6000 Words - Capstone Project Report on Data Warehousing role on Information Systems

• Purpose/Motivation: research questions, relevance & context of the project (one or two sentences). • Design/Methodology/Approach: Explain the methods used for the data collection in your project (this includes literature; and other analyses your project requires) • Results/Findings: Briefly outline the results of your systematic search; the consistent themes, any gaps if appropriate (one or two sentences). • Conclusion: final assessment of the overall evidence and how it will help you conduct the rest of your project (one or two sentences).

• the nature of the topic under discussion (the topic of your project)

A data warehouse is a massive collection of corporate data used to aid decision-making inside an organization. The data warehouse concept has been around since the 1980s, when it was created to help in the transfer of data from being used to power operations to being used to feed decision support systems that disclose business insight.

• the reason for the project

The reason for the project is finding out the role of data warehousing on information systems, accommodating business intelligence, and improvement of business agility. The project identifies technologies such as data warehousing projects in information technology systems, resonating with fundamental IT projects in improving business performance efficiently and cost-effectively.

• relevance

The induction of the report in the field of the study is relatively relevant to our team, as it will guide the various principles used in the compelling research study in the future, hence providing the avenue for more skill and knowledge regarding the data warehouse in the information systems which will help our team to excel in our career.

• the research questions addressed

Some of the research questions for our proposed study addresses are:

· What is an information system and what type of data repositories are used in information systems?

· Why are data warehouse created? Role in information systems, especially when it comes to business intelligence.

· Data warehouse in information system – what the literature says about this? How it used? Why is it used?

· Finding examples and research discussion of these examples.

· Investigating a company that adopted data warehousing.

· Why and in what circumstances data warehouse are used in organization through the information systems?

• the parameters of the topic (what does it include and exclude)?

Our research parameters will include the importance and issues of data warehousing on information technology and exclude focus on experimenting. The study aims to identify the role which is played by data warehousing on the information systems. Notably, some of the parameters reflected by the research study include the literature review, discussion, study implication, and results.

· targeted search: identify names, dates databases, professional sources searched

Our research started with a focus on reputed organisations rather than local companies, so we targeted search to identify names of the big companies who are incorporating data warehouse in their business. We reviewed comprehensively scholarly papers, journals, conferences and case studies to show how an organization actually uses data warehouse.

· Search strategy: keywords, phrases used

Our search strategy started with narrowing the topic and coming up with subject headings to search under. Our team did test search and discussions of the results of those searches.

For leading a well-organized and positive search procedure, the below libraries were searched to get most applicable materials:

· ACM

· IEEE Xplore

· ScienceDirect

· Emerald

· Google Scholar

The aim behind the choice of these libraries is that these libraries are central in the field and publishing excellence research which are peer reviewed. Phrases used:

“Data warehouse, Data Warehouse with Big Data Technology, Adoption of Data Warehouse in organization, Data Warehouse and Business Intelligence, Strategic Business Management and Data warehousing, Strategic oriented Business Intelligence, Amazon Redshift Cluster Management, ETL process for Decision Support System, Trend in Information Technology, Role of Data Warehouse in Digital Business, Role of IT based system in Business. Issues and Importance of Data Warehousing.”

· Why was information included and/or excluded?

· What specific topics of interest were targeted?

Several forms of study were done to show the role of data warehousing application in information systems. Business Intelligence, Data Warehousing and Information Systems are closed related. Conducting our research following our search strategy, we have found the below papers relevant to our topic for further discussion.

1) Data warehouse as a backbone for business intelligence: Issues and challenges. (Al-Debei, M. M. 2011)

2) Healthcare data warehousing and quality assurance. (Berndt, D. 2001)

3) An empirical investigation of the effects of data warehousing on decision performance. (Park, Y. T. 2006)

4) Data warehouse success and strategic oriented business intelligence: a theoretical framework. (Alhyasat, E. B (2013)

5) UK data warehousing and business intelligence implementation. (Dobbs, T. 2002)

6) Beyond data warehousing: what's next in business intelligence. (Golfarelli, M. 2004)

7) An ETL strategy for real-time data warehouse. (Zhou, H. 2011)

8) The data warehouse and data mining. (Inmon, W. H. 1996)

9) Information system integration. (Hasselbring, W. 2000)

10) Inconsistencies in data warehousing.

