implementation of the project will include the creation of a fully functional BI structure
for Textile Company A and the completion of the onboarding of the analytical team. The final
stage of the project is evaluation, which requires tests to create informational reports for various
departments.
Project Goals and Objectives
Goal
Supporting objectives
Deliverables enabling the project
objectives
Met/Unmet
1. BI infrastructure features
assessment and planning
1.a. BI implementation
strategy development
1.a.i. Data sources and analytical tools
review
Met
1.a.ii. Setting objectives
Met
1.a.iii. Tools assessment
Met
1.b. Defining key BI
characteristics
1.b.i. BI team assignment
Met
1.b.ii. Define KPIs
Met
2. BI infrastructure design and
implementation
2.a. Setting data integration
tools
2.a.i. Setting data warehouse
Met
2.a.ii. Setting data integration tools or
ETL (Extract, Transform, Load)
Met
Goal
Supporting objectives
Deliverables enabling the project
objectives
Met/Unmet
2.b. Choosing an approach for
architecture design
2.b.i. Data warehouse connection
Met
2.b.ii. Setting online analytical
processing (OLAP)
Met
2.b.iii. Setting data marts
Met
2.c. The end-user interface
implementation
2.c.i. Setting ad hoc reporting systems
Met
2.c.ii. Conduct end-user training
Met
Goal 1
BI infrastructure features assessment and planning. At this stage, it is necessary to evaluate the
existing resources, infrastructure, goals, and responsible persons, as well as a potential vendor
for the implementation of BI. This goal has been met when all key aspects of the BI solution
are selected and clearly defined based on available data and based on the objectives of the
organization. This goal included two objectives:
Objective a
BI implementation strategy development. This objective includes activities to evaluate the
company’s current resources and infrastructures and appropriate changes that have to be made
in order to achieve the company’s goals. The objective was considered successful when all the
decisions regarding BI infrastructure design were made, including the choice of relevant and
available tools.
Objective b
Defining key BI characteristics. This objective allows the project to scale the required BI
structure and exclude features that the company does not need. At this stage, it is important to
assess the competencies available to the organization, as well as the KPIs that are key to
monitoring. The objective was considered successful when the complete diagram of the BI
infrastructure was created.
Goal 2
BI infrastructure design and implementation. This goal is key within the framework of this
project, as it involves the creation of a functioning BI infrastructure as a result. This objective
is supported by three objectives, which include setting data integration tools, BI architecture
design, and end-user interface implementation. These objectives describe the key aspects of
the software architecture BI solution and constitute a fully functional structure.
Objective a
Setting data integration tools. For the correct operation of BI, tools for collecting, processing,
and storing data are required, which requires the choice of data warehouse and data integration
tools. At the moment, cloud data storages are the most common, which is especially important
for SMEs. The objective was considered successful when the data integration tool was installed
and connected to the company’s databases.
Objective b
Choosing an approach for architecture design. At this stage, it is necessary to decide, based on
the needs and resources of the company, which BI architecture is the most relevant. In this
situation, the company plans to expand, so it would be logical to use a hybrid setup (warehouse
+ OLAP + data marts) to increase the potential duration of the BI solution. The objective was
considered successful when the data warehouse, OLAPs, and data marts were installed and
connected to the ETL tool.
Objective c
The end-user interface implementation. At this stage, it is important to choose a tool that will
allow the company to effectively represent the collected data in the form of analytical reports
for employees. The objective was considered successful when the end-user interface was
installed and connected to the BI structure.
Project Timeline
Milestone or deliverable
Planned
duration
(hours or
days)
Actual
duration
(hours or
days)
Projected
start date
Anticipated
end date
Actual start
date
Anticipated
start date
Project goals and objectives
discussion meeting
1 day
1 day
9.18.2024
9.18.2024
9.18.2024
9.18.2024
Determine existing data
analytics structure
2 days
2 days
9.19.2024
9.20.2024
9.19.2024
9.20.2024
Identify the problems existing
in the analytics structure
2 days
2 days
9.21.2024
9.22.2024
9.21.2024
9.22.2024
Identify adjustments needed
based on company’s goals and
resources
2 days
2 days
9.23.2024
9.24.2024
9.23.2024
9.24.2024
BI team assignment and
training
5 days
5 days
9.24.2024
9.28.2024
9.24.2024
9.28.2024
BI infrastructure design with
identification of appropriate
tools
5 days
5 days
9.29.2024
10.3.2024
9.29.2024
10.3.2024
BI infrastructure setting
10 days
12 days
10.4.2024
10.13.2024
10.04.2024
10.15.2024
BI infrastructure testing
2 days
2 days
10.14.2024
10.15.2024
10.16.2024
10.17.2024
End-users training
5 days
5 days
10.16.2024
10.20.2024
10.17.2024
10.21.2024
The main stages of the project implementation took place according to the schedule, as the
project team worked well-coordinated and according to a well-thought-out preliminary plan.
However, the delay occurred during the BI infrastructure setting phase, which was scheduled
to be completed in 10 days. Instead, the process took 12 days, as there were difficulties
connecting the company’s databases to the ETL tool, which required additional research and
planning. Difficulties arose due to the incompatibility of existing databases with the selected
ETL tool. After reviewing the materials described in the review of the work section, it was
decided to switch to Pentaho ETL solution, which required additional settings. Thus, the project
implementation ended on 10.22.2024 instead of 10.20.2024 with a delay of less than 2 days.
Unanticipated Requirements
An unanticipated requirement that arose during the implementation of the project was the need
to choose a more relevant ETL tool. The problem arose during setting up the BI infrastructure
and testing its operation. Initially, it was planned to use the Apache Spark tool as an ETL since
it is free and easy to implement. However, later it was discovered that this Apache Spark does
not correctly interact with various file formats, including PDF, which are widely used in
company databases. This problem led to incorrect data filtering, which drastically reduced the
effectiveness of the tool. Thus, it was decided to choose a more suitable instrument, which
resulted in the need for additional research and a slight delay. As a result, it was decided to use
Pentaho because this structure is able to work with a variety of files, and it is also widely
considered in the literature and is popular in case studies. The main reason for choosing this
solution was the widespread use of Pentaho among SMEs, which is most relevant for this
project.
Conclusions
The main result of the project implementation is the creation of a fully functional BI
infrastructure in Textile Company A. The potential effect of the project is the ability to
effectively use descriptive and predictive analytics in the organization to optimize current
operations and plan for expansion. Among the immediately observed effects, one can single
out the complete automation of the process of storing, analyzing data, and generating
information reports. Most importantly, the ETL tool integrated into the infrastructure allows
the company to minimize the amount of irrelevant information through the sorting of data in
the databases used. The project is considered successful because, as a result of the
implementation of the BI infrastructure, it became possible to generate multidimensional
informational reports that various departments of the company can use for a more efficient
decision-making process.
Project Deliverables
Figure 1 shows a diagram of the data analytics structure that existed at Textile Company A
before the project was implemented. Previously, the company used manual data analysis and
the generation of information reports, which were handled by employees of the analytical
department, which resulted in low efficiency of data analysis. Figure 2 illustrates the BI
infrastructure diagram that was created at Textile Company A as part of the project
implementation. This scheme describes all the key elements of the system and their interaction.
Based on this illustration, it can be judged that the project made it possible to automate the
process of data analytics in the company in order to increase the efficiency of the decision-
making process based on reports. Finally, Figure 3 contains an example of a multidimensional
report that is generated by the BI infrastructure created within the project. This illustration is
the most important deliverable of the project, as it shows the actual result of its implementation.
Figure 3: The Example of Multidimensional Report Generated by BI Infrastructure
Visualization Tool.
Textile Company A is a small business in North Carolina that is currently struggling due to the
changing global environment. The organization is engaged in the purchase of textiles in
Europe, the Middle and Far East, as well as their distribution in the United States. The
complication of global supply chains leads to a constant increase in the cost of purchasing
textiles, which negatively affects the company’s profits. To overcome this crisis, Textile
Company A plans to expand its operations to South America and Canada. In this regard, the
company needs to upgrade its information infrastructure for better data analytics within the
company.
Prior to the implementation of the project to create a business intelligence (BI) system, the
organization used an outdated approach to storing and analyzing data. It was minimally
automated and did not allow working effectively with a large amount of data. In particular, the
company used several separate data sources (CRM, ERP, individual databases, and external
sources) and generated individual reports based on the data received from each of them. This
approach made holistic data analytics and information-driven decision-making more difficult.
A diagram illustrating the old data analytics system at Textile Company A is presented in Figure
1.
Figure 1: Data Analytics System in Textile Company A before the Project Completion.
As part of the project, it was possible to create a new BI infrastructure in the organization,
which fully automated the company’s interaction with data. It was possible to create a structure
consisting of ETL tools (Extract, Transform, Load), a data warehouse, online analytical
processing (OLAP), and data marts. A detailed scheme of the BI infrastructure created for
Textile Company A is illustrated in Figure 2. As a result, this infrastructure uses all the
company’s data sources for analytics and generates the most informative reports for different
organization reports. Additionally, BI helps in structuring data and filtering out unnecessary
information. Textile Company A can now use the framework for descriptive and predictive
analytics. This allows the company to streamline existing processes and make better decisions
for future expansion planning.
Figure 2: BI Infrastructure for Data Analytics in Textile Company A after the Project
Completion.
The project started with an evaluation of the data analytics structure that existed in the
company. After assessing the available resources and the needs of the organization, decisions
were made regarding suitable storage solutions as well as BI tools. Further, the BI infrastructure
was planned, and the steps for its implementation into the existing information structure of the
company took into account all the necessary changes. After that, BI was implemented and
integrated into the analytics structure of Textile Company A. At the last stage, tests were carried
out to evaluate performance, and employees were trained to work with the end-user interface.
Review of Other Work
The materials considered in the project proposal made it possible to effectively plan the
development and implementation of the BI solution. However, during the implementation of
the project, additional research was required to identify points for improving the existing
design. The paper by Dieni et al. (2021) describes the process of creating a BI infrastructure
for unemployment rate analysis and management for the Central Bureau of Statistics of
Indonesia. Although this article does not belong to the scope of SMEs, it provides a detailed
description of all stages of the process of developing and implementing a BI infrastructure.
Dieni et al. (2021) pay great attention to the development stage of the ETL tool, which, as
emphasized, is the most significant for the correct operation of the BI structure. Based on the
analysis presented in the article, it was decided to use the Pentaho BI solution as the main ETL
tool, as well as a resource for the cloud data warehouse. This approach can both reduce the cost
of creating a BI infrastructure and provide a company with additional options for the future
expansion of an information-analytical system.
During the implementation of the project, the possible use of alternative solutions to create the
most balanced infrastructure was also considered. At the planning stage, it was decided to use
a hybrid approach to the data warehouse: both cloud-based and physical storage. Garani et al.
(2019) analyzed the case of implementing the BI solution in a telecommunication company.
They focus on the design of a data warehouse that would be effective for all the business tasks
required by the organization. As part of the research, Garani et al. (2019) propose the scheme
of the data warehouse containing five dimensions of data sets divided into different categories
(customers, calls, contracts, employment, and product). Most importantly, they explain how
each of those data sets is related to OLAP structures, which are used in the current project as
well. This study showed how to structure a data warehouse information system in the most
efficient way in order for it to work best with data OLAP. Additionally, the article confirmed
the need to use cloud-based storage for interaction with OLAP in conjunction with a physical
warehouse.
On the whole, OLAP and data warehouse design and implementation are the most challenging
parts of the project. At the stage of developing these parts of the infrastructure, many questions
arose regarding how best to organize these elements relative to each other. Al-Aqrabi et al.
(2019) present a case study on the integration of OLAP into the BI infrastructure. Most valuable
is that in this article, the researchers offer a detailed description of the OLAP framework and
describe the elements that interact most effectively with various types of data warehouse
solutions. The use of cloud-based BI data storage when integrating OLAP requires “compliance
of the BI application with web services architectural standards (and the standards defined by
the SaaS or PaaS provider, like Google Apps standards)” (Al-Aqrabi et al., 2019, p. 81). This
information is extremely important for the implementation of the project, as it allows you to
take into account the compatibility of infrastructure elements.
Thus, the literature reviewed in the project proposal helped in the analysis and evaluation of
the existing analytical structure of the company, as well as in the selection and design of a
relevant BI system. However, during the implementation of the project, it was necessary to
optimize the design of the infrastructure in the most efficient way so that it meets the needs of
the company and uses a limited amount of resources. The materials considered during the
implementation and described in this section made it possible to take into account the subtleties
of the design of BI infrastructures that were not obvious during preliminary planning. The study
of these sources will help create the most effective structure and avoid possible difficulties with
its operation.
Changes to the Project Environment
Before the implementation of the project, the analytical infrastructure of Textile Company A
was extremely outdated and required an upgrade in accordance with modern technical
standards. The company used manual data analysis of the companies listed in the databases,
which was carried out by the analytical department. After that, based on the analytics, reports
were compiled for the company as a whole without taking into account the needs of individual
departments. This structure was slow and inflexible and could also contain a large number of
errors and irrelevant information. This situation made it impossible to effectively optimize the
company’s operations, as well as plan for expansion.
The BI infrastructure implementation project has significantly changed the company’s
decision-making process based on the received analytical data. Most importantly, data analytics
has become as automated as possible, which has increased the efficiency of using the
information in the company at times. In particular, the ETL tool made it possible to filter out
irrelevant data, which minimized the number of errors. The inclusion of March data in the BI
infrastructure has allowed departments to receive personalized analytical reports based on their
needs. Additionally, the use of OPLA cubes has made the structure extremely stable in the event
of a future increase in the number of databases and an increase in the number of queries.
Overall, the implementation of the BI infrastructure has allowed Textile Company A to use
descriptive analytics to optimize its operations and predictive analytics to plan the company’s
expansion. It is also important that employees of various departments have access to
multidimensional information for better decision-making. Data analytics in the company has
become efficient, eliminating errors and providing the most relevant and important
information. This system can become the basis for the future development of the company and
a significant increase in its performance.
Methodology
ADDIE methodology model was chosen for the project execution as it presents the most
appropriate approach. This model includes five crucial steps: analysis, design, development,
implementation, and evaluation. The main advantage of this structure is the sequence of steps
and flexibility in the implementation of the project. In particular, ADDIE involves evaluation
after each of the major stages of project execution, which allows you to adjust its development
depending on the emerging conditions.
The analysis stage includes goal-setting and the resources available for the project. This step
also identifies the problem the business has and offers a set of potential solutions that could
address it. At the design stage, the specific tools that will be used within the project are
determined, deliverables are developed, as well as project evaluation criteria. Those two steps
have already been completed under this proposal and are described in the project overview
sections, which contain a description of the problem and the relevant IT solution.
The development stage started with the assignment of the analytical team, which will work
with ready-made BI. This step also includes the choice of the warehouse, the analytical
architecture of the system, and the end-user interface, as well as setting data integration tools.
The implementation of the project will include the creation of a fully functional BI structure
for Textile Company A and the completion of the onboarding of the analytical team. The final
stage of the project is evaluation, which requires tests to create informational reports for various
departments.
Project Goals and Objectives
Goal
Supporting objectives
Deliverables enabling the project
objectives
Met/Unmet
1. BI infrastructure features
assessment and planning
1.a. BI implementation
strategy development
1.a.i. Data sources and analytical tools
review
Met
1.a.ii. Setting objectives
Met
1.a.iii. Tools assessment
Met
1.b. Defining key BI
characteristics
1.b.i. BI team assignment
Met
1.b.ii. Define KPIs
Met
2. BI infrastructure design and
implementation
2.a. Setting data integration
tools
2.a.i. Setting data warehouse
Met
2.a.ii. Setting data integration tools or
ETL (Extract, Transform, Load)
Met
Goal
Supporting objectives
Deliverables enabling the project
objectives
Met/Unmet
2.b. Choosing an approach for
architecture design
2.b.i. Data warehouse connection
Met
2.b.ii. Setting online analytical
processing (OLAP)
Met
2.b.iii. Setting data marts
Met
2.c. The end-user interface
implementation
2.c.i. Setting ad hoc reporting systems
Met
2.c.ii. Conduct end-user training
Met
Goal 1
BI infrastructure features assessment and planning. At this stage, it is necessary to evaluate the
existing resources, infrastructure, goals, and responsible persons, as well as a potential vendor
for the implementation of BI. This goal has been met when all key aspects of the BI solution
are selected and clearly defined based on available data and based on the objectives of the
organization. This goal included two objectives:
Objective a
BI implementation strategy development. This objective includes activities to evaluate the
company’s current resources and infrastructures and appropriate changes that have to be made
in order to achieve the company’s goals. The objective was considered successful when all the
decisions regarding BI infrastructure design were made, including the choice of relevant and
available tools.
Objective b
Defining key BI characteristics. This objective allows the project to scale the required BI
structure and exclude features that the company does not need. At this stage, it is important to
assess the competencies available to the organization, as well as the KPIs that are key to
monitoring. The objective was considered successful when the complete diagram of the BI
infrastructure was created.
Goal 2
BI infrastructure design and implementation. This goal is key within the framework of this
project, as it involves the creation of a functioning BI infrastructure as a result. This objective
is supported by three objectives, which include setting data integration tools, BI architecture
design, and end-user interface implementation. These objectives describe the key aspects of
the software architecture BI solution and constitute a fully functional structure.
Objective a
Setting data integration tools. For the correct operation of BI, tools for collecting, processing,
and storing data are required, which requires the choice of data warehouse and data integration
tools. At the moment, cloud data storages are the most common, which is especially important
for SMEs. The objective was considered successful when the data integration tool was installed
and connected to the company’s databases.
Objective b
Choosing an approach for architecture design. At this stage, it is necessary to decide, based on
the needs and resources of the company, which BI architecture is the most relevant. In this
situation, the company plans to expand, so it would be logical to use a hybrid setup (warehouse
+ OLAP + data marts) to increase the potential duration of the BI solution. The objective was
considered successful when the data warehouse, OLAPs, and data marts were installed and
connected to the ETL tool.
Objective c
The end-user interface implementation. At this stage, it is important to choose a tool that will
allow the company to effectively represent the collected data in the form of analytical reports
for employees. The objective was considered successful when the end-user interface was
installed and connected to the BI structure.
Project Timeline
Milestone or deliverable
Planned
duration
(hours or
days)
Actual
duration
(hours or
days)
Projected
start date
Anticipated
end date
Actual start
date
Anticipated
start date
Project goals and objectives
discussion meeting
1 day
1 day
9.18.2024
9.18.2024
9.18.2024
9.18.2024
Determine existing data
analytics structure
2 days
2 days
9.19.2024
9.20.2024
9.19.2024
9.20.2024
Identify the problems existing
in the analytics structure
2 days
2 days
9.21.2024
9.22.2024
9.21.2024
9.22.2024
Identify adjustments needed
based on company’s goals and
resources
2 days
2 days
9.23.2024
9.24.2024
9.23.2024
9.24.2024
BI team assignment and
training
5 days
5 days
9.24.2024
9.28.2024
9.24.2024
9.28.2024
BI infrastructure design with
identification of appropriate
tools
5 days
5 days
9.29.2024
10.3.2024
9.29.2024
10.3.2024
BI infrastructure setting
10 days
12 days
10.4.2024
10.13.2024
10.04.2024
10.15.2024
BI infrastructure testing
2 days
2 days
10.14.2024
10.15.2024
10.16.2024
10.17.2024
End-users training
5 days
5 days
10.16.2024
10.20.2024
10.17.2024
10.21.2024
The main stages of the project implementation took place according to the schedule, as the
project team worked well-coordinated and according to a well-thought-out preliminary plan.
