EmergingTrendsinDataAnalyticsandBusinessIntelligenceStudentExample1.pdf
EMERGING TRENDS IN DATA ANALYTICS AND BUSINESS INTELLIGENCE 1
Emerging Trends in Data Analytics and Business Intelligence
List Names of The People in your group
Business Intelligence (ITS-531-20)
University of the Cumberlands
Professor Kelly Bruning
EMERGING TRENDS IN DATA ANALYTICS AND BUSINESS INTELLIGENCE 2
Table of Contents
Introduction ..................................................................................................................................... 3
Emerging Trends in Data Analytics and Business Intelligence ...................................................... 4
Increasing Operational Efficiency with Business Intelligence and Analytics ................................ 5
Business Intelligence .................................................................................................................. 5
Practical implications of BI ........................................................................................................ 7
Example .................................................................................................................................. 7
Future of BI ................................................................................................................................. 8
Positive and Negative impact of BI ............................................................................................ 9
Recommendations ....................................................................................................................... 9
Data Analytics and Business Intelligence in Cloud computing .................................................... 10
Practical Implications................................................................................................................ 11
Example ................................................................................................................................ 11
Example ................................................................................................................................ 12
Future of Cloud Computing ...................................................................................................... 13
Positive and Negative Impacts .................................................................................................. 14
Recommendations ..................................................................................................................... 15
Location Based Analytics ............................................................................................................. 15
Real time implementation of location analytics........................................................................ 18
Example ................................................................................................................................ 18
Example ................................................................................................................................ 18
Predictions................................................................................................................................. 20
Negative and Positive Effects on Business Organizations ....................................................... 20
Positive Effects ......................................................................................................................... 20
Negative Effects ........................................................................................................................ 21
Recommendations ..................................................................................................................... 21
Conclusion .................................................................................................................................... 21
References ..................................................................................................................................... 22
EMERGING TRENDS IN DATA ANALYTICS AND BUSINESS INTELLIGENCE 3
Introduction
The emerging trends in the field of Business Intelligence and Data Analytics has noticeably
advanced with several innovations, evolutions in the modern data driven era. There are several
technologies currently trending which are being adapted by numerous organizations. For
instance, machine learning, data science, advanced data analytics, data governance, Apache
Hadoop, Apache Spark, internet of things, no sql, blockchain, virtual reality, geo-spatial location
analytics and business intelligence on the cloud etc. These emerging trends help organizations
with improved customer management, improved cost management, operational excellence, data
quality, security, customer data confidentiality, better use of business data through Business
Intelligence and data warehousing.
EMERGING TRENDS IN DATA ANALYTICS AND BUSINESS INTELLIGENCE 4
Emerging Trends in Data Analytics and Business Intelligence
The following are the three trends discussed in this paper
1. Increasing Operational Efficiency with Business Intelligence and Analytics
2. Data Analytics and Business Intelligence in Cloud computing
3. Location Based Analytics
EMERGING TRENDS IN DATA ANALYTICS AND BUSINESS INTELLIGENCE 5
Increasing Operational Efficiency with Business Intelligence and Analytics
Business Intelligence
Business Intelligence incorporates many technologies, tools, applications for analysis and
best practices are inherited to integrate, collect, analyze, and display raw data of business
organization for creating actionable and insightful business information. BI as a process of
technology-driven and as a discipline is made up of numerous linked activities, comprising
online analytical processing, data mining, reporting, and querying. BI tools comprise of business-
driven data, to provide supporting documents and reports useful for business decision making.
With BI tools, business persons may start examining the data themselves instead to wait for
Information Technology to run analytical and compound reports. This information access
benefits operators back up commercial decisions with solid numbers, rather than gut anecdotes
and feelings. BI in business aims to support executives understand their business needs to make
improvements, plan budgets, provide managers with their team performances and upgrades to
make business decisions. Organizations also use BI tools to run their budget reports for cutting
costs, modifying existing applications by upgrading to latest versions and specify incompetent
operational procedures.