11) Data warehouse applications in modern day business. (Issa, C. M. 2002)

Across the globe, various companies have adopted data warehousing. For example, Netflix is among the big brand organization that uses the data analytics to enable effective advertising. Thus, with over 100 million subscribers, it makes it possible for the company to collect huge data, which helps the firm achieve industry status, which boosts the firm operation. Thus, being a subscriber, the firm suggests the next movie, which the client should probably watch the ideal which is agitated by the last search and the watch data, thus indicating how the firm gathers big data (Fernández-Manzano, E. 2016).

There are various organizations that uses the data warehouse in the effective strategic business management. Notably, some organizations are involved in the digital market and supply management of various products across the world. Some of the organization includes Amazon, Alibaba, Apple, eBay, and Walmart (Harris, D. 2013). Among the organization which uses the data warehouse is Amazon Inc. Notably, Amazon Inc has invested extensively in the data warehouse in their operation. For instance, the organization uses the Amazon Redshift cluster management, which involves the fully managed petabyte-scale data warehouse services in the cloud storage. Notably, the organization can service all the work setting up, operating, and ensuring effective data warehouse scaling through the Amazon Redshift (Gupta, A. (2015).

· Study design classification? (In the articles selected, did you look at the types of methods used to report findings)

Mostly the authors employed the systematic literature review survey to induce the specific research and induce the practical and systematic collection of the defined research question and the main keywords to effectively analyse the results.

• How many sources collected

• How many reviewed

• How did you classify organize the literature? (e.g., methodologies and methods in use; general conclusions that are being drawn)

We collected around 50 sources of information and filtered out to choose the most relevant topics to conduct our research. We classified information in a topical order by showing connection to our project topic.

While progressing we went through focused subjective discussion in our group with or supervisor to implement our ideas/findings in our report.

In general conclusions, we can say that, you can obtain information, analyze it and make conclusions on the basis of the warehouse data. Data can be applied to different dynamics gathered in a storage. For example, warehouse data are used to assure effective tuning strategies. The information systems, on the other hand, include the integral collection of different components utilized to acquire, process, record and enhance information, digital products and practical knowledge. Most businesses depend on the information system, in particular, to enable and run their business and compete competitively on the market. The research study presents the road that defines the practical framework to develop the data warehouse, particularly for current businesses and detect the involvement of the data warehousing in the computer system in various peer review academic literature.

• What are the key themes and ideas common in the literature?

Common ideas in our findings are that, using the business intelligence from the data warehouse was inducting the effective strategic outcome of the firm performance. Moreover, it was making the efficient decision-making procedure and better organizational intelligence, hence enabling the better running of the business. The three vital themes shared by data warehouses, is processing time, drilling down, and drilling up. If any information system has all of these features, and it turns with a high level of effectiveness, it can be recognised as data warehouse. A distinctive data warehouse has four mechanisms: a central database, ETL (extract, transform, load) tools, metadata, and access tools. All of these components are planned for rapidity so that you can get values quickly and analyse data.

• What contradictions/ inconsistencies did you find?

Any corporation must first invest in the infrastructure needed to support the data warehouse and business intelligence tools. The following rule applies: the better the architecture is set up and developed, the better the results. On the other hand, information systems involve the integrated set of various components used for collecting, processing, storing data, and enhancing information, digital products, and practical knowledge. Notably, most business organizations depend on the information system to facilitate and manage their operations and compete in the market favorably.

When a data warehouse attempts to syndicate inconsistent data from dissimilar foundations, it meets errors. Inconsistent data, duplicates, logic conflicts, and missing data all result in data class challenges. Poor data quality consequences in defective reportage and analytics essential for best decision making (J. Stuller, 1999).

• What relationships did you find between articles?