However, the delay occurred during the BI infrastructure setting phase, which was scheduled
to be completed in 10 days. Instead, the process took 12 days, as there were difficulties
connecting the company’s databases to the ETL tool, which required additional research and
planning. Difficulties arose due to the incompatibility of existing databases with the selected
ETL tool. After reviewing the materials described in the review of the work section, it was
decided to switch to Pentaho ETL solution, which required additional settings. Thus, the project
implementation ended on 10.22.2024 instead of 10.20.2024 with a delay of less than 2 days.
Unanticipated Requirements
An unanticipated requirement that arose during the implementation of the project was the need
to choose a more relevant ETL tool. The problem arose during setting up the BI infrastructure
and testing its operation. Initially, it was planned to use the Apache Spark tool as an ETL since
it is free and easy to implement. However, later it was discovered that this Apache Spark does
not correctly interact with various file formats, including PDF, which are widely used in
company databases. This problem led to incorrect data filtering, which drastically reduced the
effectiveness of the tool. Thus, it was decided to choose a more suitable instrument, which
resulted in the need for additional research and a slight delay. As a result, it was decided to use
Pentaho because this structure is able to work with a variety of files, and it is also widely
considered in the literature and is popular in case studies. The main reason for choosing this
solution was the widespread use of Pentaho among SMEs, which is most relevant for this
project.
Conclusions
The main result of the project implementation is the creation of a fully functional BI
infrastructure in Textile Company A. The potential effect of the project is the ability to
effectively use descriptive and predictive analytics in the organization to optimize current
operations and plan for expansion. Among the immediately observed effects, one can single
out the complete automation of the process of storing, analyzing data, and generating
information reports. Most importantly, the ETL tool integrated into the infrastructure allows
the company to minimize the amount of irrelevant information through the sorting of data in
the databases used. The project is considered successful because, as a result of the
implementation of the BI infrastructure, it became possible to generate multidimensional
informational reports that various departments of the company can use for a more efficient
decision-making process.
Project Deliverables
Figure 1 shows a diagram of the data analytics structure that existed at Textile Company A
before the project was implemented. Previously, the company used manual data analysis and
the generation of information reports, which were handled by employees of the analytical
department, which resulted in low efficiency of data analysis. Figure 2 illustrates the BI
infrastructure diagram that was created at Textile Company A as part of the project
implementation. This scheme describes all the key elements of the system and their interaction.
Based on this illustration, it can be judged that the project made it possible to automate the
process of data analytics in the company in order to increase the efficiency of the decision-
making process based on reports. Finally, Figure 3 contains an example of a multidimensional
report that is generated by the BI infrastructure created within the project. This illustration is
the most important deliverable of the project, as it shows the actual result of its implementation.
Figure 3: The Example of Multidimensional Report Generated by BI Infrastructure
Visualization Tool.
Textile Company A is a small business in North Carolina that is currently struggling due to the
changing global environment. The organization is engaged in the purchase of textiles in
Europe, the Middle and Far East, as well as their distribution in the United States. The
complication of global supply chains leads to a constant increase in the cost of purchasing
textiles, which negatively affects the company’s profits. To overcome this crisis, Textile
Company A plans to expand its operations to South America and Canada. In this regard, the
company needs to upgrade its information infrastructure for better data analytics within the
company.
Prior to the implementation of the project to create a business intelligence (BI) system, the
organization used an outdated approach to storing and analyzing data. It was minimally
automated and did not allow working effectively with a large amount of data. In particular, the
company used several separate data sources (CRM, ERP, individual databases, and external
sources) and generated individual reports based on the data received from each of them. This
approach made holistic data analytics and information-driven decision-making more difficult.
A diagram illustrating the old data analytics system at Textile Company A is presented in Figure
1.
Figure 1: Data Analytics System in Textile Company A before the Project Completion.
As part of the project, it was possible to create a new BI infrastructure in the organization,
which fully automated the company’s interaction with data. It was possible to create a structure
consisting of ETL tools (Extract, Transform, Load), a data warehouse, online analytical
processing (OLAP), and data marts. A detailed scheme of the BI infrastructure created for
Textile Company A is illustrated in Figure 2. As a result, this infrastructure uses all the
company’s data sources for analytics and generates the most informative reports for different
organization reports. Additionally, BI helps in structuring data and filtering out unnecessary
information. Textile Company A can now use the framework for descriptive and predictive
analytics. This allows the company to streamline existing processes and make better decisions
for future expansion planning.
Figure 2: BI Infrastructure for Data Analytics in Textile Company A after the Project
Completion.
The project started with an evaluation of the data analytics structure that existed in the
company. After assessing the available resources and the needs of the organization, decisions
were made regarding suitable storage solutions as well as BI tools. Further, the BI infrastructure
was planned, and the steps for its implementation into the existing information structure of the
company took into account all the necessary changes. After that, BI was implemented and
integrated into the analytics structure of Textile Company A. At the last stage, tests were carried
out to evaluate performance, and employees were trained to work with the end-user interface.
Review of Other Work
The materials considered in the project proposal made it possible to effectively plan the
development and implementation of the BI solution. However, during the implementation of
the project, additional research was required to identify points for improving the existing
design. The paper by Dieni et al. (2021) describes the process of creating a BI infrastructure
for unemployment rate analysis and management for the Central Bureau of Statistics of
Indonesia. Although this article does not belong to the scope of SMEs, it provides a detailed
description of all stages of the process of developing and implementing a BI infrastructure.
Dieni et al. (2021) pay great attention to the development stage of the ETL tool, which, as
emphasized, is the most significant for the correct operation of the BI structure. Based on the
analysis presented in the article, it was decided to use the Pentaho BI solution as the main ETL
tool, as well as a resource for the cloud data warehouse. This approach can both reduce the cost
of creating a BI infrastructure and provide a company with additional options for the future
expansion of an information-analytical system.
During the implementation of the project, the possible use of alternative solutions to create the
most balanced infrastructure was also considered. At the planning stage, it was decided to use
a hybrid approach to the data warehouse: both cloud-based and physical storage. Garani et al.
(2019) analyzed the case of implementing the BI solution in a telecommunication company.
They focus on the design of a data warehouse that would be effective for all the business tasks
required by the organization. As part of the research, Garani et al. (2019) propose the scheme
of the data warehouse containing five dimensions of data sets divided into different categories
(customers, calls, contracts, employment, and product). Most importantly, they explain how
each of those data sets is related to OLAP structures, which are used in the current project as
well. This study showed how to structure a data warehouse information system in the most
efficient way in order for it to work best with data OLAP. Additionally, the article confirmed
the need to use cloud-based storage for interaction with OLAP in conjunction with a physical
warehouse.
On the whole, OLAP and data warehouse design and implementation are the most challenging
parts of the project. At the stage of developing these parts of the infrastructure, many questions
arose regarding how best to organize these elements relative to each other. Al-Aqrabi et al.
(2019) present a case study on the integration of OLAP into the BI infrastructure. Most valuable
is that in this article, the researchers offer a detailed description of the OLAP framework and
describe the elements that interact most effectively with various types of data warehouse
solutions. The use of cloud-based BI data storage when integrating OLAP requires “compliance
of the BI application with web services architectural standards (and the standards defined by
the SaaS or PaaS provider, like Google Apps standards)” (Al-Aqrabi et al., 2019, p. 81). This
information is extremely important for the implementation of the project, as it allows you to
take into account the compatibility of infrastructure elements.
Thus, the literature reviewed in the project proposal helped in the analysis and evaluation of
the existing analytical structure of the company, as well as in the selection and design of a
relevant BI system. However, during the implementation of the project, it was necessary to
optimize the design of the infrastructure in the most efficient way so that it meets the needs of
the company and uses a limited amount of resources. The materials considered during the
implementation and described in this section made it possible to take into account the subtleties
of the design of BI infrastructures that were not obvious during preliminary planning. The study
of these sources will help create the most effective structure and avoid possible difficulties with
its operation.
Changes to the Project Environment
Before the implementation of the project, the analytical infrastructure of Textile Company A
was extremely outdated and required an upgrade in accordance with modern technical
standards. The company used manual data analysis of the companies listed in the databases,
which was carried out by the analytical department. After that, based on the analytics, reports
were compiled for the company as a whole without taking into account the needs of individual
departments. This structure was slow and inflexible and could also contain a large number of
errors and irrelevant information. This situation made it impossible to effectively optimize the
company’s operations, as well as plan for expansion.
The BI infrastructure implementation project has significantly changed the company’s
decision-making process based on the received analytical data. Most importantly, data analytics
has become as automated as possible, which has increased the efficiency of using the
information in the company at times. In particular, the ETL tool made it possible to filter out
irrelevant data, which minimized the number of errors. The inclusion of March data in the BI
infrastructure has allowed departments to receive personalized analytical reports based on their
needs. Additionally, the use of OPLA cubes has made the structure extremely stable in the event
of a future increase in the number of databases and an increase in the number of queries.
Overall, the implementation of the BI infrastructure has allowed Textile Company A to use
descriptive analytics to optimize its operations and predictive analytics to plan the company’s
expansion. It is also important that employees of various departments have access to
multidimensional information for better decision-making. Data analytics in the company has
become efficient, eliminating errors and providing the most relevant and important
information. This system can become the basis for the future development of the company and
a significant increase in its performance.
Methodology
ADDIE methodology model was chosen for the project execution as it presents the most
appropriate approach. This model includes five crucial steps: analysis, design, development,
implementation, and evaluation. The main advantage of this structure is the sequence of steps
and flexibility in the implementation of the project. In particular, ADDIE involves evaluation
after each of the major stages of project execution, which allows you to adjust its development
depending on the emerging conditions.
The analysis stage includes goal-setting and the resources available for the project. This step
also identifies the problem the business has and offers a set of potential solutions that could
address it. At the design stage, the specific tools that will be used within the project are
determined, deliverables are developed, as well as project evaluation criteria. Those two steps
have already been completed under this proposal and are described in the project overview
sections, which contain a description of the problem and the relevant IT solution.
The development stage started with the assignment of the analytical team, which will work
with ready-made BI. This step also includes the choice of the warehouse, the analytical
architecture of the system, and the end-user interface, as well as setting data integration tools.
The implementation of the project will include the creation of a fully functional BI structure
for Textile Company A and the completion of the onboarding of the analytical team. The final
stage of the project is evaluation, which requires tests to create informational reports for various
departments.
Project Goals and Objectives
Goal
Supporting objectives
Deliverables enabling the project
objectives
Met/Unmet
1. BI infrastructure features
assessment and planning
1.a. BI implementation
strategy development
1.a.i. Data sources and analytical tools
review
Met
1.a.ii. Setting objectives
Met
1.a.iii. Tools assessment
Met
1.b. Defining key BI
characteristics
1.b.i. BI team assignment
Met
1.b.ii. Define KPIs
Met
2. BI infrastructure design and
implementation
2.a. Setting data integration
tools
2.a.i. Setting data warehouse
Met
2.a.ii. Setting data integration tools or
ETL (Extract, Transform, Load)
Met
Goal
Supporting objectives
Deliverables enabling the project
objectives
Met/Unmet
2.b. Choosing an approach for
architecture design
2.b.i. Data warehouse connection
Met
2.b.ii. Setting online analytical
processing (OLAP)
Met
2.b.iii. Setting data marts
Met
2.c. The end-user interface
implementation
2.c.i. Setting ad hoc reporting systems
Met
2.c.ii. Conduct end-user training
Met
Goal 1
BI infrastructure features assessment and planning. At this stage, it is necessary to evaluate the
existing resources, infrastructure, goals, and responsible persons, as well as a potential vendor
for the implementation of BI. This goal has been met when all key aspects of the BI solution
are selected and clearly defined based on available data and based on the objectives of the
organization. This goal included two objectives:
Objective a
BI implementation strategy development. This objective includes activities to evaluate the
company’s current resources and infrastructures and appropriate changes that have to be made
in order to achieve the company’s goals. The objective was considered successful when all the
decisions regarding BI infrastructure design were made, including the choice of relevant and
available tools.
Objective b
Defining key BI characteristics. This objective allows the project to scale the required BI
structure and exclude features that the company does not need. At this stage, it is important to
assess the competencies available to the organization, as well as the KPIs that are key to
monitoring. The objective was considered successful when the complete diagram of the BI
infrastructure was created.
Goal 2
BI infrastructure design and implementation. This goal is key within the framework of this
project, as it involves the creation of a functioning BI infrastructure as a result. This objective
is supported by three objectives, which include setting data integration tools, BI architecture
design, and end-user interface implementation. These objectives describe the key aspects of
the software architecture BI solution and constitute a fully functional structure.
Objective a
Setting data integration tools. For the correct operation of BI, tools for collecting, processing,
and storing data are required, which requires the choice of data warehouse and data integration
tools. At the moment, cloud data storages are the most common, which is especially important
for SMEs. The objective was considered successful when the data integration tool was installed
and connected to the company’s databases.
Objective b
Choosing an approach for architecture design. At this stage, it is necessary to decide, based on
the needs and resources of the company, which BI architecture is the most relevant. In this
situation, the company plans to expand, so it would be logical to use a hybrid setup (warehouse
+ OLAP + data marts) to increase the potential duration of the BI solution. The objective was
considered successful when the data warehouse, OLAPs, and data marts were installed and
connected to the ETL tool.
Objective c
The end-user interface implementation. At this stage, it is important to choose a tool that will
allow the company to effectively represent the collected data in the form of analytical reports
for employees. The objective was considered successful when the end-user interface was
installed and connected to the BI structure.
Project Timeline
Milestone or deliverable
Planned
duration
(hours or
days)
Actual
duration
(hours or
days)
Projected
start date
Anticipated
end date
Actual start
date
Anticipated
start date
Project goals and objectives
discussion meeting
1 day
1 day
9.18.2024
9.18.2024
9.18.2024
9.18.2024
Determine existing data
analytics structure
2 days
2 days
9.19.2024
9.20.2024
9.19.2024
9.20.2024
Identify the problems existing
in the analytics structure
2 days
2 days
9.21.2024
9.22.2024
9.21.2024
9.22.2024
Identify adjustments needed
based on company’s goals and
resources
2 days
2 days
9.23.2024
9.24.2024
9.23.2024
9.24.2024
BI team assignment and
training
5 days
5 days
9.24.2024
9.28.2024
9.24.2024
9.28.2024
BI infrastructure design with
identification of appropriate
tools
5 days
5 days
9.29.2024
10.3.2024
9.29.2024
10.3.2024
BI infrastructure setting
10 days
12 days
10.4.2024
10.13.2024
10.04.2024
10.15.2024
BI infrastructure testing
2 days
2 days
10.14.2024
10.15.2024
10.16.2024
10.17.2024
End-users training
5 days
5 days
10.16.2024
10.20.2024
10.17.2024
10.21.2024
The main stages of the project implementation took place according to the schedule, as the
project team worked well-coordinated and according to a well-thought-out preliminary plan.
However, the delay occurred during the BI infrastructure setting phase, which was scheduled
to be completed in 10 days. Instead, the process took 12 days, as there were difficulties
connecting the company’s databases to the ETL tool, which required additional research and
planning. Difficulties arose due to the incompatibility of existing databases with the selected
ETL tool. After reviewing the materials described in the review of the work section, it was
decided to switch to Pentaho ETL solution, which required additional settings. Thus, the project
implementation ended on 10.22.2024 instead of 10.20.2024 with a delay of less than 2 days.
Unanticipated Requirements
An unanticipated requirement that arose during the implementation of the project was the need
to choose a more relevant ETL tool. The problem arose during setting up the BI infrastructure
and testing its operation. Initially, it was planned to use the Apache Spark tool as an ETL since
it is free and easy to implement. However, later it was discovered that this Apache Spark does
not correctly interact with various file formats, including PDF, which are widely used in
company databases. This problem led to incorrect data filtering, which drastically reduced the
effectiveness of the tool. Thus, it was decided to choose a more suitable instrument, which
resulted in the need for additional research and a slight delay. As a result, it was decided to use
Pentaho because this structure is able to work with a variety of files, and it is also widely
considered in the literature and is popular in case studies. The main reason for choosing this
solution was the widespread use of Pentaho among SMEs, which is most relevant for this
project.
Conclusions
The main result of the project implementation is the creation of a fully functional BI
infrastructure in Textile Company A. The potential effect of the project is the ability to
effectively use descriptive and predictive analytics in the organization to optimize current
operations and plan for expansion. Among the immediately observed effects, one can single
out the complete automation of the process of storing, analyzing data, and generating
information reports. Most importantly, the ETL tool integrated into the infrastructure allows
the company to minimize the amount of irrelevant information through the sorting of data in
the databases used. The project is considered successful because, as a result of the
implementation of the BI infrastructure, it became possible to generate multidimensional
informational reports that various departments of the company can use for a more efficient
decision-making process.
Project Deliverables
Figure 1 shows a diagram of the data analytics structure that existed at Textile Company A
before the project was implemented. Previously, the company used manual data analysis and
the generation of information reports, which were handled by employees of the analytical
department, which resulted in low efficiency of data analysis. Figure 2 illustrates the BI
infrastructure diagram that was created at Textile Company A as part of the project
implementation. This scheme describes all the key elements of the system and their interaction.
Based on this illustration, it can be judged that the project made it possible to automate the
process of data analytics in the company in order to increase the efficiency of the decision-
making process based on reports. Finally, Figure 3 contains an example of a multidimensional
report that is generated by the BI infrastructure created within the project. This illustration is
the most important deliverable of the project, as it shows the actual result of its implementation.
Figure 3: The Example of Multidimensional Report Generated by BI Infrastructure
Visualization Tool.
Textile Company A is a small business in North Carolina that is currently struggling due to the
changing global environment. The organization is engaged in the purchase of textiles in
Europe, the Middle and Far East, as well as their distribution in the United States. The
complication of global supply chains leads to a constant increase in the cost of purchasing
textiles, which negatively affects the company’s profits. To overcome this crisis, Textile
Company A plans to expand its operations to South America and Canada. In this regard, the
company needs to upgrade its information infrastructure for better data analytics within the
company.
Prior to the implementation of the project to create a business intelligence (BI) system, the
organization used an outdated approach to storing and analyzing data. It was minimally
automated and did not allow working effectively with a large amount of data. In particular, the
company used several separate data sources (CRM, ERP, individual databases, and external
sources) and generated individual reports based on the data received from each of them. This
approach made holistic data analytics and information-driven decision-making more difficult.
A diagram illustrating the old data analytics system at Textile Company A is presented in Figure
1.
Figure 1: Data Analytics System in Textile Company A before the Project Completion.
As part of the project, it was possible to create a new BI infrastructure in the organization,
which fully automated the company’s interaction with data. It was possible to create a structure
consisting of ETL tools (Extract, Transform, Load), a data warehouse, online analytical
processing (OLAP), and data marts. A detailed scheme of the BI infrastructure created for
Textile Company A is illustrated in Figure 2. As a result, this infrastructure uses all the
company’s data sources for analytics and generates the most informative reports for different
organization reports. Additionally, BI helps in structuring data and filtering out unnecessary
information. Textile Company A can now use the framework for descriptive and predictive
analytics. This allows the company to streamline existing processes and make better decisions
for future expansion planning.
Figure 2: BI Infrastructure for Data Analytics in Textile Company A after the Project
Completion.