BI maintains and improves working efficiency and benefits companies to increase
executive productivity. The software of Business intelligence deals with several benefits,
comprising influential data and reporting analytics abilities. Using BI’s data visualization tools
such as real-time dashboards, directors may generate instinctive, clear reports that enclose
relevant, unlawful data. Business Analytics is the course of discovering reports and data in order
to remove expressive insights, which may be used to high understand and increase the
performance of the business (Hung, Huang, Lin, Chen, & Tarn, 2016).” BI deals as an objective
EMERGING TRENDS IN DATA ANALYTICS AND BUSINESS INTELLIGENCE 6
management function. Managers are capable to program data depend on goals, which can
include sales objectives, financial objectives, or productivity measures, regularly. The features of
BI subsidize to the objective of offering an awareness of present commercial practices. The
software of Business analytics is used to analyze and explore current and historical data. It
exploits statistical analysis, quantitative analysis, and data mining to recognize past commercial
trends.
According to Robles-Flores and Kulkarni (2013), the rapid rise in data volume in
businesses has meant that comprehensive data gathering is barely likely through manual means.
BI solutions may help here. They offer tools with proper technologies to contribute to the
integration, collection, editing, storage, and study of existing data. Though almost only big
companies were involved in this matter a few ages ago, it has temporarily also developed
necessary for start-up businesses, and so the marketplace for BI has been increasing for years. He
focuses on the overall potentials of consuming BI in the beginning (Kulkarni & Robles-Flores,
2013). First, it will be observed which workers of result that are appropriate for beginning and
what chances exist for realizing BI systems in the beginning. Then it will be revealed to what
amount BI has succeeded in the beginning, in which parts the methods of BI are practiced in
start-ups, and what drive BI has in the beginning. Finally, the critical success factors for the
projects of BI, in the beginning, are considered.
With growing globalization of marketplaces, aggressive competition, growing the speed
with variations in customer needs and market conditions, all market members and businesses
look new challenges. In the long run, businesses will be capable of recognize themselves, who
may adapt to these situations, who may respond quickly and be flexible to changes though at the
similar time consuming their costs under the device. For this purpose, a precise knowledge of the
EMERGING TRENDS IN DATA ANALYTICS AND BUSINESS INTELLIGENCE 7
present corporate and marketplace situation is crucial. To safeguard this and to offer
management with the data needed in their decision-making and planning, cultured information
and communication schemes are practiced. Like the 1960s, numerous approaches have been
industrialized for the systems, which have to develop known under numerous diverse names like
Decision Support Systems, Management Information Systems, and Executive Information
Systems. Today, the word BI has become recognized both in research and in practice. BI defines
methods like collecting, processing, storing, analyzing, and offering company data.
Practical implications of BI
BI has a direct impact on the business strategic, operational, and tactical business results.
BI confines fact-based decision making to consume historical data instead of assumptions. The
tools of BI perform business data analysis and generate reports, dashboards, summaries, graphs,
maps, and charts to offer users with complete intelligence about the business nature. BI supports
on data visualization that improves the data quality and the decision making.
Example: An owner of the hotel practices analytical applications of BI to collect
statistical information about average tenancy and room rate. It benefits to discover aggregate
income generated in each room (Wieder & Ossimitz, 2015, pp. 1163-1171). It also gathers
statistics on marketplace share and information from consumer surveys from every hotel to
choose its competitive situation in numerous markets. Through analysing these trends every year,
every month, and everyday supports management to provide discounts on hotel room rentals.
Example: A bank provides certain level of access to branch managers to multiple
applications of BI to evaluate employee performances, operational data and compare it to the
other zones. It helps the branch manager to govern who the supreme profitable consumers are
and which consumers they must work on. The usage of BI tools frees IT staff from the challenge
EMERGING TRENDS IN DATA ANALYTICS AND BUSINESS INTELLIGENCE 8
of producing logical reports for business departments. It also provides employees with rich
source of data with certain access levels to look over the percentage of commissions they are
earning monthly, run market share reports and look over risk profiles.
Future of BI
The key future trends of business intelligence is forecasting and development of the
digital BI world into space where platforms and tools will develop more wide-spectrum and
finally, highly collaborative (Bach, Jaklič, & Vugec, 2018, pp. 63-86). The development of BI
has been intensive on small form-factor strategies, but the emphasis will shift to actual big touch
devices. “This will permit team colleagues to function towards business decisions by the side-by-
side data exploration in actual thought time.” Numerous vendors are functioning toward this
enlarged integration, with application programming interfaces permitting for business data
analysis in users’ current systems. The BI industry has extended exponentially in current years
and is probably to endure growing. If you want to create the business data analysis in a
recognized or newly approved BI system, your team must be data-driven. Businesses must focus
on how and why they are consuming data. With these goals in mind, business leaders may design
a strategy for BI usage specialized for their team, provide them with cloud based environment to
store structured and unstructured data and create a data-driven environment. The software of BI
will become more accessible as the business grows. This development will also drive a more
informed user base. However, along with these developments, commercial leaders are essential
to take on the duty of educating their staff.