The data warehouse is applied in diverse ways in the information system. For instance, information technology tools are used in the data warehouse process. The data from the warehouse can be analyzed using the information system to provide various results. In the information system, a data warehouse is used in marketing analysis, competition analysis, and the banking sector to identify the various marketing dynamic, which helps the organization in prudential strategic management as they make data mining relatively possible. Information system enables the firm to incorporate the data warehouse through mining which enables effective analysis of the information for decision-making purposes.

• What gaps did you find?

We found gaps in sector of cost/benefit analysis. A data warehouse is a large IT development, and like numerous vast IT projects, it can slurp a portion of IT man hours and financial cash to create a tool that doesn't get used frequently adequate to defend the application expenditure.

· What does the evidence mean overall? Explain the information found. What answers did you find to your research questions?

· What gaps did you find?

· What else needs to be done?

We have reviewed the below papers for literature analysis and discussion, draft information on the papers are below:

The investigators of the project “Data storage to analyze multidimensional simulated data from a geographically distributed agro-structural model to better nitrogen management in catchment", discuss the data store designed to store and allow for successful data simulation analysis. The findings have enabled the authors to use OLAP effectively to investigate, extract and aggregate the data to extract various data types. (Bouadi, 2017)

"To develop the ophthalmological data storage and show the patients data using electronic medical records." The goal of the study is to establish a Database for the near-even day-to-day use of electronica medical data and diagnostic instruments in an academic ophthalmic Centre. The results have been used to drive data visualization and filtration development using the Internet surfing tool. The author concludes by saying that the decision is based on the data warehouse structure and structured data. (Kortüm et al.2017)

A study was carried out to examine the development & deployment of a 'forebrain' warehouse management system in-house. Data repository information system and type. The study aimed to determine the dynamics of data storage and can be recreated in order to facilitate the requirements of consumers and to meet their requirements. The authors used the questionnaire survey to acquire the data analyzed using the methodology of qualitative data analysis. The authors found that a core brain in the data warehouse was effective and it had to be regularly modified to suit market requirements and demands. (Madurapperuma & Kuruppuarachchi, 2018)

In a research paper "The architecture and implementation of a Prostate Cancer Clinical Research Information Warehouse", the study used the data repository survey technique. The author identifies from the findings that the information system incorporates data from the data warehouse in order to allow for successful prostate cancer control. (Seneviratne et al. 2018)

A case study’s primary objective was to identify the key organizational criteria for the selection of data warehouse architectures. The study used field surveys and the methodology of multinational logistic regression. The authors remark that the many interconnections between organizational aspects can influence the architecture of the data warehouse. The authors conclude by highlighting the necessity to identify in the decision-making procedure, during restructuring, the model for optimal data warehouse prediction for effective selection decisions. (Ariyachandra & Watson, 2010)

In a research study that aims to identify "Autonomous data warehouse query prediction framework using the lazy learning strategy". The aim of the study was to discover the proposed autonomous data warehouse prediction framework. The study used the case-based approach of reasoning to estimate the data warehouse's performance. The authors found that the self-inspection, self-prediction and self-adaptation autonomous computing characteristics are included. The author will finish by saying that the methodology proposed yielded better outcomes than the lazy methods of learning, including the IBK, KStar and LWL. (Raza et al. 2020)

A survey to explore the "Combination of clinical data warehouse information and a pharmaceutical database in order to develop an electronically health record detection framework for co-morbidity." The purpose of this project was for the development by using a clinical data warehouse (CDW) with a knowledge base of an algorithm to detect comortistics in the Electric Health Records (EHR). The surveying method was used to examine the database of the Theriaque. Results indicated that 68.4% of 4312 General Dataset patients were missing a corresponding ICD-10 code, at least one prescription. The author suggests that the detection of comorbidity can be achieved by simple algorithms integrating accessible and immediately reused information in knowledge bases, drug prescriptions and laboratory test results. (Sylvester et al.' 2018)

A paper on "IoT based farm management information systems architectural design method." The aim of the study was to assess the effectiveness of data collecting and processing technologies in automation. The study employed the research approach based on design. The author has noted that the integration of the data automation information system into the farming operations is relatively effective. (Köksal & Tekinerdogan, 2019)

"The implementation in industrial enterprise resource planning of an information management system". The study used a schematic model of the information system model to determine the effective technique for inducing the company's logistic information. The authors believe that the usage of the information system helps to different strategic business planning by demonstrating real information necessary for decision-making and implementation. (VORONKOVA, V., et al. 2018)

A research paper, "Development of clinical data storage from a clinical information intensive care system." The study utilized the frame technique of Atos Origin Metadata. The authors identified a successful support system for effective infrastructure management renewal from the start. (De Mul et al., 2012)

The further work involves more in-depth analysis of our findings to tailor the results to better suit our project topic.