The project started with an evaluation of the data analytics structure that existed in the
company. After assessing the available resources and the needs of the organization, decisions
were made regarding suitable storage solutions as well as BI tools. Further, the BI infrastructure
was planned, and the steps for its implementation into the existing information structure of the
company took into account all the necessary changes. After that, BI was implemented and
integrated into the analytics structure of Textile Company A. At the last stage, tests were carried
out to evaluate performance, and employees were trained to work with the end-user interface.
Review of Other Work
The materials considered in the project proposal made it possible to effectively plan the
development and implementation of the BI solution. However, during the implementation of
the project, additional research was required to identify points for improving the existing
design. The paper by Dieni et al. (2021) describes the process of creating a BI infrastructure
for unemployment rate analysis and management for the Central Bureau of Statistics of
Indonesia. Although this article does not belong to the scope of SMEs, it provides a detailed
description of all stages of the process of developing and implementing a BI infrastructure.
Dieni et al. (2021) pay great attention to the development stage of the ETL tool, which, as
emphasized, is the most significant for the correct operation of the BI structure. Based on the
analysis presented in the article, it was decided to use the Pentaho BI solution as the main ETL
tool, as well as a resource for the cloud data warehouse. This approach can both reduce the cost
of creating a BI infrastructure and provide a company with additional options for the future
expansion of an information-analytical system.
During the implementation of the project, the possible use of alternative solutions to create the
most balanced infrastructure was also considered. At the planning stage, it was decided to use
a hybrid approach to the data warehouse: both cloud-based and physical storage. Garani et al.
(2019) analyzed the case of implementing the BI solution in a telecommunication company.
They focus on the design of a data warehouse that would be effective for all the business tasks
required by the organization. As part of the research, Garani et al. (2019) propose the scheme
of the data warehouse containing five dimensions of data sets divided into different categories
(customers, calls, contracts, employment, and product). Most importantly, they explain how
each of those data sets is related to OLAP structures, which are used in the current project as
well. This study showed how to structure a data warehouse information system in the most
efficient way in order for it to work best with data OLAP. Additionally, the article confirmed
the need to use cloud-based storage for interaction with OLAP in conjunction with a physical
warehouse.
On the whole, OLAP and data warehouse design and implementation are the most challenging
parts of the project. At the stage of developing these parts of the infrastructure, many questions
arose regarding how best to organize these elements relative to each other. Al-Aqrabi et al.
(2019) present a case study on the integration of OLAP into the BI infrastructure. Most valuable
is that in this article, the researchers offer a detailed description of the OLAP framework and
describe the elements that interact most effectively with various types of data warehouse
solutions. The use of cloud-based BI data storage when integrating OLAP requires “compliance
of the BI application with web services architectural standards (and the standards defined by
the SaaS or PaaS provider, like Google Apps standards)” (Al-Aqrabi et al., 2019, p. 81). This
information is extremely important for the implementation of the project, as it allows you to
take into account the compatibility of infrastructure elements.
Thus, the literature reviewed in the project proposal helped in the analysis and evaluation of
the existing analytical structure of the company, as well as in the selection and design of a
relevant BI system. However, during the implementation of the project, it was necessary to
optimize the design of the infrastructure in the most efficient way so that it meets the needs of
the company and uses a limited amount of resources. The materials considered during the
implementation and described in this section made it possible to take into account the subtleties
of the design of BI infrastructures that were not obvious during preliminary planning. The study
of these sources will help create the most effective structure and avoid possible difficulties with
its operation.
Changes to the Project Environment
Before the implementation of the project, the analytical infrastructure of Textile Company A
was extremely outdated and required an upgrade in accordance with modern technical
standards. The company used manual data analysis of the companies listed in the databases,
which was carried out by the analytical department. After that, based on the analytics, reports
were compiled for the company as a whole without taking into account the needs of individual
departments. This structure was slow and inflexible and could also contain a large number of
errors and irrelevant information. This situation made it impossible to effectively optimize the
company’s operations, as well as plan for expansion.
The BI infrastructure implementation project has significantly changed the company’s
decision-making process based on the received analytical data. Most importantly, data analytics
has become as automated as possible, which has increased the efficiency of using the
information in the company at times. In particular, the ETL tool made it possible to filter out
irrelevant data, which minimized the number of errors. The inclusion of March data in the BI
infrastructure has allowed departments to receive personalized analytical reports based on their
needs. Additionally, the use of OPLA cubes has made the structure extremely stable in the event
of a future increase in the number of databases and an increase in the number of queries.
Overall, the implementation of the BI infrastructure has allowed Textile Company A to use
descriptive analytics to optimize its operations and predictive analytics to plan the company’s
expansion. It is also important that employees of various departments have access to
multidimensional information for better decision-making. Data analytics in the company has
become efficient, eliminating errors and providing the most relevant and important
information. This system can become the basis for the future development of the company and
a significant increase in its performance.
Methodology
ADDIE methodology model was chosen for the project execution as it presents the most
appropriate approach. This model includes five crucial steps: analysis, design, development,
implementation, and evaluation. The main advantage of this structure is the sequence of steps
and flexibility in the implementation of the project. In particular, ADDIE involves evaluation
after each of the major stages of project execution, which allows you to adjust its development
depending on the emerging conditions.
The analysis stage includes goal-setting and the resources available for the project. This step
also identifies the problem the business has and offers a set of potential solutions that could
address it. At the design stage, the specific tools that will be used within the project are
determined, deliverables are developed, as well as project evaluation criteria. Those two steps
have already been completed under this proposal and are described in the project overview
sections, which contain a description of the problem and the relevant IT solution.
The development stage started with the assignment of the analytical team, which will work
with ready-made BI. This step also includes the choice of the warehouse, the analytical
architecture of the system, and the end-user interface, as well as setting data integration tools.
The implementation of the project will include the creation of a fully functional BI structure
for Textile Company A and the completion of the onboarding of the analytical team. The final
stage of the project is evaluation, which requires tests to create informational reports for various
departments.
Project Goals and Objectives
Goal
Supporting objectives
Deliverables enabling the project
objectives
Met/Unmet
1. BI infrastructure features
assessment and planning
1.a. BI implementation
strategy development
1.a.i. Data sources and analytical tools
review
Met
1.a.ii. Setting objectives
Met
1.a.iii. Tools assessment
Met
1.b. Defining key BI
characteristics
1.b.i. BI team assignment
Met
1.b.ii. Define KPIs
Met
2. BI infrastructure design and
implementation
2.a. Setting data integration
tools
2.a.i. Setting data warehouse
Met
2.a.ii. Setting data integration tools or
ETL (Extract, Transform, Load)
Met
Goal
Supporting objectives
Deliverables enabling the project
objectives
Met/Unmet
2.b. Choosing an approach for
architecture design
2.b.i. Data warehouse connection
Met
2.b.ii. Setting online analytical
processing (OLAP)
Met
2.b.iii. Setting data marts
Met
2.c. The end-user interface
implementation
2.c.i. Setting ad hoc reporting systems
Met
2.c.ii. Conduct end-user training
Met
Goal 1
BI infrastructure features assessment and planning. At this stage, it is necessary to evaluate the
existing resources, infrastructure, goals, and responsible persons, as well as a potential vendor
for the implementation of BI. This goal has been met when all key aspects of the BI solution
are selected and clearly defined based on available data and based on the objectives of the
organization. This goal included two objectives:
Objective a
BI implementation strategy development. This objective includes activities to evaluate the
company’s current resources and infrastructures and appropriate changes that have to be made
in order to achieve the company’s goals. The objective was considered successful when all the
decisions regarding BI infrastructure design were made, including the choice of relevant and
available tools.
Objective b
Defining key BI characteristics. This objective allows the project to scale the required BI
structure and exclude features that the company does not need. At this stage, it is important to
assess the competencies available to the organization, as well as the KPIs that are key to
monitoring. The objective was considered successful when the complete diagram of the BI
infrastructure was created.
Goal 2
BI infrastructure design and implementation. This goal is key within the framework of this
project, as it involves the creation of a functioning BI infrastructure as a result. This objective
is supported by three objectives, which include setting data integration tools, BI architecture
design, and end-user interface implementation. These objectives describe the key aspects of
the software architecture BI solution and constitute a fully functional structure.
Objective a
Setting data integration tools. For the correct operation of BI, tools for collecting, processing,
and storing data are required, which requires the choice of data warehouse and data integration
tools. At the moment, cloud data storages are the most common, which is especially important
for SMEs. The objective was considered successful when the data integration tool was installed
and connected to the company’s databases.
Objective b
Choosing an approach for architecture design. At this stage, it is necessary to decide, based on
the needs and resources of the company, which BI architecture is the most relevant. In this
situation, the company plans to expand, so it would be logical to use a hybrid setup (warehouse
+ OLAP + data marts) to increase the potential duration of the BI solution. The objective was
considered successful when the data warehouse, OLAPs, and data marts were installed and
connected to the ETL tool.
Objective c
The end-user interface implementation. At this stage, it is important to choose a tool that will
allow the company to effectively represent the collected data in the form of analytical reports
for employees. The objective was considered successful when the end-user interface was
installed and connected to the BI structure.
Project Timeline
Milestone or deliverable
Planned
duration
(hours or
days)
Actual
duration
(hours or
days)
Projected
start date
Anticipated
end date
Actual start
date
Anticipated
start date
Project goals and objectives
discussion meeting
1 day
1 day
9.18.2024
9.18.2024
9.18.2024
9.18.2024
Determine existing data
analytics structure
2 days
2 days
9.19.2024
9.20.2024
9.19.2024
9.20.2024
Identify the problems existing
in the analytics structure
2 days
2 days
9.21.2024
9.22.2024
9.21.2024
9.22.2024
Identify adjustments needed
based on company’s goals and
resources
2 days
2 days
9.23.2024
9.24.2024
9.23.2024
9.24.2024
BI team assignment and
training
5 days
5 days
9.24.2024
9.28.2024
9.24.2024
9.28.2024
BI infrastructure design with
identification of appropriate
tools
5 days
5 days
9.29.2024
10.3.2024
9.29.2024
10.3.2024
BI infrastructure setting
10 days
12 days
10.4.2024
10.13.2024
10.04.2024
10.15.2024
BI infrastructure testing
2 days
2 days
10.14.2024
10.15.2024
10.16.2024
10.17.2024
End-users training
5 days
5 days
10.16.2024
10.20.2024
10.17.2024
10.21.2024
The main stages of the project implementation took place according to the schedule, as the
project team worked well-coordinated and according to a well-thought-out preliminary plan.
However, the delay occurred during the BI infrastructure setting phase, which was scheduled
to be completed in 10 days. Instead, the process took 12 days, as there were difficulties
connecting the company’s databases to the ETL tool, which required additional research and
planning. Difficulties arose due to the incompatibility of existing databases with the selected
ETL tool. After reviewing the materials described in the review of the work section, it was
decided to switch to Pentaho ETL solution, which required additional settings. Thus, the project
implementation ended on 10.22.2024 instead of 10.20.2024 with a delay of less than 2 days.
Unanticipated Requirements
An unanticipated requirement that arose during the implementation of the project was the need
to choose a more relevant ETL tool. The problem arose during setting up the BI infrastructure
and testing its operation. Initially, it was planned to use the Apache Spark tool as an ETL since
it is free and easy to implement. However, later it was discovered that this Apache Spark does
not correctly interact with various file formats, including PDF, which are widely used in
company databases. This problem led to incorrect data filtering, which drastically reduced the
effectiveness of the tool. Thus, it was decided to choose a more suitable instrument, which
resulted in the need for additional research and a slight delay. As a result, it was decided to use
Pentaho because this structure is able to work with a variety of files, and it is also widely
considered in the literature and is popular in case studies. The main reason for choosing this
solution was the widespread use of Pentaho among SMEs, which is most relevant for this
project.
Conclusions
The main result of the project implementation is the creation of a fully functional BI
infrastructure in Textile Company A. The potential effect of the project is the ability to
effectively use descriptive and predictive analytics in the organization to optimize current
operations and plan for expansion. Among the immediately observed effects, one can single
out the complete automation of the process of storing, analyzing data, and generating
information reports. Most importantly, the ETL tool integrated into the infrastructure allows
the company to minimize the amount of irrelevant information through the sorting of data in
the databases used. The project is considered successful because, as a result of the
implementation of the BI infrastructure, it became possible to generate multidimensional
informational reports that various departments of the company can use for a more efficient
decision-making process.
Project Deliverables
Figure 1 shows a diagram of the data analytics structure that existed at Textile Company A
before the project was implemented. Previously, the company used manual data analysis and
the generation of information reports, which were handled by employees of the analytical
department, which resulted in low efficiency of data analysis. Figure 2 illustrates the BI
infrastructure diagram that was created at Textile Company A as part of the project
implementation. This scheme describes all the key elements of the system and their interaction.
Based on this illustration, it can be judged that the project made it possible to automate the
process of data analytics in the company in order to increase the efficiency of the decision-
making process based on reports. Finally, Figure 3 contains an example of a multidimensional
report that is generated by the BI infrastructure created within the project. This illustration is
the most important deliverable of the project, as it shows the actual result of its implementation.
Figure 3: The Example of Multidimensional Report Generated by BI Infrastructure
Visualization Tool.
Textile Company A is a small business in North Carolina that is currently struggling due to the
changing global environment. The organization is engaged in the purchase of textiles in
Europe, the Middle and Far East, as well as their distribution in the United States. The
complication of global supply chains leads to a constant increase in the cost of purchasing
textiles, which negatively affects the company’s profits. To overcome this crisis, Textile
Company A plans to expand its operations to South America and Canada. In this regard, the
company needs to upgrade its information infrastructure for better data analytics within the
company.
Prior to the implementation of the project to create a business intelligence (BI) system, the
organization used an outdated approach to storing and analyzing data. It was minimally
automated and did not allow working effectively with a large amount of data. In particular, the
company used several separate data sources (CRM, ERP, individual databases, and external
sources) and generated individual reports based on the data received from each of them. This
approach made holistic data analytics and information-driven decision-making more difficult.
A diagram illustrating the old data analytics system at Textile Company A is presented in Figure
1.
Figure 1: Data Analytics System in Textile Company A before the Project Completion.
As part of the project, it was possible to create a new BI infrastructure in the organization,
which fully automated the company’s interaction with data. It was possible to create a structure
consisting of ETL tools (Extract, Transform, Load), a data warehouse, online analytical
processing (OLAP), and data marts. A detailed scheme of the BI infrastructure created for
Textile Company A is illustrated in Figure 2. As a result, this infrastructure uses all the
company’s data sources for analytics and generates the most informative reports for different
organization reports. Additionally, BI helps in structuring data and filtering out unnecessary
information. Textile Company A can now use the framework for descriptive and predictive
analytics. This allows the company to streamline existing processes and make better decisions
for future expansion planning.
Figure 2: BI Infrastructure for Data Analytics in Textile Company A after the Project
Completion.
The project started with an evaluation of the data analytics structure that existed in the
company. After assessing the available resources and the needs of the organization, decisions
were made regarding suitable storage solutions as well as BI tools. Further, the BI infrastructure
was planned, and the steps for its implementation into the existing information structure of the
company took into account all the necessary changes. After that, BI was implemented and
integrated into the analytics structure of Textile Company A. At the last stage, tests were carried
out to evaluate performance, and employees were trained to work with the end-user interface.
Review of Other Work
The materials considered in the project proposal made it possible to effectively plan the
development and implementation of the BI solution. However, during the implementation of
the project, additional research was required to identify points for improving the existing
design. The paper by Dieni et al. (2021) describes the process of creating a BI infrastructure
for unemployment rate analysis and management for the Central Bureau of Statistics of
Indonesia. Although this article does not belong to the scope of SMEs, it provides a detailed
description of all stages of the process of developing and implementing a BI infrastructure.
Dieni et al. (2021) pay great attention to the development stage of the ETL tool, which, as
emphasized, is the most significant for the correct operation of the BI structure. Based on the
analysis presented in the article, it was decided to use the Pentaho BI solution as the main ETL
tool, as well as a resource for the cloud data warehouse. This approach can both reduce the cost
of creating a BI infrastructure and provide a company with additional options for the future
expansion of an information-analytical system.
During the implementation of the project, the possible use of alternative solutions to create the
most balanced infrastructure was also considered. At the planning stage, it was decided to use
a hybrid approach to the data warehouse: both cloud-based and physical storage. Garani et al.
(2019) analyzed the case of implementing the BI solution in a telecommunication company.
They focus on the design of a data warehouse that would be effective for all the business tasks
required by the organization. As part of the research, Garani et al. (2019) propose the scheme
of the data warehouse containing five dimensions of data sets divided into different categories
(customers, calls, contracts, employment, and product). Most importantly, they explain how
each of those data sets is related to OLAP structures, which are used in the current project as
well. This study showed how to structure a data warehouse information system in the most
efficient way in order for it to work best with data OLAP. Additionally, the article confirmed
the need to use cloud-based storage for interaction with OLAP in conjunction with a physical
warehouse.
On the whole, OLAP and data warehouse design and implementation are the most challenging
parts of the project. At the stage of developing these parts of the infrastructure, many questions
arose regarding how best to organize these elements relative to each other. Al-Aqrabi et al.
(2019) present a case study on the integration of OLAP into the BI infrastructure. Most valuable
is that in this article, the researchers offer a detailed description of the OLAP framework and
describe the elements that interact most effectively with various types of data warehouse
solutions. The use of cloud-based BI data storage when integrating OLAP requires “compliance
of the BI application with web services architectural standards (and the standards defined by
the SaaS or PaaS provider, like Google Apps standards)” (Al-Aqrabi et al., 2019, p. 81). This
information is extremely important for the implementation of the project, as it allows you to
take into account the compatibility of infrastructure elements.
Thus, the literature reviewed in the project proposal helped in the analysis and evaluation of
the existing analytical structure of the company, as well as in the selection and design of a
relevant BI system. However, during the implementation of the project, it was necessary to
optimize the design of the infrastructure in the most efficient way so that it meets the needs of
the company and uses a limited amount of resources. The materials considered during the
implementation and described in this section made it possible to take into account the subtleties
of the design of BI infrastructures that were not obvious during preliminary planning. The study
of these sources will help create the most effective structure and avoid possible difficulties with
its operation.
Changes to the Project Environment
Before the implementation of the project, the analytical infrastructure of Textile Company A
was extremely outdated and required an upgrade in accordance with modern technical
standards. The company used manual data analysis of the companies listed in the databases,
which was carried out by the analytical department. After that, based on the analytics, reports
were compiled for the company as a whole without taking into account the needs of individual
departments. This structure was slow and inflexible and could also contain a large number of
errors and irrelevant information. This situation made it impossible to effectively optimize the
company’s operations, as well as plan for expansion.
The BI infrastructure implementation project has significantly changed the company’s
decision-making process based on the received analytical data. Most importantly, data analytics
has become as automated as possible, which has increased the efficiency of using the
information in the company at times. In particular, the ETL tool made it possible to filter out
irrelevant data, which minimized the number of errors. The inclusion of March data in the BI
infrastructure has allowed departments to receive personalized analytical reports based on their
needs. Additionally, the use of OPLA cubes has made the structure extremely stable in the event
of a future increase in the number of databases and an increase in the number of queries.
Overall, the implementation of the BI infrastructure has allowed Textile Company A to use
descriptive analytics to optimize its operations and predictive analytics to plan the company’s
expansion. It is also important that employees of various departments have access to
multidimensional information for better decision-making. Data analytics in the company has
become efficient, eliminating errors and providing the most relevant and important
information. This system can become the basis for the future development of the company and
a significant increase in its performance.