EMERGING TRENDS IN DATA ANALYTICS AND BUSINESS INTELLIGENCE 9
Positive and Negative impact of BI
The businesses have focused on BI for gathering important competitive data from past
data and inspecting it in graphs and dashboards. Though static data is no longer necessary for
creating informed results. In the present competitive marketplace, businesses need a view of not
only the past and today's consequences, but also what is probable to occur in the future so they
may anticipate and strategy for change. Rather than BI, in the year 2019, the focus will be on
commercial insights, where businesses judge the performance on data-driven analytics and
measuring business analytics as per the results, and forecasting outcomes depend on past data. It
will all be around the value that data may create for its operators, instead of dashboards and
reports.
The negative impacts of BI come when user does not have a big pool of correct data from
which to compete for conclusions (Kulkarni & Robles-Flores, 2013, pp. 15-17). When this
occurs, decision-makers will often create wrong decisions as they are creating their decisions off
data that is incomplete or inaccurate. It is essential in BI to extrapolate numerous elements and
factors to go along with the data that is gathered in order to derive to a complete picture that is
required to create business decisions.
Recommendations
As per the recommendations, BI is facing new technologies ad approaches, proposing
both disruptions and opportunities for buyers and suppliers. BI consumers are now challenging
solutions that are easier to deploy, buy, use, and integrate to support mobile computing and
social or collaborative capabilities. Business Intelligence is very vital for business organizations
for distributing useful data from the great volumes of data being composed. There are numerous
BI tools accessible, but no tool is correct for each user’s requirement. Organizations are to
EMERGING TRENDS IN DATA ANALYTICS AND BUSINESS INTELLIGENCE 10
understand the emerging trend of BI to better their operational performance (Islam, 2018) and
integrate the most efficient tools by concentrating on the budget assigned to their development
team and allow them to come up with Data accuracy and compliance and be transparent to
identify and eliminate the gaps that leads to improve customer satisfaction.
Data Analytics and Business Intelligence in Cloud computing
Recent days the emerging trend is Cloud Computing. Cloud Computing is described as a
type of computing which relies on the shared resources. In that case there will be less usage of
local servers or personal devices to handle different type of applications. All the applications are
mostly accessed via web. When accessing via web the services are delivered and used for the
internet and are paid by the cloud customer (Gupta, Mittal, Joshi, Pearce, & Joshi, 2016). Cloud
computing is the place where the hardware and software is located and the way it works will not
matter but the user will be somewhere up cloud that represents the internet.
Cloud computing are very popular the reason is to reduce cost and complexity of
operating computers and networks. Cloud computing is considered as efficient as it allows
organizations to focus on innovation which helps in the product development. Mainly cloud
computing is used for unlimited storage in the cloud, where the cloud is cheaper than the drive
storage space (Gupta, Mittal, Joshi, Pearce, & Joshi, 2016). There are some providers who
introduce unlimited storage. There are some reasons to use cloud computing for data protection
and there is a flexibility for this. The main reason is to access the data anytime and anywhere by
using the services.
According to (Yang, Huang, Li, Liu, & Hu, 2016) it was noted that the organizations face
three basic private cloud paths which build internally with developer focused tools and the
infrastructure which is defined by software. Most of the organizations use cloud computing for
EMERGING TRENDS IN DATA ANALYTICS AND BUSINESS INTELLIGENCE 11
the business services. The organizations will start their own digital application platforms that will
include server less and event driven services and form the basic foundation for the business to
run in core level. Cloud computing is mainly used to deploy, monitor and upgrade the
technology for the organization to run in a good profit which is very helpful for the business.
According to Han, Liang, and Zhang (2015) the integrating mobile sensing and cloud
computing which helps in forming the single idea of mobile cloud sensing. For the mobile
platform the data is provided as data –as –a –service in the case of cloud. It is a powerful
computing for the mobile devices to connect regarding network resources. There are solutions
which have been investigated to connect mobile devices with the most powerful cloud
computing where the network and its capabilities (Hung, Tuan-Anh, and Huh 2013).There is an
example for the cloud based mobile Augmentation which is the emerging trend of threat mobile
computing model to increase and enhance the storage capabilities of mobile devices. Soyata et al.