· From the information found, what will you do to implement your project?

· What processes/information/practices do you need to use/follow to successfully complete your project?

To execute a successful project, our team has to establish the project scope and goals. Managing and collecting quality data for research is important which we are an ongoing task for us till the final report submission. We aim to be specific with the project guidelines, and do reasonable and time-based work, monitor the progress and do evaluation in team. Reading quality literature and finding the relevance with the topic to implement the idea in the final report and presentation is our team’s next phase of this project.

· Restate the purpose of your review

The study is relatively effective as it provides the requisite knowledge and skill regarding the data warehouse and information system. The information is relatively effective, especially in business operations, healthcare, and government planning. Through the study, it will be possible to induce various dynamic changes that can improve livelihood and better the world in the current and the future.

· Describe the overall evidence: a summary of major findings (agreements and disagreements)

Generally, a big data warehouse and the information system for the global rider enable more accommodative data analysis. The analysis of the data helps the individual and the organization to make effective decisions making. Over the globe, most international and big brands use big data to effectively analyse the market dynamic (Jeble, S. (2017). The analysis enables the firms to have a positive competitive advantage and a better reputation in the market. The healthcare sector, for instance, can mine the information from the big data to identify the medical report of the patient hence facilitating positive diagnosis. Moreover, through the data analysis its possible for the organization to identify the area in which they will put more strength in the market dynamic for an efficient comeback in the operational process.

A data warehouse is a massive collection of corporate data used to aid decision-making inside an organization. Decision support systems can help make better use of the data in your data warehouse. These technologies assist executives in efficiently and effectively using the warehouse. They can acquire information, analyse it, and make judgments based on the data in the warehouse. The data collected in a warehouse can be applied to the different dynamics. Information systems involve the integrated set of various components which is used for collecting, processing, storing data, and enhancing the provision of information, digital products, and practical knowledge.

· What has been gained from the review

We achieved vast knowledge from the review. Firstly, the data collected in a warehouse can be applied to the different dynamics. For instance, the data from the warehouse is used to ensure effective Tuning Production Strategies. Secondly, data warehousing also aids in customer relationship management, according to operations analysis. The data warehouse enables effective leadership in the IT department and it releases the department from off the hook.

· What is your take home message?

As our take home message, thanks to data warehouse and business intelligence tools, for playing a beneficial role in information systems. However, a corporation must first invest in the infrastructure needed to support the data warehouse and business intelligence tools. The following rule applies: the better the architecture is set up and developed, the better the results will be. On the other hand, information systems involve the integrated set of various components used for collecting, processing, storing data, and enhancing information, digital products, and practical knowledge. Notably, most business organizations depend on the information system to facilitate and manage their operations and compete in the market favorably.

Harris, D. (2013). Why Apple, eBay, and Walmart have some of the biggest data warehouses you’ve ever seen. Gigaom. URL: https://gigaom. com/2013/03/27/why-apple-ebay-and-walmart-have-some-of-the-biggest-data-warehouses-youve-ever-seen.

Gupta, A., Agarwal, D., Tan, D., Kulesza, J., Pathak, R., Stefani, S., & Srinivasan, V. (2015, May). Amazon redshift and the case for simpler data warehouses. In Proceedings of the 2015 ACM SIGMOD international conference on management of data (pp. 1917-1923).

Jeble, S., Kumari, S., & Patil, Y. (2017). Role of big data in decision making. Operations and Supply Chain Management: An International Journal, 11(1), 36-44.