Methodology
ADDIE methodology model was chosen for the project execution as it presents the most
appropriate approach. This model includes five crucial steps: analysis, design, development,
implementation, and evaluation. The main advantage of this structure is the sequence of steps
and flexibility in the implementation of the project. In particular, ADDIE involves evaluation
after each of the major stages of project execution, which allows you to adjust its development
depending on the emerging conditions.
The analysis stage includes goal-setting and the resources available for the project. This step
also identifies the problem the business has and offers a set of potential solutions that could
address it. At the design stage, the specific tools that will be used within the project are
determined, deliverables are developed, as well as project evaluation criteria. Those two steps
have already been completed under this proposal and are described in the project overview
sections, which contain a description of the problem and the relevant IT solution.
The development stage started with the assignment of the analytical team, which will work
with ready-made BI. This step also includes the choice of the warehouse, the analytical
architecture of the system, and the end-user interface, as well as setting data integration tools.
The implementation of the project will include the creation of a fully functional BI structure
for Textile Company A and the completion of the onboarding of the analytical team. The final
stage of the project is evaluation, which requires tests to create informational reports for various
departments.
Project Goals and Objectives
Goal
Supporting objectives
Deliverables enabling the project
objectives
Met/Unmet
1. BI infrastructure features
assessment and planning
1.a. BI implementation
strategy development
1.a.i. Data sources and analytical tools
review
Met
1.a.ii. Setting objectives
Met
1.a.iii. Tools assessment
Met
1.b. Defining key BI
characteristics
1.b.i. BI team assignment
Met
1.b.ii. Define KPIs
Met
2. BI infrastructure design and
implementation
2.a. Setting data integration
tools
2.a.i. Setting data warehouse
Met
2.a.ii. Setting data integration tools or
ETL (Extract, Transform, Load)
Met
Goal
Supporting objectives
Deliverables enabling the project
objectives
Met/Unmet
2.b. Choosing an approach for
architecture design
2.b.i. Data warehouse connection
Met
2.b.ii. Setting online analytical
processing (OLAP)
Met
2.b.iii. Setting data marts
Met
2.c. The end-user interface
implementation
2.c.i. Setting ad hoc reporting systems
Met
2.c.ii. Conduct end-user training
Met
Goal 1
BI infrastructure features assessment and planning. At this stage, it is necessary to evaluate the
existing resources, infrastructure, goals, and responsible persons, as well as a potential vendor
for the implementation of BI. This goal has been met when all key aspects of the BI solution
are selected and clearly defined based on available data and based on the objectives of the
organization. This goal included two objectives:
Objective a
BI implementation strategy development. This objective includes activities to evaluate the
company’s current resources and infrastructures and appropriate changes that have to be made
in order to achieve the company’s goals. The objective was considered successful when all the
decisions regarding BI infrastructure design were made, including the choice of relevant and
available tools.
Objective b
Defining key BI characteristics. This objective allows the project to scale the required BI
structure and exclude features that the company does not need. At this stage, it is important to
assess the competencies available to the organization, as well as the KPIs that are key to
monitoring. The objective was considered successful when the complete diagram of the BI
infrastructure was created.
Goal 2
BI infrastructure design and implementation. This goal is key within the framework of this
project, as it involves the creation of a functioning BI infrastructure as a result. This objective
is supported by three objectives, which include setting data integration tools, BI architecture
design, and end-user interface implementation. These objectives describe the key aspects of
the software architecture BI solution and constitute a fully functional structure.
Objective a
Setting data integration tools. For the correct operation of BI, tools for collecting, processing,
and storing data are required, which requires the choice of data warehouse and data integration
tools. At the moment, cloud data storages are the most common, which is especially important
for SMEs. The objective was considered successful when the data integration tool was installed
and connected to the company’s databases.
Objective b
Choosing an approach for architecture design. At this stage, it is necessary to decide, based on
the needs and resources of the company, which BI architecture is the most relevant. In this
situation, the company plans to expand, so it would be logical to use a hybrid setup (warehouse
+ OLAP + data marts) to increase the potential duration of the BI solution. The objective was
considered successful when the data warehouse, OLAPs, and data marts were installed and
connected to the ETL tool.
Objective c
The end-user interface implementation. At this stage, it is important to choose a tool that will
allow the company to effectively represent the collected data in the form of analytical reports
for employees. The objective was considered successful when the end-user interface was
installed and connected to the BI structure.
Project Timeline
Milestone or deliverable
Planned
duration
(hours or
days)
Actual
duration
(hours or
days)
Projected
start date
Anticipated
end date
Actual start
date
Anticipated
start date
Project goals and objectives
discussion meeting
1 day
1 day
9.18.2024
9.18.2024
9.18.2024
9.18.2024
Determine existing data
analytics structure
2 days
2 days
9.19.2024
9.20.2024
9.19.2024
9.20.2024
Identify the problems existing
in the analytics structure
2 days
2 days
9.21.2024
9.22.2024
9.21.2024
9.22.2024
Identify adjustments needed
based on company’s goals and
resources
2 days
2 days
9.23.2024
9.24.2024
9.23.2024
9.24.2024
BI team assignment and
training
5 days
5 days
9.24.2024
9.28.2024
9.24.2024
9.28.2024
BI infrastructure design with
identification of appropriate
tools
5 days
5 days
9.29.2024
10.3.2024
9.29.2024
10.3.2024
BI infrastructure setting
10 days
12 days
10.4.2024
10.13.2024
10.04.2024
10.15.2024
BI infrastructure testing
2 days
2 days
10.14.2024
10.15.2024
10.16.2024
10.17.2024
End-users training
5 days
5 days
10.16.2024
10.20.2024
10.17.2024
10.21.2024
The main stages of the project implementation took place according to the schedule, as the
project team worked well-coordinated and according to a well-thought-out preliminary plan.
However, the delay occurred during the BI infrastructure setting phase, which was scheduled
to be completed in 10 days. Instead, the process took 12 days, as there were difficulties
connecting the company’s databases to the ETL tool, which required additional research and
planning. Difficulties arose due to the incompatibility of existing databases with the selected
ETL tool. After reviewing the materials described in the review of the work section, it was
decided to switch to Pentaho ETL solution, which required additional settings. Thus, the project
implementation ended on 10.22.2024 instead of 10.20.2024 with a delay of less than 2 days.
Unanticipated Requirements
An unanticipated requirement that arose during the implementation of the project was the need
to choose a more relevant ETL tool. The problem arose during setting up the BI infrastructure
and testing its operation. Initially, it was planned to use the Apache Spark tool as an ETL since
it is free and easy to implement. However, later it was discovered that this Apache Spark does
not correctly interact with various file formats, including PDF, which are widely used in
company databases. This problem led to incorrect data filtering, which drastically reduced the
effectiveness of the tool. Thus, it was decided to choose a more suitable instrument, which
resulted in the need for additional research and a slight delay. As a result, it was decided to use
Pentaho because this structure is able to work with a variety of files, and it is also widely
considered in the literature and is popular in case studies. The main reason for choosing this
solution was the widespread use of Pentaho among SMEs, which is most relevant for this
project.
Conclusions
The main result of the project implementation is the creation of a fully functional BI
infrastructure in Textile Company A. The potential effect of the project is the ability to
effectively use descriptive and predictive analytics in the organization to optimize current
operations and plan for expansion. Among the immediately observed effects, one can single
out the complete automation of the process of storing, analyzing data, and generating
information reports. Most importantly, the ETL tool integrated into the infrastructure allows
the company to minimize the amount of irrelevant information through the sorting of data in
the databases used. The project is considered successful because, as a result of the
implementation of the BI infrastructure, it became possible to generate multidimensional
informational reports that various departments of the company can use for a more efficient
decision-making process.
Project Deliverables
Figure 1 shows a diagram of the data analytics structure that existed at Textile Company A
before the project was implemented. Previously, the company used manual data analysis and
the generation of information reports, which were handled by employees of the analytical
department, which resulted in low efficiency of data analysis. Figure 2 illustrates the BI
infrastructure diagram that was created at Textile Company A as part of the project
implementation. This scheme describes all the key elements of the system and their interaction.
Based on this illustration, it can be judged that the project made it possible to automate the
process of data analytics in the company in order to increase the efficiency of the decision-
making process based on reports. Finally, Figure 3 contains an example of a multidimensional
report that is generated by the BI infrastructure created within the project. This illustration is
the most important deliverable of the project, as it shows the actual result of its implementation.
Figure 3: The Example of Multidimensional Report Generated by BI Infrastructure
Visualization Tool.
Textile Company A is a small business in North Carolina that is currently struggling due to the
changing global environment. The organization is engaged in the purchase of textiles in
Europe, the Middle and Far East, as well as their distribution in the United States. The
complication of global supply chains leads to a constant increase in the cost of purchasing
textiles, which negatively affects the company’s profits. To overcome this crisis, Textile
Company A plans to expand its operations to South America and Canada. In this regard, the
company needs to upgrade its information infrastructure for better data analytics within the
company.
Prior to the implementation of the project to create a business intelligence (BI) system, the
organization used an outdated approach to storing and analyzing data. It was minimally
automated and did not allow working effectively with a large amount of data. In particular, the
company used several separate data sources (CRM, ERP, individual databases, and external
sources) and generated individual reports based on the data received from each of them. This
approach made holistic data analytics and information-driven decision-making more difficult.
A diagram illustrating the old data analytics system at Textile Company A is presented in Figure
1.
Figure 1: Data Analytics System in Textile Company A before the Project Completion.
As part of the project, it was possible to create a new BI infrastructure in the organization,
which fully automated the company’s interaction with data. It was possible to create a structure
consisting of ETL tools (Extract, Transform, Load), a data warehouse, online analytical
processing (OLAP), and data marts. A detailed scheme of the BI infrastructure created for
Textile Company A is illustrated in Figure 2. As a result, this infrastructure uses all the
company’s data sources for analytics and generates the most informative reports for different
organization reports. Additionally, BI helps in structuring data and filtering out unnecessary
information. Textile Company A can now use the framework for descriptive and predictive
analytics. This allows the company to streamline existing processes and make better decisions
for future expansion planning.
Figure 2: BI Infrastructure for Data Analytics in Textile Company A after the Project
Completion.
The project started with an evaluation of the data analytics structure that existed in the
company. After assessing the available resources and the needs of the organization, decisions
were made regarding suitable storage solutions as well as BI tools. Further, the BI infrastructure
was planned, and the steps for its implementation into the existing information structure of the
company took into account all the necessary changes. After that, BI was implemented and
integrated into the analytics structure of Textile Company A. At the last stage, tests were carried
out to evaluate performance, and employees were trained to work with the end-user interface.
Review of Other Work
The materials considered in the project proposal made it possible to effectively plan the
development and implementation of the BI solution. However, during the implementation of
the project, additional research was required to identify points for improving the existing
design. The paper by Dieni et al. (2021) describes the process of creating a BI infrastructure
for unemployment rate analysis and management for the Central Bureau of Statistics of
Indonesia. Although this article does not belong to the scope of SMEs, it provides a detailed
description of all stages of the process of developing and implementing a BI infrastructure.
Dieni et al. (2021) pay great attention to the development stage of the ETL tool, which, as
emphasized, is the most significant for the correct operation of the BI structure. Based on the
analysis presented in the article, it was decided to use the Pentaho BI solution as the main ETL
tool, as well as a resource for the cloud data warehouse. This approach can both reduce the cost
of creating a BI infrastructure and provide a company with additional options for the future
expansion of an information-analytical system.
During the implementation of the project, the possible use of alternative solutions to create the
most balanced infrastructure was also considered. At the planning stage, it was decided to use
a hybrid approach to the data warehouse: both cloud-based and physical storage. Garani et al.
(2019) analyzed the case of implementing the BI solution in a telecommunication company.
They focus on the design of a data warehouse that would be effective for all the business tasks
required by the organization. As part of the research, Garani et al. (2019) propose the scheme
of the data warehouse containing five dimensions of data sets divided into different categories
(customers, calls, contracts, employment, and product). Most importantly, they explain how
each of those data sets is related to OLAP structures, which are used in the current project as
well. This study showed how to structure a data warehouse information system in the most
efficient way in order for it to work best with data OLAP. Additionally, the article confirmed
the need to use cloud-based storage for interaction with OLAP in conjunction with a physical
warehouse.
On the whole, OLAP and data warehouse design and implementation are the most challenging
parts of the project. At the stage of developing these parts of the infrastructure, many questions
arose regarding how best to organize these elements relative to each other. Al-Aqrabi et al.
(2019) present a case study on the integration of OLAP into the BI infrastructure. Most valuable
is that in this article, the researchers offer a detailed description of the OLAP framework and
describe the elements that interact most effectively with various types of data warehouse
solutions. The use of cloud-based BI data storage when integrating OLAP requires “compliance
of the BI application with web services architectural standards (and the standards defined by
the SaaS or PaaS provider, like Google Apps standards)” (Al-Aqrabi et al., 2019, p. 81). This
information is extremely important for the implementation of the project, as it allows you to
take into account the compatibility of infrastructure elements.
Thus, the literature reviewed in the project proposal helped in the analysis and evaluation of
the existing analytical structure of the company, as well as in the selection and design of a
relevant BI system. However, during the implementation of the project, it was necessary to
optimize the design of the infrastructure in the most efficient way so that it meets the needs of
the company and uses a limited amount of resources. The materials considered during the
implementation and described in this section made it possible to take into account the subtleties
of the design of BI infrastructures that were not obvious during preliminary planning. The study
of these sources will help create the most effective structure and avoid possible difficulties with
its operation.
Changes to the Project Environment
Before the implementation of the project, the analytical infrastructure of Textile Company A
was extremely outdated and required an upgrade in accordance with modern technical
standards. The company used manual data analysis of the companies listed in the databases,
which was carried out by the analytical department. After that, based on the analytics, reports
were compiled for the company as a whole without taking into account the needs of individual
departments. This structure was slow and inflexible and could also contain a large number of
errors and irrelevant information. This situation made it impossible to effectively optimize the
company’s operations, as well as plan for expansion.
The BI infrastructure implementation project has significantly changed the company’s
decision-making process based on the received analytical data. Most importantly, data analytics
has become as automated as possible, which has increased the efficiency of using the
information in the company at times. In particular, the ETL tool made it possible to filter out
irrelevant data, which minimized the number of errors. The inclusion of March data in the BI
infrastructure has allowed departments to receive personalized analytical reports based on their
needs. Additionally, the use of OPLA cubes has made the structure extremely stable in the event
of a future increase in the number of databases and an increase in the number of queries.
Overall, the implementation of the BI infrastructure has allowed Textile Company A to use
descriptive analytics to optimize its operations and predictive analytics to plan the company’s
expansion. It is also important that employees of various departments have access to
multidimensional information for better decision-making. Data analytics in the company has
become efficient, eliminating errors and providing the most relevant and important
information. This system can become the basis for the future development of the company and
a significant increase in its performance.
Methodology
ADDIE methodology model was chosen for the project execution as it presents the most
appropriate approach. This model includes five crucial steps: analysis, design, development,
implementation, and evaluation. The main advantage of this structure is the sequence of steps
and flexibility in the implementation of the project. In particular, ADDIE involves evaluation
after each of the major stages of project execution, which allows you to adjust its development
depending on the emerging conditions.
The analysis stage includes goal-setting and the resources available for the project. This step
also identifies the problem the business has and offers a set of potential solutions that could
address it. At the design stage, the specific tools that will be used within the project are
determined, deliverables are developed, as well as project evaluation criteria. Those two steps
have already been completed under this proposal and are described in the project overview
sections, which contain a description of the problem and the relevant IT solution.
The development stage started with the assignment of the analytical team, which will work
with ready-made BI. This step also includes the choice of the warehouse, the analytical
architecture of the system, and the end-user interface, as well as setting data integration tools.
The implementation of the project will include the creation of a fully functional BI structure
for Textile Company A and the completion of the onboarding of the analytical team. The final
stage of the project is evaluation, which requires tests to create informational reports for various
departments.
Project Goals and Objectives
Goal
Supporting objectives
Deliverables enabling the project
objectives
Met/Unmet
1. BI infrastructure features
assessment and planning
1.a. BI implementation
strategy development
1.a.i. Data sources and analytical tools
review
Met
1.a.ii. Setting objectives
Met
1.a.iii. Tools assessment
Met
1.b. Defining key BI
characteristics
1.b.i. BI team assignment
Met
1.b.ii. Define KPIs
Met
2. BI infrastructure design and
implementation
2.a. Setting data integration
tools
2.a.i. Setting data warehouse
Met
2.a.ii. Setting data integration tools or
ETL (Extract, Transform, Load)
Met
Goal
Supporting objectives
Deliverables enabling the project
objectives
Met/Unmet
2.b. Choosing an approach for
architecture design
2.b.i. Data warehouse connection
Met
2.b.ii. Setting online analytical
processing (OLAP)
Met
2.b.iii. Setting data marts
Met
2.c. The end-user interface
implementation
2.c.i. Setting ad hoc reporting systems
Met
2.c.ii. Conduct end-user training
Met
Goal 1
BI infrastructure features assessment and planning. At this stage, it is necessary to evaluate the
existing resources, infrastructure, goals, and responsible persons, as well as a potential vendor
for the implementation of BI. This goal has been met when all key aspects of the BI solution
are selected and clearly defined based on available data and based on the objectives of the
organization. This goal included two objectives:
Objective a
BI implementation strategy development. This objective includes activities to evaluate the
company’s current resources and infrastructures and appropriate changes that have to be made
in order to achieve the company’s goals. The objective was considered successful when all the
decisions regarding BI infrastructure design were made, including the choice of relevant and
available tools.
Objective b
Defining key BI characteristics. This objective allows the project to scale the required BI
structure and exclude features that the company does not need. At this stage, it is important to
assess the competencies available to the organization, as well as the KPIs that are key to
monitoring. The objective was considered successful when the complete diagram of the BI
infrastructure was created.
Goal 2
BI infrastructure design and implementation. This goal is key within the framework of this
project, as it involves the creation of a functioning BI infrastructure as a result. This objective
is supported by three objectives, which include setting data integration tools, BI architecture
design, and end-user interface implementation. These objectives describe the key aspects of
the software architecture BI solution and constitute a fully functional structure.
Objective a
Setting data integration tools. For the correct operation of BI, tools for collecting, processing,
and storing data are required, which requires the choice of data warehouse and data integration
tools. At the moment, cloud data storages are the most common, which is especially important
for SMEs. The objective was considered successful when the data integration tool was installed
and connected to the company’s databases.
Objective b
Choosing an approach for architecture design. At this stage, it is necessary to decide, based on
the needs and resources of the company, which BI architecture is the most relevant. In this
situation, the company plans to expand, so it would be logical to use a hybrid setup (warehouse
+ OLAP + data marts) to increase the potential duration of the BI solution. The objective was
considered successful when the data warehouse, OLAPs, and data marts were installed and
connected to the ETL tool.
Objective c
The end-user interface implementation. At this stage, it is important to choose a tool that will
allow the company to effectively represent the collected data in the form of analytical reports
for employees. The objective was considered successful when the end-user interface was
installed and connected to the BI structure.