(2012) Mobile cloud-based Hybrid Architecture is proposed for mobile cloud computing
applications. Cloud computing offers unlimited demand processing power. There is a limitation
of cloud computing network bandwidth by which the efficiency of computation will be impacted
over large data volumes Virtualization of cloud computing is a challenging task to ensure the
data and to support the data processing (Huang et al. 2013).
Practical Implications
Example: AWS is an example in which cloud computing is used as emerging trend their
different types of services. Amazon Web Services hosts a cloud conference which is AWS
relevant. The conference lasts for a week and it provides opportunities to gain some information
about the Amazons products and the latest launches and the services which has to be provided to
all the skill levels. In next two years Amazon web services (AWS) will be reaching a high
EMERGING TRENDS IN DATA ANALYTICS AND BUSINESS INTELLIGENCE 12
revenue of all cloud services. It clearly shows that Amazon leads ahead of Google and Microsoft
in cloud computing. If there is an issue, to address that issue a set of new machine learning
models were released when there is a cloud conference which will be helpful to the developers
and scientists to deploy and manage machine learning models. There are some latest services
which are used is Amazon Recognition, Amazon Polly.
In Amazon server less cloud computing allows the developers to develop and run the
applications and services without any complex infrastructure of servers. Server less is one of
the emerging trends in cloud computing. There is another service which is called AWS Server
less Application Repository which is designed to be in the publication, discovery and
deployment of server less applications. These products and services will be widely used.
Example: The other example where the cloud computing is an emerging trend is in the
healthcare sector. In the healthcare systems there are cloud based Electronic Medical Records
(EMR). These records are done electronically and secured and that data will be centralized in a
storage location. EMRs can bring the healthcare systems together in which the information will
be accessed across the other healthcare system, they are connected through the Application
Programming Interfaces (API) that will be there in the cloud infrastructure. The developing
infrastructure in the health care system will working hard to connect to different type of trusts
,clinics and other hospitals through the cloud network. This cloud network can monitor the types
of cost effective services which are offered to the patients. There will be the communication
through the cloud where the doctors stay connected with the cloud based phone system. For the
high quality research which is very important in the case of patients cloud communication is
used. Sometimes if need doctors can communicate on phone where that process make some sort
EMERGING TRENDS IN DATA ANALYTICS AND BUSINESS INTELLIGENCE 13
of sense and this results in the best treatment for the patients. For the doctors the cloud –based
phone system would make very easy.
In the case of streamed collaboration this communication results in streamlined patient
care. This is a positive news where the patients need not visit the hospital when they can take
care of themselves for the first time. By cloud technology the whole medical team will be able to
easily communicate and share the information. This communication will be very easy in today’s
world because the cloud network can be used anywhere and anytime. Sharing data between
pharmaceutical giants with the help of cloud network will help the researchers in choosing the
best option. The most recent witnessed is when the clinical trial big data revealed which is used
curing the lung cancer. Flatiron Health center said that most of the potential data of cancer
patients is yet to be analyzed. By the cloud network mainly the communication will be easy to
the doctors and the data of the patient can also be checked anytime or anywhere when in
emergency. This was one of the emerging trends of cloud computing in the Healthcare system.
Future of Cloud Computing
In future Cloud network solutions will be increasing affordable and will have a sparking
interest from businesses who seeks the availability in the case of security for the data and
systems. Most of the enterprise IT organizations will be committing to hybrid cloud architectures
in future IT World. Some of the most innovative companies will be starting investigating and
offering hybrid cloud services to different industry sectors. The Companies like Amazon,
Microsoft are the currently are the two top companies which are using the cloud network and the
tech companies like Oracle and Google are in the way to reach the goal in the using the cloud
network. Amazon’s AWS stack and Microsoft high level service offerings are ahead in today’s
competition where both offer infrastructure and platform of service. Market share is likely to
EMERGING TRENDS IN DATA ANALYTICS AND BUSINESS INTELLIGENCE 14
shift to Google which introduces new cloud services and has a focus on modifying their cloud
strategy.