Fernández-Manzano, E. P., Neira, E., & Clares-Gavilán, J. (2016). Data management in audiovisual business: Netflix as a case study. El profesional de la información (EPI), 25(4), 568-576.

Alhyasat, E. B., & Al-Dalahmeh, M. (2013). Data warehouse success and strategic oriented business intelligence: a theoretical framework. arXiv preprint arXiv:1307.7328.

Dobbs, T., Stone, M., & Abbott, J. (2002). UK data warehousing and business intelligence implementation. Qualitative Market Research: An International Journal.

Golfarelli, M., Rizzi, S., & Cella, I. (2004, November). Beyond data warehousing: what's next in business intelligence? In Proceedings of the 7th ACM international workshop on Data warehousing and OLAP (pp. 1-6).

Zhou, H., Yang, D., & Xu, Y. (2011). An ETL strategy for real-time data warehouse. In Practical applications of intelligent systems (pp. 329-336). Springer, Berlin, Heidelberg

Inmon, W. H. (1996). The data warehouse and data mining. Communications of the ACM, 39(11), 49-51.

Hasselbring, W. (2000). Information system integration. Communications of the ACM, 43(6), 32-38.

Issa, C. M. (2002). Data warehouse applications in modern day business.

Al-Debei, M. M. (2011). Data warehouse as a backbone for business intelligence: Issues and challenges. European Journal of Economics, Finance and Administrative Sciences, 33(1), 153-166.

J. Stuller, "Inconsistencies in data warehousing," Proceedings 1999 International Symposium on Database Applications in Non-Traditional Environments (DANTE'99) (Cat. No.PR00496), 1999, pp. 43-50, doi: 10.1109/DANTE.1999.844940.

Bouadi, T., Cordier, M.O., Moreau, P., Quiniou, R., Salmon-Monviola, J. and Gascuel-Odoux, C., 2017. A data warehouse to explore multidimensional simulated data from a spatially distributed agro-hydrological model to improve catchment nitrogen management. Environmental modelling & software, 97, pp.229-242.

Kortüm, K.U., Müller, M., Kern, C., Babenko, A., Mayer, W.J., Kampik, A., Kreutzer, T.C., Priglinger, S. and Hirneiss, C., 2017. Using electronic health records to build an ophthalmologic data warehouse and visualize patients' data. American journal of ophthalmology, 178, pp.84-93.

Madurapperuma, S., Ebert, L. and Kuruppuarachchi, D., 2018. In-house development & implementation of ‘corebrain’warehouse management system: a case study. In Proceedings of the 2nd International Conference in Technology Management, iNCOTeM (pp. 67-72).

Seneviratne, M.G., Seto, T., Blayney, D.W., Brooks, J.D. and Hernandez-Boussard, T., 2018. Architecture and implementation of a clinical research data warehouse for prostate cancer. eGEMs, 6(1).

Ariyachandra, T. and Watson, H., 2010. Key organizational factors in data warehouse architecture selection. Decision support systems, 49(2), pp.200-212.

Raza, B., Aslam, A., Sher, A., Malik, A.K. and Faheem, M., 2020. Autonomic performance prediction framework for data warehouse queries using lazy learning approach. Applied Soft Computing, 91, p.106216.

Sylvestre, E., Bouzillé, G., Chazard, E., His-Mahier, C., Riou, C. and Cuggia, M., 2018. Combining information from a clinical data warehouse and a pharmaceutical database to generate a framework to detect comorbidities in electronic health records. BMC medical informatics and decision making, 18(1), pp.1-8.

Köksal, Ö. and Tekinerdogan, B., 2019. Architecture design approach for IoT-based farm management information systems. Precision Agriculture, 20(5), pp.926-958.

VORONKOVA, O.V., KUROCHKINA, A.A., FIROVA, I.P. and BIKEZINA, T.V., 2017. Implementation of an information management system for industrial enterprise resource planning. Revista Espacios, 38(49).

De Mul, M., Alons, P., Van der Velde, P., Konings, I., Bakker, J. and Hazelzet, J., 2012. Development of a clinical data warehouse from an intensive care clinical information system. Computer methods and programs in biomedicine, 105(1), pp.22-30.

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