Project Timeline
Milestone or deliverable
Planned
duration
(hours or
days)
Actual
duration
(hours or
days)
Projected
start date
Anticipated
end date
Actual start
date
Anticipated
start date
Project goals and objectives
discussion meeting
1 day
1 day
9.18.2024
9.18.2024
9.18.2024
9.18.2024
Determine existing data
analytics structure
2 days
2 days
9.19.2024
9.20.2024
9.19.2024
9.20.2024
Identify the problems existing
in the analytics structure
2 days
2 days
9.21.2024
9.22.2024
9.21.2024
9.22.2024
Identify adjustments needed
based on company’s goals and
resources
2 days
2 days
9.23.2024
9.24.2024
9.23.2024
9.24.2024
BI team assignment and
training
5 days
5 days
9.24.2024
9.28.2024
9.24.2024
9.28.2024
BI infrastructure design with
identification of appropriate
tools
5 days
5 days
9.29.2024
10.3.2024
9.29.2024
10.3.2024
BI infrastructure setting
10 days
12 days
10.4.2024
10.13.2024
10.04.2024
10.15.2024
BI infrastructure testing
2 days
2 days
10.14.2024
10.15.2024
10.16.2024
10.17.2024
End-users training
5 days
5 days
10.16.2024
10.20.2024
10.17.2024
10.21.2024
The main stages of the project implementation took place according to the schedule, as the
project team worked well-coordinated and according to a well-thought-out preliminary plan.
However, the delay occurred during the BI infrastructure setting phase, which was scheduled
to be completed in 10 days. Instead, the process took 12 days, as there were difficulties
connecting the company’s databases to the ETL tool, which required additional research and
planning. Difficulties arose due to the incompatibility of existing databases with the selected
ETL tool. After reviewing the materials described in the review of the work section, it was
decided to switch to Pentaho ETL solution, which required additional settings. Thus, the project
implementation ended on 10.22.2024 instead of 10.20.2024 with a delay of less than 2 days.
Unanticipated Requirements
An unanticipated requirement that arose during the implementation of the project was the need
to choose a more relevant ETL tool. The problem arose during setting up the BI infrastructure
and testing its operation. Initially, it was planned to use the Apache Spark tool as an ETL since
it is free and easy to implement. However, later it was discovered that this Apache Spark does
not correctly interact with various file formats, including PDF, which are widely used in
company databases. This problem led to incorrect data filtering, which drastically reduced the
effectiveness of the tool. Thus, it was decided to choose a more suitable instrument, which
resulted in the need for additional research and a slight delay. As a result, it was decided to use
Pentaho because this structure is able to work with a variety of files, and it is also widely
considered in the literature and is popular in case studies. The main reason for choosing this
solution was the widespread use of Pentaho among SMEs, which is most relevant for this
project.
Conclusions
The main result of the project implementation is the creation of a fully functional BI
infrastructure in Textile Company A. The potential effect of the project is the ability to
effectively use descriptive and predictive analytics in the organization to optimize current
operations and plan for expansion. Among the immediately observed effects, one can single
out the complete automation of the process of storing, analyzing data, and generating
information reports. Most importantly, the ETL tool integrated into the infrastructure allows
the company to minimize the amount of irrelevant information through the sorting of data in
the databases used. The project is considered successful because, as a result of the
implementation of the BI infrastructure, it became possible to generate multidimensional
informational reports that various departments of the company can use for a more efficient
decision-making process.
Project Deliverables
Figure 1 shows a diagram of the data analytics structure that existed at Textile Company A
before the project was implemented. Previously, the company used manual data analysis and
the generation of information reports, which were handled by employees of the analytical
department, which resulted in low efficiency of data analysis. Figure 2 illustrates the BI
infrastructure diagram that was created at Textile Company A as part of the project
implementation. This scheme describes all the key elements of the system and their interaction.
Based on this illustration, it can be judged that the project made it possible to automate the
process of data analytics in the company in order to increase the efficiency of the decision-
making process based on reports. Finally, Figure 3 contains an example of a multidimensional
report that is generated by the BI infrastructure created within the project. This illustration is
the most important deliverable of the project, as it shows the actual result of its implementation.
Figure 3: The Example of Multidimensional Report Generated by BI Infrastructure
Visualization Tool.
Textile Company A is a small business in North Carolina that is currently struggling due to the
changing global environment. The organization is engaged in the purchase of textiles in
Europe, the Middle and Far East, as well as their distribution in the United States. The
complication of global supply chains leads to a constant increase in the cost of purchasing
textiles, which negatively affects the company’s profits. To overcome this crisis, Textile
Company A plans to expand its operations to South America and Canada. In this regard, the
company needs to upgrade its information infrastructure for better data analytics within the
company.
Prior to the implementation of the project to create a business intelligence (BI) system, the
organization used an outdated approach to storing and analyzing data. It was minimally
automated and did not allow working effectively with a large amount of data. In particular, the
company used several separate data sources (CRM, ERP, individual databases, and external
sources) and generated individual reports based on the data received from each of them. This
approach made holistic data analytics and information-driven decision-making more difficult.
A diagram illustrating the old data analytics system at Textile Company A is presented in Figure
1.
Figure 1: Data Analytics System in Textile Company A before the Project Completion.
As part of the project, it was possible to create a new BI infrastructure in the organization,
which fully automated the company’s interaction with data. It was possible to create a structure
consisting of ETL tools (Extract, Transform, Load), a data warehouse, online analytical
processing (OLAP), and data marts. A detailed scheme of the BI infrastructure created for
Textile Company A is illustrated in Figure 2. As a result, this infrastructure uses all the
company’s data sources for analytics and generates the most informative reports for different
organization reports. Additionally, BI helps in structuring data and filtering out unnecessary
information. Textile Company A can now use the framework for descriptive and predictive
analytics. This allows the company to streamline existing processes and make better decisions
for future expansion planning.
Figure 2: BI Infrastructure for Data Analytics in Textile Company A after the Project
Completion.
The project started with an evaluation of the data analytics structure that existed in the
company. After assessing the available resources and the needs of the organization, decisions
were made regarding suitable storage solutions as well as BI tools. Further, the BI infrastructure
was planned, and the steps for its implementation into the existing information structure of the
company took into account all the necessary changes. After that, BI was implemented and
integrated into the analytics structure of Textile Company A. At the last stage, tests were carried
out to evaluate performance, and employees were trained to work with the end-user interface.
Review of Other Work
The materials considered in the project proposal made it possible to effectively plan the
development and implementation of the BI solution. However, during the implementation of
the project, additional research was required to identify points for improving the existing
design. The paper by Dieni et al. (2021) describes the process of creating a BI infrastructure
for unemployment rate analysis and management for the Central Bureau of Statistics of
Indonesia. Although this article does not belong to the scope of SMEs, it provides a detailed
description of all stages of the process of developing and implementing a BI infrastructure.
Dieni et al. (2021) pay great attention to the development stage of the ETL tool, which, as
emphasized, is the most significant for the correct operation of the BI structure. Based on the
analysis presented in the article, it was decided to use the Pentaho BI solution as the main ETL
tool, as well as a resource for the cloud data warehouse. This approach can both reduce the cost
of creating a BI infrastructure and provide a company with additional options for the future
expansion of an information-analytical system.
During the implementation of the project, the possible use of alternative solutions to create the
most balanced infrastructure was also considered. At the planning stage, it was decided to use
a hybrid approach to the data warehouse: both cloud-based and physical storage. Garani et al.
(2019) analyzed the case of implementing the BI solution in a telecommunication company.
They focus on the design of a data warehouse that would be effective for all the business tasks
required by the organization. As part of the research, Garani et al. (2019) propose the scheme
of the data warehouse containing five dimensions of data sets divided into different categories
(customers, calls, contracts, employment, and product). Most importantly, they explain how
each of those data sets is related to OLAP structures, which are used in the current project as
well. This study showed how to structure a data warehouse information system in the most
efficient way in order for it to work best with data OLAP. Additionally, the article confirmed
the need to use cloud-based storage for interaction with OLAP in conjunction with a physical
warehouse.
On the whole, OLAP and data warehouse design and implementation are the most challenging
parts of the project. At the stage of developing these parts of the infrastructure, many questions
arose regarding how best to organize these elements relative to each other. Al-Aqrabi et al.
(2019) present a case study on the integration of OLAP into the BI infrastructure. Most valuable
is that in this article, the researchers offer a detailed description of the OLAP framework and
describe the elements that interact most effectively with various types of data warehouse
solutions. The use of cloud-based BI data storage when integrating OLAP requires “compliance
of the BI application with web services architectural standards (and the standards defined by
the SaaS or PaaS provider, like Google Apps standards)” (Al-Aqrabi et al., 2019, p. 81). This
information is extremely important for the implementation of the project, as it allows you to
take into account the compatibility of infrastructure elements.
Thus, the literature reviewed in the project proposal helped in the analysis and evaluation of
the existing analytical structure of the company, as well as in the selection and design of a
relevant BI system. However, during the implementation of the project, it was necessary to
optimize the design of the infrastructure in the most efficient way so that it meets the needs of
the company and uses a limited amount of resources. The materials considered during the
implementation and described in this section made it possible to take into account the subtleties
of the design of BI infrastructures that were not obvious during preliminary planning. The study
of these sources will help create the most effective structure and avoid possible difficulties with
its operation.
Changes to the Project Environment
Before the implementation of the project, the analytical infrastructure of Textile Company A
was extremely outdated and required an upgrade in accordance with modern technical
standards. The company used manual data analysis of the companies listed in the databases,
which was carried out by the analytical department. After that, based on the analytics, reports
were compiled for the company as a whole without taking into account the needs of individual
departments. This structure was slow and inflexible and could also contain a large number of
errors and irrelevant information. This situation made it impossible to effectively optimize the
company’s operations, as well as plan for expansion.
The BI infrastructure implementation project has significantly changed the company’s
decision-making process based on the received analytical data. Most importantly, data analytics
has become as automated as possible, which has increased the efficiency of using the
information in the company at times. In particular, the ETL tool made it possible to filter out
irrelevant data, which minimized the number of errors. The inclusion of March data in the BI
infrastructure has allowed departments to receive personalized analytical reports based on their
needs. Additionally, the use of OPLA cubes has made the structure extremely stable in the event
of a future increase in the number of databases and an increase in the number of queries.
Overall, the implementation of the BI infrastructure has allowed Textile Company A to use
descriptive analytics to optimize its operations and predictive analytics to plan the company’s
expansion. It is also important that employees of various departments have access to
multidimensional information for better decision-making. Data analytics in the company has
become efficient, eliminating errors and providing the most relevant and important
information. This system can become the basis for the future development of the company and
a significant increase in its performance.
Methodology
ADDIE methodology model was chosen for the project execution as it presents the most
appropriate approach. This model includes five crucial steps: analysis, design, development,
implementation, and evaluation. The main advantage of this structure is the sequence of steps
and flexibility in the implementation of the project. In particular, ADDIE involves evaluation
after each of the major stages of project execution, which allows you to adjust its development
depending on the emerging conditions.
The analysis stage includes goal-setting and the resources available for the project. This step
also identifies the problem the business has and offers a set of potential solutions that could
address it. At the design stage, the specific tools that will be used within the project are
determined, deliverables are developed, as well as project evaluation criteria. Those two steps
have already been completed under this proposal and are described in the project overview
sections, which contain a description of the problem and the relevant IT solution.
The development stage started with the assignment of the analytical team, which will work
with ready-made BI. This step also includes the choice of the warehouse, the analytical
architecture of the system, and the end-user interface, as well as setting data integration tools.
The implementation of the project will include the creation of a fully functional BI structure
for Textile Company A and the completion of the onboarding of the analytical team. The final
stage of the project is evaluation, which requires tests to create informational reports for various
departments.
Project Goals and Objectives
Goal
Supporting objectives
Deliverables enabling the project
objectives
Met/Unmet
1. BI infrastructure features
assessment and planning
1.a. BI implementation
strategy development
1.a.i. Data sources and analytical tools
review
Met
1.a.ii. Setting objectives
Met
1.a.iii. Tools assessment
Met
1.b. Defining key BI
characteristics
1.b.i. BI team assignment
Met
1.b.ii. Define KPIs
Met
2. BI infrastructure design and
implementation
2.a. Setting data integration
tools
2.a.i. Setting data warehouse
Met
2.a.ii. Setting data integration tools or
ETL (Extract, Transform, Load)
Met
Goal
Supporting objectives
Deliverables enabling the project
objectives
Met/Unmet
2.b. Choosing an approach for
architecture design
2.b.i. Data warehouse connection
Met
2.b.ii. Setting online analytical
processing (OLAP)
Met
2.b.iii. Setting data marts
Met
2.c. The end-user interface
implementation
2.c.i. Setting ad hoc reporting systems
Met
2.c.ii. Conduct end-user training
Met
Goal 1
BI infrastructure features assessment and planning. At this stage, it is necessary to evaluate the
existing resources, infrastructure, goals, and responsible persons, as well as a potential vendor
for the implementation of BI. This goal has been met when all key aspects of the BI solution
are selected and clearly defined based on available data and based on the objectives of the
organization. This goal included two objectives:
Objective a
BI implementation strategy development. This objective includes activities to evaluate the
company’s current resources and infrastructures and appropriate changes that have to be made
in order to achieve the company’s goals. The objective was considered successful when all the
decisions regarding BI infrastructure design were made, including the choice of relevant and
available tools.
Objective b
Defining key BI characteristics. This objective allows the project to scale the required BI
structure and exclude features that the company does not need. At this stage, it is important to
assess the competencies available to the organization, as well as the KPIs that are key to
monitoring. The objective was considered successful when the complete diagram of the BI
infrastructure was created.
Goal 2
BI infrastructure design and implementation. This goal is key within the framework of this
project, as it involves the creation of a functioning BI infrastructure as a result. This objective
is supported by three objectives, which include setting data integration tools, BI architecture
design, and end-user interface implementation. These objectives describe the key aspects of
the software architecture BI solution and constitute a fully functional structure.
Objective a
Setting data integration tools. For the correct operation of BI, tools for collecting, processing,
and storing data are required, which requires the choice of data warehouse and data integration
tools. At the moment, cloud data storages are the most common, which is especially important
for SMEs. The objective was considered successful when the data integration tool was installed
and connected to the company’s databases.
Objective b
Choosing an approach for architecture design. At this stage, it is necessary to decide, based on
the needs and resources of the company, which BI architecture is the most relevant. In this
situation, the company plans to expand, so it would be logical to use a hybrid setup (warehouse
+ OLAP + data marts) to increase the potential duration of the BI solution. The objective was
considered successful when the data warehouse, OLAPs, and data marts were installed and
connected to the ETL tool.
Objective c
The end-user interface implementation. At this stage, it is important to choose a tool that will
allow the company to effectively represent the collected data in the form of analytical reports
for employees. The objective was considered successful when the end-user interface was
installed and connected to the BI structure.
Project Timeline
Milestone or deliverable
Planned
duration
(hours or
days)
Actual
duration
(hours or
days)
Projected
start date
Anticipated
end date
Actual start
date
Anticipated
start date
Project goals and objectives
discussion meeting
1 day
1 day
9.18.2024
9.18.2024
9.18.2024
9.18.2024
Determine existing data
analytics structure
2 days
2 days
9.19.2024
9.20.2024
9.19.2024
9.20.2024
Identify the problems existing
in the analytics structure
2 days
2 days
9.21.2024
9.22.2024
9.21.2024
9.22.2024
Identify adjustments needed
based on company’s goals and
resources
2 days
2 days
9.23.2024
9.24.2024
9.23.2024
9.24.2024
BI team assignment and
training
5 days
5 days
9.24.2024
9.28.2024
9.24.2024
9.28.2024
BI infrastructure design with
identification of appropriate
tools
5 days
5 days
9.29.2024
10.3.2024
9.29.2024
10.3.2024
BI infrastructure setting
10 days
12 days
10.4.2024
10.13.2024
10.04.2024
10.15.2024
BI infrastructure testing
2 days
2 days
10.14.2024
10.15.2024
10.16.2024
10.17.2024
End-users training
5 days
5 days
10.16.2024
10.20.2024
10.17.2024
10.21.2024
The main stages of the project implementation took place according to the schedule, as the
project team worked well-coordinated and according to a well-thought-out preliminary plan.
However, the delay occurred during the BI infrastructure setting phase, which was scheduled
to be completed in 10 days. Instead, the process took 12 days, as there were difficulties
connecting the company’s databases to the ETL tool, which required additional research and
planning. Difficulties arose due to the incompatibility of existing databases with the selected
ETL tool. After reviewing the materials described in the review of the work section, it was
decided to switch to Pentaho ETL solution, which required additional settings. Thus, the project
implementation ended on 10.22.2024 instead of 10.20.2024 with a delay of less than 2 days.
Unanticipated Requirements
An unanticipated requirement that arose during the implementation of the project was the need
to choose a more relevant ETL tool. The problem arose during setting up the BI infrastructure
and testing its operation. Initially, it was planned to use the Apache Spark tool as an ETL since
it is free and easy to implement. However, later it was discovered that this Apache Spark does
not correctly interact with various file formats, including PDF, which are widely used in
company databases. This problem led to incorrect data filtering, which drastically reduced the
effectiveness of the tool. Thus, it was decided to choose a more suitable instrument, which
resulted in the need for additional research and a slight delay. As a result, it was decided to use
Pentaho because this structure is able to work with a variety of files, and it is also widely
considered in the literature and is popular in case studies. The main reason for choosing this
solution was the widespread use of Pentaho among SMEs, which is most relevant for this
project.
Conclusions
The main result of the project implementation is the creation of a fully functional BI
infrastructure in Textile Company A. The potential effect of the project is the ability to
effectively use descriptive and predictive analytics in the organization to optimize current
operations and plan for expansion. Among the immediately observed effects, one can single
out the complete automation of the process of storing, analyzing data, and generating
information reports. Most importantly, the ETL tool integrated into the infrastructure allows
the company to minimize the amount of irrelevant information through the sorting of data in
the databases used. The project is considered successful because, as a result of the
implementation of the BI infrastructure, it became possible to generate multidimensional
informational reports that various departments of the company can use for a more efficient
decision-making process.
Project Deliverables
Figure 1 shows a diagram of the data analytics structure that existed at Textile Company A
before the project was implemented. Previously, the company used manual data analysis and
the generation of information reports, which were handled by employees of the analytical
department, which resulted in low efficiency of data analysis. Figure 2 illustrates the BI
infrastructure diagram that was created at Textile Company A as part of the project
implementation. This scheme describes all the key elements of the system and their interaction.
Based on this illustration, it can be judged that the project made it possible to automate the
process of data analytics in the company in order to increase the efficiency of the decision-
making process based on reports. Finally, Figure 3 contains an example of a multidimensional
report that is generated by the BI infrastructure created within the project. This illustration is
the most important deliverable of the project, as it shows the actual result of its implementation.
Figure 3: The Example of Multidimensional Report Generated by BI Infrastructure
Visualization Tool.
Textile Company A is a small business in North Carolina that is currently struggling due to the
changing global environment. The organization is engaged in the purchase of textiles in
Europe, the Middle and Far East, as well as their distribution in the United States. The
complication of global supply chains leads to a constant increase in the cost of purchasing
textiles, which negatively affects the company’s profits. To overcome this crisis, Textile
Company A plans to expand its operations to South America and Canada. In this regard, the
company needs to upgrade its information infrastructure for better data analytics within the
company.
Prior to the implementation of the project to create a business intelligence (BI) system, the
organization used an outdated approach to storing and analyzing data. It was minimally
automated and did not allow working effectively with a large amount of data. In particular, the
company used several separate data sources (CRM, ERP, individual databases, and external
sources) and generated individual reports based on the data received from each of them. This
approach made holistic data analytics and information-driven decision-making more difficult.
A diagram illustrating the old data analytics system at Textile Company A is presented in Figure
1.