Many leading organizations will be able to design, build and operate cloud services
which is used to help in reducing management complexity and operating costs. Cloud architects
will need broad skills in infrastructure design and optimization with deep security. In future
cloud computing will be enabling individuals and organizations of all sizes to work with data in
inspiring ways. Cloud computing will be impacting virtually in every aspect of technology from
corporate capabilities.
Positive and Negative Impacts
Many enterprises still consider cloud computing as the concept of large number of
computers connected to the internet in real-time (Deshmukh & Shah, 2016). With the increasing
number of people who becomes aware of storing the data and accessing the same data. There are
some positive and negative impacts of cloud computing. The positive ones is cost reducing
which is most significant cloud computing benefit. The cost includes IT cost and non-IT cost.
The other positive is flexibility which refers allowing employees to be flexible of work practices.
Employees can access anything stored in the cloud and web-enabled devices such as smart
phones, laptops, and notebooks. Cloud computing will enhances the function of remote working.
Some of the negatives is cloud computing has benefits for which many enterprises allows
to concentrate on their core business than IT and infrastructure issues (Deshmukh & Shah, 2016).
These shortcomings are mainly related to smaller business operations. The providers may offer
to compensate the outage where the customers will not be satisfied.
EMERGING TRENDS IN DATA ANALYTICS AND BUSINESS INTELLIGENCE 15
Recommendations
Cloud computing shows that IT professionals every IT professional should be aware of
the upcoming trends emerging in cloud computing, adapt it to their data infrastructure and make
sure their application developers and architects are aware of how to utilize and manage data on
the cloud environment.
Location Based Analytics
Location based analytics is a tool that enables the business organizations to make decisions in
context to the geographic location of either the consumer or the consumer base. Often the
location data is coupled with Geographical Information Systems to provide the clear
understanding of how the data is impacting the organization’s business. Geographical
Information Systems provide the ability to visually analyze the data obtained from various
sources. Geographical Information Systems which form the critical tool for visually analyzing
the effects of data in topographic, environmental, and demographic perspectives. Integrating the
location data with the business data provides the reliable, accurate predictions of the businesses
and better business decisions. According to Heesung,W.,2018 .Location analytics or the Location
Intelligence is the tool facilitating the pictorial representation of the data like the Heat Maps on
the Map. Location intelligence is the combination of the geo spatial data warehouse and various
geo spatial Online Analytical processing tools (OLAP).
According to Turban et al , 2015, “Location or the Geospatial analytics is the combination of
visualizing tools, the business factors and the key performance indicators (KPIs) to achieve the
visual presentation of the information to make decisions for sustaining and thriving business”.
The location analysis would provide the advantage of exploring the opportunities of a specific
EMERGING TRENDS IN DATA ANALYTICS AND BUSINESS INTELLIGENCE 16
region. Geospatial analytics enable the entities or organizations to analyze the data on the basis
of the location in addition to the dimensions offered by the tradition business analytical
technologies (Wang,Z., Hu., & Zhou, W. 2017 ) .One of the major sources of the spatial data is
Geographic Information Systems (GIS). Location data doesn’t provide the complete analysis
whereas the combination of the Enterprise data warehouse and geospatial data would serve as an
reliable source of Business intelligence. Location based analytics require the Spatial data
warehouse and the location based data is input through the various sensor technologies, global
positioning systems (GPS) or through installation of Radio Frequency Identification (RFID)
devices in various logistic businesses (Hirve,s., Marsh, A., Lele, P., Chavan, U., Battacharjee, T.,
Nair, H., Campbell, H., & Juveskar, S. 2018). Spatial or location data added to the Enterprise
data ware house facilitates the business organizations to perform calculation required to perform
the data analysis with more productivity and unveiling the various trends and patterns. According
to Sachan et al. 2016, the results enable the business to establish the relationship between the
location data and the business Key performance indicators in an organization. The location
intelligence employs different data visualization techniques like Heat maps besides the
traditional data analysis visual techniques like bar graphs, pie graphs, and tables (Farney, T. A.
2011). The Geographic Information System data viewers are used for the visual representation of
the location data. According to Ziming et al, (2016) The Location data analytics carried out in
following steps.
a. Enrich: The data various sources in the context of location is collected in relation to various
other Key Performance Indicators (KPIs). The data collected is stored in an integrated data
ware house to achieve the holistic effect on the organization.