Figure 1: Data Analytics System in Textile Company A before the Project Completion.
As part of the project, it was possible to create a new BI infrastructure in the organization,
which fully automated the company’s interaction with data. It was possible to create a structure
consisting of ETL tools (Extract, Transform, Load), a data warehouse, online analytical
processing (OLAP), and data marts. A detailed scheme of the BI infrastructure created for
Textile Company A is illustrated in Figure 2. As a result, this infrastructure uses all the
company’s data sources for analytics and generates the most informative reports for different
organization reports. Additionally, BI helps in structuring data and filtering out unnecessary
information. Textile Company A can now use the framework for descriptive and predictive
analytics. This allows the company to streamline existing processes and make better decisions
for future expansion planning.
Figure 2: BI Infrastructure for Data Analytics in Textile Company A after the Project
Completion.
The project started with an evaluation of the data analytics structure that existed in the
company. After assessing the available resources and the needs of the organization, decisions
were made regarding suitable storage solutions as well as BI tools. Further, the BI infrastructure
was planned, and the steps for its implementation into the existing information structure of the
company took into account all the necessary changes. After that, BI was implemented and
integrated into the analytics structure of Textile Company A. At the last stage, tests were carried
out to evaluate performance, and employees were trained to work with the end-user interface.
Review of Other Work
The materials considered in the project proposal made it possible to effectively plan the
development and implementation of the BI solution. However, during the implementation of
the project, additional research was required to identify points for improving the existing
design. The paper by Dieni et al. (2021) describes the process of creating a BI infrastructure
for unemployment rate analysis and management for the Central Bureau of Statistics of
Indonesia. Although this article does not belong to the scope of SMEs, it provides a detailed
description of all stages of the process of developing and implementing a BI infrastructure.
Dieni et al. (2021) pay great attention to the development stage of the ETL tool, which, as
emphasized, is the most significant for the correct operation of the BI structure. Based on the
analysis presented in the article, it was decided to use the Pentaho BI solution as the main ETL
tool, as well as a resource for the cloud data warehouse. This approach can both reduce the cost
of creating a BI infrastructure and provide a company with additional options for the future
expansion of an information-analytical system.
During the implementation of the project, the possible use of alternative solutions to create the
most balanced infrastructure was also considered. At the planning stage, it was decided to use
a hybrid approach to the data warehouse: both cloud-based and physical storage. Garani et al.
(2019) analyzed the case of implementing the BI solution in a telecommunication company.
They focus on the design of a data warehouse that would be effective for all the business tasks
required by the organization. As part of the research, Garani et al. (2019) propose the scheme
of the data warehouse containing five dimensions of data sets divided into different categories
(customers, calls, contracts, employment, and product). Most importantly, they explain how
each of those data sets is related to OLAP structures, which are used in the current project as
well. This study showed how to structure a data warehouse information system in the most
efficient way in order for it to work best with data OLAP. Additionally, the article confirmed
the need to use cloud-based storage for interaction with OLAP in conjunction with a physical
warehouse.
On the whole, OLAP and data warehouse design and implementation are the most challenging
parts of the project. At the stage of developing these parts of the infrastructure, many questions
arose regarding how best to organize these elements relative to each other. Al-Aqrabi et al.
(2019) present a case study on the integration of OLAP into the BI infrastructure. Most valuable
is that in this article, the researchers offer a detailed description of the OLAP framework and
describe the elements that interact most effectively with various types of data warehouse
solutions. The use of cloud-based BI data storage when integrating OLAP requires “compliance
of the BI application with web services architectural standards (and the standards defined by
the SaaS or PaaS provider, like Google Apps standards)” (Al-Aqrabi et al., 2019, p. 81). This
information is extremely important for the implementation of the project, as it allows you to
take into account the compatibility of infrastructure elements.
Thus, the literature reviewed in the project proposal helped in the analysis and evaluation of
the existing analytical structure of the company, as well as in the selection and design of a
relevant BI system. However, during the implementation of the project, it was necessary to
optimize the design of the infrastructure in the most efficient way so that it meets the needs of
the company and uses a limited amount of resources. The materials considered during the
implementation and described in this section made it possible to take into account the subtleties
of the design of BI infrastructures that were not obvious during preliminary planning. The study
of these sources will help create the most effective structure and avoid possible difficulties with
its operation.
Changes to the Project Environment
Before the implementation of the project, the analytical infrastructure of Textile Company A
was extremely outdated and required an upgrade in accordance with modern technical
standards. The company used manual data analysis of the companies listed in the databases,
which was carried out by the analytical department. After that, based on the analytics, reports
were compiled for the company as a whole without taking into account the needs of individual
departments. This structure was slow and inflexible and could also contain a large number of
errors and irrelevant information. This situation made it impossible to effectively optimize the
company’s operations, as well as plan for expansion.
The BI infrastructure implementation project has significantly changed the company’s
decision-making process based on the received analytical data. Most importantly, data analytics
has become as automated as possible, which has increased the efficiency of using the
information in the company at times. In particular, the ETL tool made it possible to filter out
irrelevant data, which minimized the number of errors. The inclusion of March data in the BI
infrastructure has allowed departments to receive personalized analytical reports based on their
needs. Additionally, the use of OPLA cubes has made the structure extremely stable in the event
of a future increase in the number of databases and an increase in the number of queries.
Overall, the implementation of the BI infrastructure has allowed Textile Company A to use
descriptive analytics to optimize its operations and predictive analytics to plan the company’s
expansion. It is also important that employees of various departments have access to
multidimensional information for better decision-making. Data analytics in the company has
become efficient, eliminating errors and providing the most relevant and important
information. This system can become the basis for the future development of the company and
a significant increase in its performance.
Methodology
ADDIE methodology model was chosen for the project execution as it presents the most
appropriate approach. This model includes five crucial steps: analysis, design, development,
implementation, and evaluation. The main advantage of this structure is the sequence of steps
and flexibility in the implementation of the project. In particular, ADDIE involves evaluation
after each of the major stages of project execution, which allows you to adjust its development
depending on the emerging conditions.
The analysis stage includes goal-setting and the resources available for the project. This step
also identifies the problem the business has and offers a set of potential solutions that could
address it. At the design stage, the specific tools that will be used within the project are
determined, deliverables are developed, as well as project evaluation criteria. Those two steps
have already been completed under this proposal and are described in the project overview
sections, which contain a description of the problem and the relevant IT solution.
The development stage started with the assignment of the analytical team, which will work
with ready-made BI. This step also includes the choice of the warehouse, the analytical
architecture of the system, and the end-user interface, as well as setting data integration tools.
The implementation of the project will include the creation of a fully functional BI structure
for Textile Company A and the completion of the onboarding of the analytical team. The final
stage of the project is evaluation, which requires tests to create informational reports for various
departments.
Project Goals and Objectives
Goal
Supporting objectives
Deliverables enabling the project
objectives
Met/Unmet
1. BI infrastructure features
assessment and planning
1.a. BI implementation
strategy development
1.a.i. Data sources and analytical tools
review
Met
1.a.ii. Setting objectives
Met
1.a.iii. Tools assessment
Met
1.b. Defining key BI
characteristics
1.b.i. BI team assignment
Met
1.b.ii. Define KPIs
Met
2. BI infrastructure design and
implementation
2.a. Setting data integration
tools
2.a.i. Setting data warehouse
Met
2.a.ii. Setting data integration tools or
ETL (Extract, Transform, Load)
Met
Goal
Supporting objectives
Deliverables enabling the project
objectives
Met/Unmet
2.b. Choosing an approach for
architecture design
2.b.i. Data warehouse connection
Met
2.b.ii. Setting online analytical
processing (OLAP)
Met
2.b.iii. Setting data marts
Met
2.c. The end-user interface
implementation
2.c.i. Setting ad hoc reporting systems
Met
2.c.ii. Conduct end-user training
Met
Goal 1
BI infrastructure features assessment and planning. At this stage, it is necessary to evaluate the
existing resources, infrastructure, goals, and responsible persons, as well as a potential vendor
for the implementation of BI. This goal has been met when all key aspects of the BI solution
are selected and clearly defined based on available data and based on the objectives of the
organization. This goal included two objectives:
Objective a
BI implementation strategy development. This objective includes activities to evaluate the
company’s current resources and infrastructures and appropriate changes that have to be made
in order to achieve the company’s goals. The objective was considered successful when all the
decisions regarding BI infrastructure design were made, including the choice of relevant and
available tools.
Objective b
Defining key BI characteristics. This objective allows the project to scale the required BI
structure and exclude features that the company does not need. At this stage, it is important to
assess the competencies available to the organization, as well as the KPIs that are key to
monitoring. The objective was considered successful when the complete diagram of the BI
infrastructure was created.
Goal 2
BI infrastructure design and implementation. This goal is key within the framework of this
project, as it involves the creation of a functioning BI infrastructure as a result. This objective
is supported by three objectives, which include setting data integration tools, BI architecture
design, and end-user interface implementation. These objectives describe the key aspects of
the software architecture BI solution and constitute a fully functional structure.
Objective a
Setting data integration tools. For the correct operation of BI, tools for collecting, processing,
and storing data are required, which requires the choice of data warehouse and data integration
tools. At the moment, cloud data storages are the most common, which is especially important
for SMEs. The objective was considered successful when the data integration tool was installed
and connected to the company’s databases.
Objective b
Choosing an approach for architecture design. At this stage, it is necessary to decide, based on
the needs and resources of the company, which BI architecture is the most relevant. In this
situation, the company plans to expand, so it would be logical to use a hybrid setup (warehouse
+ OLAP + data marts) to increase the potential duration of the BI solution. The objective was
considered successful when the data warehouse, OLAPs, and data marts were installed and
connected to the ETL tool.
Objective c
The end-user interface implementation. At this stage, it is important to choose a tool that will
allow the company to effectively represent the collected data in the form of analytical reports
for employees. The objective was considered successful when the end-user interface was
installed and connected to the BI structure.
Project Timeline
Milestone or deliverable
Planned
duration
(hours or
days)
Actual
duration
(hours or
days)
Projected
start date
Anticipated
end date
Actual start
date
Anticipated
start date
Project goals and objectives
discussion meeting
1 day
1 day
9.18.2024
9.18.2024
9.18.2024
9.18.2024
Determine existing data
analytics structure
2 days
2 days
9.19.2024
9.20.2024
9.19.2024
9.20.2024
Identify the problems existing
in the analytics structure
2 days
2 days
9.21.2024
9.22.2024
9.21.2024
9.22.2024
Identify adjustments needed
based on company’s goals and
resources
2 days
2 days
9.23.2024
9.24.2024
9.23.2024
9.24.2024
BI team assignment and
training
5 days
5 days
9.24.2024
9.28.2024
9.24.2024
9.28.2024
BI infrastructure design with
identification of appropriate
tools
5 days
5 days
9.29.2024
10.3.2024
9.29.2024
10.3.2024
BI infrastructure setting
10 days
12 days
10.4.2024
10.13.2024
10.04.2024
10.15.2024
BI infrastructure testing
2 days
2 days
10.14.2024
10.15.2024
10.16.2024
10.17.2024
End-users training
5 days
5 days
10.16.2024
10.20.2024
10.17.2024
10.21.2024
The main stages of the project implementation took place according to the schedule, as the
project team worked well-coordinated and according to a well-thought-out preliminary plan.
However, the delay occurred during the BI infrastructure setting phase, which was scheduled
to be completed in 10 days. Instead, the process took 12 days, as there were difficulties
connecting the company’s databases to the ETL tool, which required additional research and
planning. Difficulties arose due to the incompatibility of existing databases with the selected
ETL tool. After reviewing the materials described in the review of the work section, it was
decided to switch to Pentaho ETL solution, which required additional settings. Thus, the project
implementation ended on 10.22.2024 instead of 10.20.2024 with a delay of less than 2 days.
Unanticipated Requirements
An unanticipated requirement that arose during the implementation of the project was the need
to choose a more relevant ETL tool. The problem arose during setting up the BI infrastructure
and testing its operation. Initially, it was planned to use the Apache Spark tool as an ETL since
it is free and easy to implement. However, later it was discovered that this Apache Spark does
not correctly interact with various file formats, including PDF, which are widely used in
company databases. This problem led to incorrect data filtering, which drastically reduced the
effectiveness of the tool. Thus, it was decided to choose a more suitable instrument, which
resulted in the need for additional research and a slight delay. As a result, it was decided to use
Pentaho because this structure is able to work with a variety of files, and it is also widely
considered in the literature and is popular in case studies. The main reason for choosing this
solution was the widespread use of Pentaho among SMEs, which is most relevant for this
project.
Conclusions
The main result of the project implementation is the creation of a fully functional BI
infrastructure in Textile Company A. The potential effect of the project is the ability to
effectively use descriptive and predictive analytics in the organization to optimize current
operations and plan for expansion. Among the immediately observed effects, one can single
out the complete automation of the process of storing, analyzing data, and generating
information reports. Most importantly, the ETL tool integrated into the infrastructure allows
the company to minimize the amount of irrelevant information through the sorting of data in
the databases used. The project is considered successful because, as a result of the
implementation of the BI infrastructure, it became possible to generate multidimensional
informational reports that various departments of the company can use for a more efficient
decision-making process.
Project Deliverables
Figure 1 shows a diagram of the data analytics structure that existed at Textile Company A
before the project was implemented. Previously, the company used manual data analysis and
the generation of information reports, which were handled by employees of the analytical
department, which resulted in low efficiency of data analysis. Figure 2 illustrates the BI
infrastructure diagram that was created at Textile Company A as part of the project
implementation. This scheme describes all the key elements of the system and their interaction.
Based on this illustration, it can be judged that the project made it possible to automate the
process of data analytics in the company in order to increase the efficiency of the decision-
making process based on reports. Finally, Figure 3 contains an example of a multidimensional
report that is generated by the BI infrastructure created within the project. This illustration is
the most important deliverable of the project, as it shows the actual result of its implementation.
Figure 3: The Example of Multidimensional Report Generated by BI Infrastructure
Visualization Tool.
Textile Company A is a small business in North Carolina that is currently struggling due to the
changing global environment. The organization is engaged in the purchase of textiles in
Europe, the Middle and Far East, as well as their distribution in the United States. The
complication of global supply chains leads to a constant increase in the cost of purchasing
textiles, which negatively affects the company’s profits. To overcome this crisis, Textile
Company A plans to expand its operations to South America and Canada. In this regard, the
company needs to upgrade its information infrastructure for better data analytics within the
company.
Prior to the implementation of the project to create a business intelligence (BI) system, the
organization used an outdated approach to storing and analyzing data. It was minimally
automated and did not allow working effectively with a large amount of data. In particular, the
company used several separate data sources (CRM, ERP, individual databases, and external
sources) and generated individual reports based on the data received from each of them. This
approach made holistic data analytics and information-driven decision-making more difficult.
A diagram illustrating the old data analytics system at Textile Company A is presented in Figure
1.
Figure 1: Data Analytics System in Textile Company A before the Project Completion.
As part of the project, it was possible to create a new BI infrastructure in the organization,
which fully automated the company’s interaction with data. It was possible to create a structure
consisting of ETL tools (Extract, Transform, Load), a data warehouse, online analytical
processing (OLAP), and data marts. A detailed scheme of the BI infrastructure created for
Textile Company A is illustrated in Figure 2. As a result, this infrastructure uses all the
company’s data sources for analytics and generates the most informative reports for different
organization reports. Additionally, BI helps in structuring data and filtering out unnecessary
information. Textile Company A can now use the framework for descriptive and predictive
analytics. This allows the company to streamline existing processes and make better decisions
for future expansion planning.
Figure 2: BI Infrastructure for Data Analytics in Textile Company A after the Project
Completion.
The project started with an evaluation of the data analytics structure that existed in the
company. After assessing the available resources and the needs of the organization, decisions
were made regarding suitable storage solutions as well as BI tools. Further, the BI infrastructure
was planned, and the steps for its implementation into the existing information structure of the
company took into account all the necessary changes. After that, BI was implemented and
integrated into the analytics structure of Textile Company A. At the last stage, tests were carried
out to evaluate performance, and employees were trained to work with the end-user interface.
Review of Other Work
The materials considered in the project proposal made it possible to effectively plan the
development and implementation of the BI solution. However, during the implementation of
the project, additional research was required to identify points for improving the existing
design. The paper by Dieni et al. (2021) describes the process of creating a BI infrastructure
for unemployment rate analysis and management for the Central Bureau of Statistics of
Indonesia. Although this article does not belong to the scope of SMEs, it provides a detailed
description of all stages of the process of developing and implementing a BI infrastructure.
Dieni et al. (2021) pay great attention to the development stage of the ETL tool, which, as
emphasized, is the most significant for the correct operation of the BI structure. Based on the
analysis presented in the article, it was decided to use the Pentaho BI solution as the main ETL
tool, as well as a resource for the cloud data warehouse. This approach can both reduce the cost
of creating a BI infrastructure and provide a company with additional options for the future
expansion of an information-analytical system.
During the implementation of the project, the possible use of alternative solutions to create the
most balanced infrastructure was also considered. At the planning stage, it was decided to use
a hybrid approach to the data warehouse: both cloud-based and physical storage. Garani et al.
(2019) analyzed the case of implementing the BI solution in a telecommunication company.
They focus on the design of a data warehouse that would be effective for all the business tasks
required by the organization. As part of the research, Garani et al. (2019) propose the scheme
of the data warehouse containing five dimensions of data sets divided into different categories
(customers, calls, contracts, employment, and product). Most importantly, they explain how
each of those data sets is related to OLAP structures, which are used in the current project as
well. This study showed how to structure a data warehouse information system in the most
efficient way in order for it to work best with data OLAP. Additionally, the article confirmed
the need to use cloud-based storage for interaction with OLAP in conjunction with a physical
warehouse.
On the whole, OLAP and data warehouse design and implementation are the most challenging
parts of the project. At the stage of developing these parts of the infrastructure, many questions
arose regarding how best to organize these elements relative to each other. Al-Aqrabi et al.
(2019) present a case study on the integration of OLAP into the BI infrastructure. Most valuable
is that in this article, the researchers offer a detailed description of the OLAP framework and
describe the elements that interact most effectively with various types of data warehouse
solutions. The use of cloud-based BI data storage when integrating OLAP requires “compliance
of the BI application with web services architectural standards (and the standards defined by
the SaaS or PaaS provider, like Google Apps standards)” (Al-Aqrabi et al., 2019, p. 81). This
information is extremely important for the implementation of the project, as it allows you to
take into account the compatibility of infrastructure elements.
Thus, the literature reviewed in the project proposal helped in the analysis and evaluation of
the existing analytical structure of the company, as well as in the selection and design of a
relevant BI system. However, during the implementation of the project, it was necessary to
optimize the design of the infrastructure in the most efficient way so that it meets the needs of
the company and uses a limited amount of resources. The materials considered during the
implementation and described in this section made it possible to take into account the subtleties
of the design of BI infrastructures that were not obvious during preliminary planning. The study
of these sources will help create the most effective structure and avoid possible difficulties with
its operation.
Changes to the Project Environment
Before the implementation of the project, the analytical infrastructure of Textile Company A
was extremely outdated and required an upgrade in accordance with modern technical
standards. The company used manual data analysis of the companies listed in the databases,
which was carried out by the analytical department. After that, based on the analytics, reports
were compiled for the company as a whole without taking into account the needs of individual
departments. This structure was slow and inflexible and could also contain a large number of
errors and irrelevant information. This situation made it impossible to effectively optimize the
company’s operations, as well as plan for expansion.