EMERGING TRENDS IN DATA ANALYTICS AND BUSINESS INTELLIGENCE 17
b. Analyze: Analysis of the huge data with data points or Key Performance Indicators (KPIs)
from data warehouse in relevance to the location data.
c. Map and Discover: After analyzing the data, it is further visualized through various visual
analytical dashboards. The data patterns and correlations are identified by applying various
algorithms.
d. Predict: Identifying the patterns to identify the causes, improve the process to meet the needs
of consumers, identify the areas of improvement and explore new fields of growth for the
business expansion.
EMERGING TRENDS IN DATA ANALYTICS AND BUSINESS INTELLIGENCE 18
Figure 1. Categorization of Geospatial analytics-based applications. From classification of
location-based analytics, Sharda, R., Turban, E., & Delen, D. (2014). Business Intelligence and
Analytics: Systems for Decision Support, Global Edition. London, England: Pearson.
Real time implementation of location analytics
Example: Location based analytics employed at Great Clips: According to Turban et al, (2015)
Great clips has used location analytics tool provided by Alteyx to analyze the location-based data
and integrated customer data to explore new locations for starting new saloon site locations. By
implementing the Location intelligence provided by Alteryx great clips achieved reduction in the
time to analyze the data, improved performance by reduction in workforce, and make smart
decisions.
Example: Location Analytics at Intergalactic telephone Corp (ITC): Inetrgalactic Telephone
Corp offers telephone services to its customers across USA. There were number reported
incidents of dropped call across USA (Turban et al, (2015). The company has decided to do
Location analytics with the assistance of Teradata in the North Eastern USA.
EMERGING TRENDS IN DATA ANALYTICS AND BUSINESS INTELLIGENCE 19
Figure.2. Zoey and Jake (2012). BSI Teradata: The Case of the Dropped Mobile Calls
[PowerPoint slides]. Retrieved from https://www.slideshare.net/teradata/bsi-teradata-the-case-of-
the-dropped-mobile-calls.
EMERGING TRENDS IN DATA ANALYTICS AND BUSINESS INTELLIGENCE 20
Predictions
Following are the predictions of location analytics,
Location intelligence would be used in all kinds of industries like Information Technology,
Health care, Retail, Pharma, insurance, telecom, and fast food chain industries. I would predict
the usage of analytics in food industry to cater the needs of different demographic categories
depending on their food habits. Location analytics has a great scope in the Information
technology. Location analytics in IT would help develop web applications, mobile applications
to cater the personalized view to people of different regions.
Negative and Positive Effects on Business Organizations
Location analytics has both positive and the negative effects on the entities. The following are
the positive and negative effects.
Positive Effects
1. Location intelligence helps organizations to serve the needs of people on location centric
basis.
2. Geospatial analytics helps organizations to plan future events based on the previous data.
Find helpful in Weather prediction and climate patterns.
3. Geospatial analytics solves various issues associated with the business organizations and
increased productivity and business performance.
EMERGING TRENDS IN DATA ANALYTICS AND BUSINESS INTELLIGENCE 21
Negative Effects
One major negative effect is the loss of customer privacy which further would raise lots of legal
issues.
Recommendations
I would recommend the organizations to develop applications that would gather the location data
in compliance with the rules or legislatures of respective countries where they operate their
business. Develop an application that would report the patterns and trends by analyzing the
different kinds of analytical data like demographic data, gender data, Age data, mortality etc.
Conclusion
The need to increase the business value is paving the way for the rise of emerging trends in
business and data analytics. The question that arises is how to measure the business value. This
paper discusses in detail about measuring business value based on the needs of the business user
and data accessibility using analytic and visualization tools. To make the analytics more
meaningful to the business user, highly appealing visualizations are built that reveal deeper data
insights based on user preferences. The analytic applications are being embedded in enterprise
applications. Issues such as data integration, data storage, data analysis are being extremely
critical during the system design (Kohavi, Ron & J. Rothleder, Neal & Simoudis, Evangelos,
2002, p.7). To enhance the effectiveness of analytics, the business analytics solutions are being
extended beyond customer focused to sales, marketing and other business supporting functions.
In the end, to achieve the optimal results, a number of analytic solutions exist today that provide
mechanism to provide deeper insights and a method to measure the key performance indicators
in an actionable manner.
EMERGING TRENDS IN DATA ANALYTICS AND BUSINESS INTELLIGENCE 22
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