The BI infrastructure implementation project has significantly changed the company’s
decision-making process based on the received analytical data. Most importantly, data analytics
has become as automated as possible, which has increased the efficiency of using the
information in the company at times. In particular, the ETL tool made it possible to filter out
irrelevant data, which minimized the number of errors. The inclusion of March data in the BI
infrastructure has allowed departments to receive personalized analytical reports based on their
needs. Additionally, the use of OPLA cubes has made the structure extremely stable in the event
of a future increase in the number of databases and an increase in the number of queries.
Overall, the implementation of the BI infrastructure has allowed Textile Company A to use
descriptive analytics to optimize its operations and predictive analytics to plan the company’s
expansion. It is also important that employees of various departments have access to
multidimensional information for better decision-making. Data analytics in the company has
become efficient, eliminating errors and providing the most relevant and important
information. This system can become the basis for the future development of the company and
a significant increase in its performance.
Methodology
ADDIE methodology model was chosen for the project execution as it presents the most
appropriate approach. This model includes five crucial steps: analysis, design, development,
implementation, and evaluation. The main advantage of this structure is the sequence of steps
and flexibility in the implementation of the project. In particular, ADDIE involves evaluation
after each of the major stages of project execution, which allows you to adjust its development
depending on the emerging conditions.
The analysis stage includes goal-setting and the resources available for the project. This step
also identifies the problem the business has and offers a set of potential solutions that could
address it. At the design stage, the specific tools that will be used within the project are
determined, deliverables are developed, as well as project evaluation criteria. Those two steps
have already been completed under this proposal and are described in the project overview
sections, which contain a description of the problem and the relevant IT solution.
The development stage started with the assignment of the analytical team, which will work
with ready-made BI. This step also includes the choice of the warehouse, the analytical
architecture of the system, and the end-user interface, as well as setting data integration tools.
The implementation of the project will include the creation of a fully functional BI structure
for Textile Company A and the completion of the onboarding of the analytical team. The final
stage of the project is evaluation, which requires tests to create informational reports for various
departments.
Project Goals and Objectives
Goal
Supporting objectives
Deliverables enabling the project
objectives
Met/Unmet
1. BI infrastructure features
assessment and planning
1.a. BI implementation
strategy development
1.a.i. Data sources and analytical tools
review
Met
1.a.ii. Setting objectives
Met
1.a.iii. Tools assessment
Met
1.b. Defining key BI
characteristics
1.b.i. BI team assignment
Met
1.b.ii. Define KPIs
Met
2. BI infrastructure design and
implementation
2.a. Setting data integration
tools
2.a.i. Setting data warehouse
Met
2.a.ii. Setting data integration tools or
ETL (Extract, Transform, Load)
Met
Goal
Supporting objectives
Deliverables enabling the project
objectives
Met/Unmet
2.b. Choosing an approach for
architecture design
2.b.i. Data warehouse connection
Met
2.b.ii. Setting online analytical
processing (OLAP)
Met
2.b.iii. Setting data marts
Met
2.c. The end-user interface
implementation
2.c.i. Setting ad hoc reporting systems
Met
2.c.ii. Conduct end-user training
Met
Goal 1
BI infrastructure features assessment and planning. At this stage, it is necessary to evaluate the
existing resources, infrastructure, goals, and responsible persons, as well as a potential vendor
for the implementation of BI. This goal has been met when all key aspects of the BI solution
are selected and clearly defined based on available data and based on the objectives of the
organization. This goal included two objectives:
Objective a
BI implementation strategy development. This objective includes activities to evaluate the
company’s current resources and infrastructures and appropriate changes that have to be made
in order to achieve the company’s goals. The objective was considered successful when all the
decisions regarding BI infrastructure design were made, including the choice of relevant and
available tools.
Objective b
Defining key BI characteristics. This objective allows the project to scale the required BI
structure and exclude features that the company does not need. At this stage, it is important to
assess the competencies available to the organization, as well as the KPIs that are key to
monitoring. The objective was considered successful when the complete diagram of the BI
infrastructure was created.
Goal 2
BI infrastructure design and implementation. This goal is key within the framework of this
project, as it involves the creation of a functioning BI infrastructure as a result. This objective
is supported by three objectives, which include setting data integration tools, BI architecture
design, and end-user interface implementation. These objectives describe the key aspects of
the software architecture BI solution and constitute a fully functional structure.
Objective a
Setting data integration tools. For the correct operation of BI, tools for collecting, processing,
and storing data are required, which requires the choice of data warehouse and data integration
tools. At the moment, cloud data storages are the most common, which is especially important
for SMEs. The objective was considered successful when the data integration tool was installed
and connected to the company’s databases.
Objective b
Choosing an approach for architecture design. At this stage, it is necessary to decide, based on
the needs and resources of the company, which BI architecture is the most relevant. In this
situation, the company plans to expand, so it would be logical to use a hybrid setup (warehouse
+ OLAP + data marts) to increase the potential duration of the BI solution. The objective was
considered successful when the data warehouse, OLAPs, and data marts were installed and
connected to the ETL tool.
Objective c
The end-user interface implementation. At this stage, it is important to choose a tool that will
allow the company to effectively represent the collected data in the form of analytical reports
for employees. The objective was considered successful when the end-user interface was
installed and connected to the BI structure.
Project Timeline
Milestone or deliverable
Planned
duration
(hours or
days)
Actual
duration
(hours or
days)
Projected
start date
Anticipated
end date
Actual start
date
Anticipated
start date
Project goals and objectives
discussion meeting
1 day
1 day
9.18.2024
9.18.2024
9.18.2024
9.18.2024
Determine existing data
analytics structure
2 days
2 days
9.19.2024
9.20.2024
9.19.2024
9.20.2024
Identify the problems existing
in the analytics structure
2 days
2 days
9.21.2024
9.22.2024
9.21.2024
9.22.2024
Identify adjustments needed
based on company’s goals and
resources
2 days
2 days
9.23.2024
9.24.2024
9.23.2024
9.24.2024
BI team assignment and
training
5 days
5 days
9.24.2024
9.28.2024
9.24.2024
9.28.2024
BI infrastructure design with
identification of appropriate
tools
5 days
5 days
9.29.2024
10.3.2024
9.29.2024
10.3.2024
BI infrastructure setting
10 days
12 days
10.4.2024
10.13.2024
10.04.2024
10.15.2024
BI infrastructure testing
2 days
2 days
10.14.2024
10.15.2024
10.16.2024
10.17.2024
End-users training
5 days
5 days
10.16.2024
10.20.2024
10.17.2024
10.21.2024
The main stages of the project implementation took place according to the schedule, as the
project team worked well-coordinated and according to a well-thought-out preliminary plan.
However, the delay occurred during the BI infrastructure setting phase, which was scheduled
to be completed in 10 days. Instead, the process took 12 days, as there were difficulties
connecting the company’s databases to the ETL tool, which required additional research and
planning. Difficulties arose due to the incompatibility of existing databases with the selected
ETL tool. After reviewing the materials described in the review of the work section, it was
decided to switch to Pentaho ETL solution, which required additional settings. Thus, the project
implementation ended on 10.22.2024 instead of 10.20.2024 with a delay of less than 2 days.
Unanticipated Requirements
An unanticipated requirement that arose during the implementation of the project was the need
to choose a more relevant ETL tool. The problem arose during setting up the BI infrastructure
and testing its operation. Initially, it was planned to use the Apache Spark tool as an ETL since
it is free and easy to implement. However, later it was discovered that this Apache Spark does
not correctly interact with various file formats, including PDF, which are widely used in
company databases. This problem led to incorrect data filtering, which drastically reduced the
effectiveness of the tool. Thus, it was decided to choose a more suitable instrument, which
resulted in the need for additional research and a slight delay. As a result, it was decided to use
Pentaho because this structure is able to work with a variety of files, and it is also widely
considered in the literature and is popular in case studies. The main reason for choosing this
solution was the widespread use of Pentaho among SMEs, which is most relevant for this
project.
Conclusions
The main result of the project implementation is the creation of a fully functional BI
infrastructure in Textile Company A. The potential effect of the project is the ability to
effectively use descriptive and predictive analytics in the organization to optimize current
operations and plan for expansion. Among the immediately observed effects, one can single
out the complete automation of the process of storing, analyzing data, and generating
information reports. Most importantly, the ETL tool integrated into the infrastructure allows
the company to minimize the amount of irrelevant information through the sorting of data in
the databases used. The project is considered successful because, as a result of the
implementation of the BI infrastructure, it became possible to generate multidimensional
informational reports that various departments of the company can use for a more efficient
decision-making process.
Project Deliverables
Figure 1 shows a diagram of the data analytics structure that existed at Textile Company A
before the project was implemented. Previously, the company used manual data analysis and
the generation of information reports, which were handled by employees of the analytical
department, which resulted in low efficiency of data analysis. Figure 2 illustrates the BI
infrastructure diagram that was created at Textile Company A as part of the project
implementation. This scheme describes all the key elements of the system and their interaction.
Based on this illustration, it can be judged that the project made it possible to automate the
process of data analytics in the company in order to increase the efficiency of the decision-
making process based on reports. Finally, Figure 3 contains an example of a multidimensional
report that is generated by the BI infrastructure created within the project. This illustration is
the most important deliverable of the project, as it shows the actual result of its implementation.
Figure 3: The Example of Multidimensional Report Generated by BI Infrastructure
Visualization Tool.
Textile Company A is a small business in North Carolina that is currently struggling due to the
changing global environment. The organization is engaged in the purchase of textiles in
Europe, the Middle and Far East, as well as their distribution in the United States. The
complication of global supply chains leads to a constant increase in the cost of purchasing
textiles, which negatively affects the company’s profits. To overcome this crisis, Textile
Company A plans to expand its operations to South America and Canada. In this regard, the
company needs to upgrade its information infrastructure for better data analytics within the
company.
Prior to the implementation of the project to create a business intelligence (BI) system, the
organization used an outdated approach to storing and analyzing data. It was minimally
automated and did not allow working effectively with a large amount of data. In particular, the
company used several separate data sources (CRM, ERP, individual databases, and external
sources) and generated individual reports based on the data received from each of them. This
approach made holistic data analytics and information-driven decision-making more difficult.
A diagram illustrating the old data analytics system at Textile Company A is presented in Figure
1.
Figure 1: Data Analytics System in Textile Company A before the Project Completion.
As part of the project, it was possible to create a new BI infrastructure in the organization,
which fully automated the company’s interaction with data. It was possible to create a structure
consisting of ETL tools (Extract, Transform, Load), a data warehouse, online analytical
processing (OLAP), and data marts. A detailed scheme of the BI infrastructure created for
Textile Company A is illustrated in Figure 2. As a result, this infrastructure uses all the
company’s data sources for analytics and generates the most informative reports for different
organization reports. Additionally, BI helps in structuring data and filtering out unnecessary
information. Textile Company A can now use the framework for descriptive and predictive
analytics. This allows the company to streamline existing processes and make better decisions
for future expansion planning.
Figure 2: BI Infrastructure for Data Analytics in Textile Company A after the Project
Completion.
The project started with an evaluation of the data analytics structure that existed in the
company. After assessing the available resources and the needs of the organization, decisions
were made regarding suitable storage solutions as well as BI tools. Further, the BI infrastructure
was planned, and the steps for its implementation into the existing information structure of the
company took into account all the necessary changes. After that, BI was implemented and
integrated into the analytics structure of Textile Company A. At the last stage, tests were carried
out to evaluate performance, and employees were trained to work with the end-user interface.
Review of Other Work
The materials considered in the project proposal made it possible to effectively plan the
development and implementation of the BI solution. However, during the implementation of
the project, additional research was required to identify points for improving the existing
design. The paper by Dieni et al. (2021) describes the process of creating a BI infrastructure
for unemployment rate analysis and management for the Central Bureau of Statistics of
Indonesia. Although this article does not belong to the scope of SMEs, it provides a detailed
description of all stages of the process of developing and implementing a BI infrastructure.
Dieni et al. (2021) pay great attention to the development stage of the ETL tool, which, as
emphasized, is the most significant for the correct operation of the BI structure. Based on the
analysis presented in the article, it was decided to use the Pentaho BI solution as the main ETL
tool, as well as a resource for the cloud data warehouse. This approach can both reduce the cost
of creating a BI infrastructure and provide a company with additional options for the future
expansion of an information-analytical system.
During the implementation of the project, the possible use of alternative solutions to create the
most balanced infrastructure was also considered. At the planning stage, it was decided to use
a hybrid approach to the data warehouse: both cloud-based and physical storage. Garani et al.
(2019) analyzed the case of implementing the BI solution in a telecommunication company.
They focus on the design of a data warehouse that would be effective for all the business tasks
required by the organization. As part of the research, Garani et al. (2019) propose the scheme
of the data warehouse containing five dimensions of data sets divided into different categories
(customers, calls, contracts, employment, and product). Most importantly, they explain how
each of those data sets is related to OLAP structures, which are used in the current project as
well. This study showed how to structure a data warehouse information system in the most
efficient way in order for it to work best with data OLAP. Additionally, the article confirmed
the need to use cloud-based storage for interaction with OLAP in conjunction with a physical
warehouse.
On the whole, OLAP and data warehouse design and implementation are the most challenging
parts of the project. At the stage of developing these parts of the infrastructure, many questions
arose regarding how best to organize these elements relative to each other. Al-Aqrabi et al.
(2019) present a case study on the integration of OLAP into the BI infrastructure. Most valuable
is that in this article, the researchers offer a detailed description of the OLAP framework and
describe the elements that interact most effectively with various types of data warehouse
solutions. The use of cloud-based BI data storage when integrating OLAP requires “compliance
of the BI application with web services architectural standards (and the standards defined by
the SaaS or PaaS provider, like Google Apps standards)” (Al-Aqrabi et al., 2019, p. 81). This
information is extremely important for the implementation of the project, as it allows you to
take into account the compatibility of infrastructure elements.
Thus, the literature reviewed in the project proposal helped in the analysis and evaluation of
the existing analytical structure of the company, as well as in the selection and design of a
relevant BI system. However, during the implementation of the project, it was necessary to
optimize the design of the infrastructure in the most efficient way so that it meets the needs of
the company and uses a limited amount of resources. The materials considered during the
implementation and described in this section made it possible to take into account the subtleties
of the design of BI infrastructures that were not obvious during preliminary planning. The study
of these sources will help create the most effective structure and avoid possible difficulties with
its operation.
Changes to the Project Environment
Before the implementation of the project, the analytical infrastructure of Textile Company A
was extremely outdated and required an upgrade in accordance with modern technical
standards. The company used manual data analysis of the companies listed in the databases,
which was carried out by the analytical department. After that, based on the analytics, reports
were compiled for the company as a whole without taking into account the needs of individual
departments. This structure was slow and inflexible and could also contain a large number of
errors and irrelevant information. This situation made it impossible to effectively optimize the
company’s operations, as well as plan for expansion.
The BI infrastructure implementation project has significantly changed the company’s
decision-making process based on the received analytical data. Most importantly, data analytics
has become as automated as possible, which has increased the efficiency of using the
information in the company at times. In particular, the ETL tool made it possible to filter out
irrelevant data, which minimized the number of errors. The inclusion of March data in the BI
infrastructure has allowed departments to receive personalized analytical reports based on their
needs. Additionally, the use of OPLA cubes has made the structure extremely stable in the event
of a future increase in the number of databases and an increase in the number of queries.
Overall, the implementation of the BI infrastructure has allowed Textile Company A to use
descriptive analytics to optimize its operations and predictive analytics to plan the company’s
expansion. It is also important that employees of various departments have access to
multidimensional information for better decision-making. Data analytics in the company has
become efficient, eliminating errors and providing the most relevant and important
information. This system can become the basis for the future development of the company and
a significant increase in its performance.
Methodology
ADDIE methodology model was chosen for the project execution as it presents the most
appropriate approach. This model includes five crucial steps: analysis, design, development,
implementation, and evaluation. The main advantage of this structure is the sequence of steps
and flexibility in the implementation of the project. In particular, ADDIE involves evaluation
after each of the major stages of project execution, which allows you to adjust its development
depending on the emerging conditions.
The analysis stage includes goal-setting and the resources available for the project. This step
also identifies the problem the business has and offers a set of potential solutions that could
address it. At the design stage, the specific tools that will be used within the project are
determined, deliverables are developed, as well as project evaluation criteria. Those two steps
have already been completed under this proposal and are described in the project overview
sections, which contain a description of the problem and the relevant IT solution.
The development stage started with the assignment of the analytical team, which will work
with ready-made BI. This step also includes the choice of the warehouse, the analytical
architecture of the system, and the end-user interface, as well as setting data integration tools.
The implementation of the project will include the creation of a fully functional BI structure
for Textile Company A and the completion of the onboarding of the analytical team. The final
stage of the project is evaluation, which requires tests to create informational reports for various
departments.
Project Goals and Objectives
Goal
Supporting objectives
Deliverables enabling the project
objectives
Met/Unmet
1. BI infrastructure features
assessment and planning
1.a. BI implementation
strategy development
1.a.i. Data sources and analytical tools
review
Met
1.a.ii. Setting objectives
Met
1.a.iii. Tools assessment
Met
1.b. Defining key BI
characteristics
1.b.i. BI team assignment
Met
1.b.ii. Define KPIs
Met
2. BI infrastructure design and
implementation
2.a. Setting data integration
tools
2.a.i. Setting data warehouse
Met
2.a.ii. Setting data integration tools or
ETL (Extract, Transform, Load)
Met
Goal
Supporting objectives
Deliverables enabling the project
objectives
Met/Unmet
2.b. Choosing an approach for
architecture design
2.b.i. Data warehouse connection
Met
2.b.ii. Setting online analytical
processing (OLAP)
Met
2.b.iii. Setting data marts
Met
2.c. The end-user interface
implementation
2.c.i. Setting ad hoc reporting systems
Met
2.c.ii. Conduct end-user training
Met
Goal 1
BI infrastructure features assessment and planning. At this stage, it is necessary to evaluate the
existing resources, infrastructure, goals, and responsible persons, as well as a potential vendor
for the implementation of BI. This goal has been met when all key aspects of the BI solution
are selected and clearly defined based on available data and based on the objectives of the
organization. This goal included two objectives:
Objective a
BI implementation strategy development. This objective includes activities to evaluate the
company’s current resources and infrastructures and appropriate changes that have to be made
in order to achieve the company’s goals. The objective was considered successful when all the
decisions regarding BI infrastructure design were made, including the choice of relevant and
available tools.
Objective b
Defining key BI characteristics. This objective allows the project to scale the required BI
structure and exclude features that the company does not need. At this stage, it is important to
assess the competencies available to the organization, as well as the KPIs that are key to
monitoring. The objective was considered successful when the complete diagram of the BI
infrastructure was created.
Goal 2
BI infrastructure design and implementation. This goal is key within the framework of this
project, as it involves the creation of a functioning BI infrastructure as a result. This objective
is supported by three objectives, which include setting data integration tools, BI architecture
design, and end-user interface implementation. These objectives describe the key aspects of
the software architecture BI solution and constitute a fully functional structure.
Objective a
Setting data integration tools. For the correct operation of BI, tools for collecting, processing,
and storing data are required, which requires the choice of data warehouse and data integration
tools. At the moment, cloud data storages are the most common, which is especially important
for SMEs. The objective was considered successful when the data integration tool was installed
and connected to the company’s databases.
Objective b
Choosing an approach for architecture design. At this stage, it is necessary to decide, based on
the needs and resources of the company, which BI architecture is the most relevant. In this
situation, the company plans to expand, so it would be logical to use a hybrid setup (warehouse
+ OLAP + data marts) to increase the potential duration of the BI solution. The objective was
considered successful when the data warehouse, OLAPs, and data marts were installed and
connected to the ETL tool.
Objective c
The end-user interface implementation. At this stage, it is important to choose a tool that will
allow the company to effectively represent the collected data in the form of analytical reports
for employees. The objective was considered successful when the end-user interface was
installed and connected to the BI structure.
Project Timeline
Milestone or deliverable
Planned
duration
(hours or
days)
Actual
duration
(hours or
days)
Projected
start date
Anticipated
end date
Actual start
date
Anticipated
start date
Project goals and objectives
discussion meeting
1 day
1 day
9.18.2024
9.18.2024
9.18.2024
9.18.2024
Determine existing data
analytics structure
2 days
2 days
9.19.2024
9.20.2024
9.19.2024
9.20.2024
Identify the problems existing
in the analytics structure
2 days
2 days
9.21.2024
9.22.2024
9.21.2024
9.22.2024
Identify adjustments needed
based on company’s goals and
resources
2 days
2 days
9.23.2024
9.24.2024
9.23.2024
9.24.2024
BI team assignment and
training
5 days
5 days
9.24.2024
9.28.2024
9.24.2024
9.28.2024
BI infrastructure design with
identification of appropriate
tools
5 days
5 days
9.29.2024
10.3.2024
9.29.2024
10.3.2024
BI infrastructure setting
10 days
12 days
10.4.2024
10.13.2024
10.04.2024
10.15.2024
BI infrastructure testing
2 days
2 days
10.14.2024
10.15.2024
10.16.2024
10.17.2024
End-users training
5 days
5 days
10.16.2024
10.20.2024
10.17.2024
10.21.2024
The main stages of the project implementation took place according to the schedule, as the
project team worked well-coordinated and according to a well-thought-out preliminary plan.
However, the delay occurred during the BI infrastructure setting phase, which was scheduled
to be completed in 10 days. Instead, the process took 12 days, as there were difficulties
connecting the company’s databases to the ETL tool, which required additional research and
planning. Difficulties arose due to the incompatibility of existing databases with the selected
ETL tool. After reviewing the materials described in the review of the work section, it was
decided to switch to Pentaho ETL solution, which required additional settings. Thus, the project
implementation ended on 10.22.2024 instead of 10.20.2024 with a delay of less than 2 days.
Unanticipated Requirements
An unanticipated requirement that arose during the implementation of the project was the need
to choose a more relevant ETL tool. The problem arose during setting up the BI infrastructure
and testing its operation. Initially, it was planned to use the Apache Spark tool as an ETL since
it is free and easy to implement. However, later it was discovered that this Apache Spark does
not correctly interact with various file formats, including PDF, which are widely used in
company databases. This problem led to incorrect data filtering, which drastically reduced the
effectiveness of the tool. Thus, it was decided to choose a more suitable instrument, which
resulted in the need for additional research and a slight delay. As a result, it was decided to use
Pentaho because this structure is able to work with a variety of files, and it is also widely
considered in the literature and is popular in case studies. The main reason for choosing this
solution was the widespread use of Pentaho among SMEs, which is most relevant for this
project.
Conclusions
The main result of the project implementation is the creation of a fully functional BI
infrastructure in Textile Company A. The potential effect of the project is the ability to
effectively use descriptive and predictive analytics in the organization to optimize current
operations and plan for expansion. Among the immediately observed effects, one can single
out the complete automation of the process of storing, analyzing data, and generating
information reports. Most importantly, the ETL tool integrated into the infrastructure allows
the company to minimize the amount of irrelevant information through the sorting of data in
the databases used. The project is considered successful because, as a result of the
implementation of the BI infrastructure, it became possible to generate multidimensional
informational reports that various departments of the company can use for a more efficient
decision-making process.
Project Deliverables
Figure 1 shows a diagram of the data analytics structure that existed at Textile Company A
before the project was implemented. Previously, the company used manual data analysis and
the generation of information reports, which were handled by employees of the analytical
department, which resulted in low efficiency of data analysis. Figure 2 illustrates the BI
infrastructure diagram that was created at Textile Company A as part of the project
implementation. This scheme describes all the key elements of the system and their interaction.
Based on this illustration, it can be judged that the project made it possible to automate the
process of data analytics in the company in order to increase the efficiency of the decision-
making process based on reports. Finally, Figure 3 contains an example of a multidimensional
report that is generated by the BI infrastructure created within the project. This illustration is
the most important deliverable of the project, as it shows the actual result of its implementation.
Figure 3: The Example of Multidimensional Report Generated by BI Infrastructure
Visualization Tool.
Textile Company A is a small business in North Carolina that is currently struggling due to the
changing global environment. The organization is engaged in the purchase of textiles in
Europe, the Middle and Far East, as well as their distribution in the United States. The
complication of global supply chains leads to a constant increase in the cost of purchasing
textiles, which negatively affects the company’s profits. To overcome this crisis, Textile
Company A plans to expand its operations to South America and Canada. In this regard, the
company needs to upgrade its information infrastructure for better data analytics within the
company.
Prior to the implementation of the project to create a business intelligence (BI) system, the
organization used an outdated approach to storing and analyzing data. It was minimally
automated and did not allow working effectively with a large amount of data. In particular, the
company used several separate data sources (CRM, ERP, individual databases, and external
sources) and generated individual reports based on the data received from each of them. This
approach made holistic data analytics and information-driven decision-making more difficult.
A diagram illustrating the old data analytics system at Textile Company A is presented in Figure
1.
Figure 1: Data Analytics System in Textile Company A before the Project Completion.
As part of the project, it was possible to create a new BI infrastructure in the organization,
which fully automated the company’s interaction with data. It was possible to create a structure
consisting of ETL tools (Extract, Transform, Load), a data warehouse, online analytical
processing (OLAP), and data marts. A detailed scheme of the BI infrastructure created for
Textile Company A is illustrated in Figure 2. As a result, this infrastructure uses all the
company’s data sources for analytics and generates the most informative reports for different
organization reports. Additionally, BI helps in structuring data and filtering out unnecessary
information. Textile Company A can now use the framework for descriptive and predictive
analytics. This allows the company to streamline existing processes and make better decisions
for future expansion planning.
Figure 2: BI Infrastructure for Data Analytics in Textile Company A after the Project
Completion.
The project started with an evaluation of the data analytics structure that existed in the
company. After assessing the available resources and the needs of the organization, decisions
were made regarding suitable storage solutions as well as BI tools. Further, the BI infrastructure
was planned, and the steps for its implementation into the existing information structure of the
company took into account all the necessary changes. After that, BI was implemented and
integrated into the analytics structure of Textile Company A. At the last stage, tests were carried
out to evaluate performance, and employees were trained to work with the end-user interface.
Review of Other Work
The materials considered in the project proposal made it possible to effectively plan the
development and implementation of the BI solution. However, during the implementation of
the project, additional research was required to identify points for improving the existing
design. The paper by Dieni et al. (2021) describes the process of creating a BI infrastructure
for unemployment rate analysis and management for the Central Bureau of Statistics of
Indonesia. Although this article does not belong to the scope of SMEs, it provides a detailed
description of all stages of the process of developing and implementing a BI infrastructure.
Dieni et al. (2021) pay great attention to the development stage of the ETL tool, which, as
emphasized, is the most significant for the correct operation of the BI structure. Based on the
analysis presented in the article, it was decided to use the Pentaho BI solution as the main ETL
tool, as well as a resource for the cloud data warehouse. This approach can both reduce the cost
of creating a BI infrastructure and provide a company with additional options for the future
expansion of an information-analytical system.
During the implementation of the project, the possible use of alternative solutions to create the
most balanced infrastructure was also considered. At the planning stage, it was decided to use
a hybrid approach to the data warehouse: both cloud-based and physical storage. Garani et al.
(2019) analyzed the case of implementing the BI solution in a telecommunication company.
They focus on the design of a data warehouse that would be effective for all the business tasks
required by the organization. As part of the research, Garani et al. (2019) propose the scheme
of the data warehouse containing five dimensions of data sets divided into different categories
(customers, calls, contracts, employment, and product). Most importantly, they explain how
each of those data sets is related to OLAP structures, which are used in the current project as
well. This study showed how to structure a data warehouse information system in the most
efficient way in order for it to work best with data OLAP. Additionally, the article confirmed
the need to use cloud-based storage for interaction with OLAP in conjunction with a physical
warehouse.
On the whole, OLAP and data warehouse design and implementation are the most challenging
parts of the project. At the stage of developing these parts of the infrastructure, many questions
arose regarding how best to organize these elements relative to each other. Al-Aqrabi et al.
(2019) present a case study on the integration of OLAP into the BI infrastructure. Most valuable
is that in this article, the researchers offer a detailed description of the OLAP framework and
describe the elements that interact most effectively with various types of data warehouse
solutions. The use of cloud-based BI data storage when integrating OLAP requires “compliance
of the BI application with web services architectural standards (and the standards defined by
the SaaS or PaaS provider, like Google Apps standards)” (Al-Aqrabi et al., 2019, p. 81). This
information is extremely important for the implementation of the project, as it allows you to
take into account the compatibility of infrastructure elements.
Thus, the literature reviewed in the project proposal helped in the analysis and evaluation of
the existing analytical structure of the company, as well as in the selection and design of a
relevant BI system. However, during the implementation of the project, it was necessary to
optimize the design of the infrastructure in the most efficient way so that it meets the needs of
the company and uses a limited amount of resources. The materials considered during the
implementation and described in this section made it possible to take into account the subtleties
of the design of BI infrastructures that were not obvious during preliminary planning. The study
of these sources will help create the most effective structure and avoid possible difficulties with
its operation.
Changes to the Project Environment
Before the implementation of the project, the analytical infrastructure of Textile Company A
was extremely outdated and required an upgrade in accordance with modern technical
standards. The company used manual data analysis of the companies listed in the databases,
which was carried out by the analytical department. After that, based on the analytics, reports
were compiled for the company as a whole without taking into account the needs of individual
departments. This structure was slow and inflexible and could also contain a large number of
errors and irrelevant information. This situation made it impossible to effectively optimize the
company’s operations, as well as plan for expansion.
The BI infrastructure implementation project has significantly changed the company’s
decision-making process based on the received analytical data. Most importantly, data analytics
has become as automated as possible, which has increased the efficiency of using the
information in the company at times. In particular, the ETL tool made it possible to filter out
irrelevant data, which minimized the number of errors. The inclusion of March data in the BI
infrastructure has allowed departments to receive personalized analytical reports based on their
needs. Additionally, the use of OPLA cubes has made the structure extremely stable in the event
of a future increase in the number of databases and an increase in the number of queries.
Overall, the implementation of the BI infrastructure has allowed Textile Company A to use
descriptive analytics to optimize its operations and predictive analytics to plan the company’s
expansion. It is also important that employees of various departments have access to
multidimensional information for better decision-making. Data analytics in the company has
become efficient, eliminating errors and providing the most relevant and important
information. This system can become the basis for the future development of the company and
a significant increase in its performance.
Methodology
ADDIE methodology model was chosen for the project execution as it presents the most
appropriate approach. This model includes five crucial steps: analysis, design, development,
implementation, and evaluation. The main advantage of this structure is the sequence of steps
and flexibility in the implementation of the project. In particular, ADDIE involves evaluation
after each of the major stages of project execution, which allows you to adjust its development
depending on the emerging conditions.
The analysis stage includes goal-setting and the resources available for the project. This step
also identifies the problem the business has and offers a set of potential solutions that could
address it. At the design stage, the specific tools that will be used within the project are
determined, deliverables are developed, as well as project evaluation criteria. Those two steps
have already been completed under this proposal and are described in the project overview
sections, which contain a description of the problem and the relevant IT solution.
The development stage started with the assignment of the analytical team, which will work
with ready-made BI. This step also includes the choice of the warehouse, the analytical
architecture of the system, and the end-user interface, as well as setting data integration tools.
The implementation of the project will include the creation of a fully functional BI structure
for Textile Company A and the completion of the onboarding of the analytical team. The final
stage of the project is evaluation, which requires tests to create informational reports for various
departments.
Project Goals and Objectives
Goal
Supporting objectives
Deliverables enabling the project
objectives
Met/Unmet
1. BI infrastructure features
assessment and planning
1.a. BI implementation
strategy development
1.a.i. Data sources and analytical tools
review
Met
1.a.ii. Setting objectives
Met
1.a.iii. Tools assessment
Met
1.b. Defining key BI
characteristics
1.b.i. BI team assignment
Met
1.b.ii. Define KPIs
Met
2. BI infrastructure design and
implementation
2.a. Setting data integration
tools
2.a.i. Setting data warehouse
Met
2.a.ii. Setting data integration tools or
ETL (Extract, Transform, Load)
Met
Goal
Supporting objectives
Deliverables enabling the project
objectives
Met/Unmet
2.b. Choosing an approach for
architecture design
2.b.i. Data warehouse connection
Met
2.b.ii. Setting online analytical
processing (OLAP)
Met
2.b.iii. Setting data marts
Met
2.c. The end-user interface
implementation
2.c.i. Setting ad hoc reporting systems
Met
2.c.ii. Conduct end-user training
Met
Goal 1
BI infrastructure features assessment and planning. At this stage, it is necessary to evaluate the
existing resources, infrastructure, goals, and responsible persons, as well as a potential vendor
for the implementation of BI. This goal has been met when all key aspects of the BI solution
are selected and clearly defined based on available data and based on the objectives of the
organization. This goal included two objectives:
Objective a
BI implementation strategy development. This objective includes activities to evaluate the
company’s current resources and infrastructures and appropriate changes that have to be made
in order to achieve the company’s goals. The objective was considered successful when all the
decisions regarding BI infrastructure design were made, including the choice of relevant and
available tools.
Objective b
Defining key BI characteristics. This objective allows the project to scale the required BI
structure and exclude features that the company does not need. At this stage, it is important to
assess the competencies available to the organization, as well as the KPIs that are key to
monitoring. The objective was considered successful when the complete diagram of the BI
infrastructure was created.
Goal 2
BI infrastructure design and implementation. This goal is key within the framework of this
project, as it involves the creation of a functioning BI infrastructure as a result. This objective
is supported by three objectives, which include setting data integration tools, BI architecture
design, and end-user interface implementation. These objectives describe the key aspects of
the software architecture BI solution and constitute a fully functional structure.
Objective a
Setting data integration tools. For the correct operation of BI, tools for collecting, processing,
and storing data are required, which requires the choice of data warehouse and data integration
tools. At the moment, cloud data storages are the most common, which is especially important
for SMEs. The objective was considered successful when the data integration tool was installed
and connected to the company’s databases.
Objective b
Choosing an approach for architecture design. At this stage, it is necessary to decide, based on
the needs and resources of the company, which BI architecture is the most relevant. In this
situation, the company plans to expand, so it would be logical to use a hybrid setup (warehouse
+ OLAP + data marts) to increase the potential duration of the BI solution. The objective was
considered successful when the data warehouse, OLAPs, and data marts were installed and
connected to the ETL tool.
Objective c
The end-user interface implementation. At this stage, it is important to choose a tool that will
allow the company to effectively represent the collected data in the form of analytical reports
for employees. The objective was considered successful when the end-user interface was
installed and connected to the BI structure.
Project Timeline
Milestone or deliverable
Planned
duration
(hours or
days)
Actual
duration
(hours or
days)
Projected
start date
Anticipated
end date
Actual start
date
Anticipated
start date
Project goals and objectives
discussion meeting
1 day
1 day
9.18.2024
9.18.2024
9.18.2024
9.18.2024
Determine existing data
analytics structure
2 days
2 days
9.19.2024
9.20.2024
9.19.2024
9.20.2024
Identify the problems existing
in the analytics structure
2 days
2 days
9.21.2024
9.22.2024
9.21.2024
9.22.2024
Identify adjustments needed
based on company’s goals and
resources
2 days
2 days
9.23.2024
9.24.2024
9.23.2024
9.24.2024
BI team assignment and
training
5 days
5 days
9.24.2024
9.28.2024
9.24.2024
9.28.2024
BI infrastructure design with
identification of appropriate
tools
5 days
5 days
9.29.2024
10.3.2024
9.29.2024
10.3.2024
BI infrastructure setting
10 days
12 days
10.4.2024
10.13.2024
10.04.2024
10.15.2024
BI infrastructure testing
2 days
2 days
10.14.2024
10.15.2024
10.16.2024
10.17.2024
End-users training
5 days
5 days
10.16.2024
10.20.2024
10.17.2024
10.21.2024
The main stages of the project implementation took place according to the schedule, as the
project team worked well-coordinated and according to a well-thought-out preliminary plan.
However, the delay occurred during the BI infrastructure setting phase, which was scheduled
to be completed in 10 days. Instead, the process took 12 days, as there were difficulties
connecting the company’s databases to the ETL tool, which required additional research and
planning. Difficulties arose due to the incompatibility of existing databases with the selected
ETL tool. After reviewing the materials described in the review of the work section, it was
decided to switch to Pentaho ETL solution, which required additional settings. Thus, the project
implementation ended on 10.22.2024 instead of 10.20.2024 with a delay of less than 2 days.
Unanticipated Requirements
An unanticipated requirement that arose during the implementation of the project was the need
to choose a more relevant ETL tool. The problem arose during setting up the BI infrastructure
and testing its operation. Initially, it was planned to use the Apache Spark tool as an ETL since
it is free and easy to implement. However, later it was discovered that this Apache Spark does
not correctly interact with various file formats, including PDF, which are widely used in
company databases. This problem led to incorrect data filtering, which drastically reduced the
effectiveness of the tool. Thus, it was decided to choose a more suitable instrument, which
resulted in the need for additional research and a slight delay. As a result, it was decided to use
Pentaho because this structure is able to work with a variety of files, and it is also widely
considered in the literature and is popular in case studies. The main reason for choosing this
solution was the widespread use of Pentaho among SMEs, which is most relevant for this
project.
Conclusions
The main result of the project implementation is the creation of a fully functional BI
infrastructure in Textile Company A. The potential effect of the project is the ability to
effectively use descriptive and predictive analytics in the organization to optimize current
operations and plan for expansion. Among the immediately observed effects, one can single
out the complete automation of the process of storing, analyzing data, and generating
information reports. Most importantly, the ETL tool integrated into the infrastructure allows
the company to minimize the amount of irrelevant information through the sorting of data in
the databases used. The project is considered successful because, as a result of the
implementation of the BI infrastructure, it became possible to generate multidimensional
informational reports that various departments of the company can use for a more efficient
decision-making process.
Project Deliverables
Figure 1 shows a diagram of the data analytics structure that existed at Textile Company A
before the project was implemented. Previously, the company used manual data analysis and
the generation of information reports, which were handled by employees of the analytical
department, which resulted in low efficiency of data analysis. Figure 2 illustrates the BI
infrastructure diagram that was created at Textile Company A as part of the project
implementation. This scheme describes all the key elements of the system and their interaction.
Based on this illustration, it can be judged that the project made it possible to automate the
process of data analytics in the company in order to increase the efficiency of the decision-
making process based on reports. Finally, Figure 3 contains an example of a multidimensional
report that is generated by the BI infrastructure created within the project. This illustration is
the most important deliverable of the project, as it shows the actual result of its implementation.
Figure 3: The Example of Multidimensional Report Generated by BI Infrastructure
Visualization Tool.
