Scalable Data Warehouse Architecture: A Higher Education Case Study

Scalable Data Warehouse Architecture: A Higher Education Case Study

Dennis C. Guster (St. Cloud State University, USA), Christopher G. Brown (St. Cloud State University, USA) and Erich P. Rice (St. Cloud State University, USA)
DOI: 10.4018/978-1-5225-3142-5.ch013
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This chapter looks at the feasibility of creating a scalable data warehouse architecture in a higher education institution. The authors lay out the background of the historical data environment of the institution and look at ways in which the application of new technologies could better meet and exceed the needs of the institution moving forward. The chapter also covers the increased role security plays in the management and governance of data and the ways in which developing more secure aware employees through the use of People Centric Security (PCS) can reduce risk and drive positive change. The authors then look at the ten steps to create a better data framework which will allow for enhanced analytics and a greater return on investment.
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[Dr. William Edwards Deming] illustrated the difference between efficiency and effectiveness with a story about the Empire buggy-whip manufacturing company, which at the turn of the century was the best buggy-whip manufacturer of all time. Every buggy-whip they made was engineered to specification; they rarely broke, and all grievances were promptly resolved to the customer’s satisfaction. In terms of efficiency, they were among the best. The problem, he said, was that they did not have a view of the future. They were in the transportation business and did not see the coming of the horseless carriage. In ten years they were out of business because they did not know the difference between effectiveness, or doing the right things, and efficiency— doing the right things right. (Voehl, 1995)

Today’s technology enriched academic environment elicits high demands for technology provisioning and support. Weldon (2015a) states college Information Technology (IT) departments are not built for future needs. In addition, the technology demands in higher education are increasing faster than IT departments can keep up. The process of building for current demands is no longer sufficient. Even agile approaches that do not address and provision for future demands incur increasing technology support, operational and redesign costs. These “solutions” may satisfy current expectations at the expense of future costs and resource demands. Weldon (2015a) also cites a report from Babson College stating the only way to increase user satisfaction while keeping the lights on and costs down is through innovation.

Weldon (2015a) further cites Michael Kubit’s examples of changes that IT campus departments need to embrace:

Table 1.
Current and future states
Knowledge hoardingKnowledge sharing
Ad hoc trainingContinuous training
Many management levelsFew management levels
Inflated titlesFew titles
Uneven responsibilitiesShared responsibility
Culture of blameCulture of accountability
Functional silosCross-functional teams
Risk averseEntrepreneurial
Information on an as-needed basisShared information
Climate of cynicismClimate of celebration

(Derived from the material provided by Weldon (Weldon, 2015a))

Key Terms in this Chapter

Agile Development: A method of software or system development that utilizes collaboration and team decisions while utilizing continuous planning, testing and integration.

Data Governance: Data governance refers to the overall management of the availability, usability, integrity and security of the data employed in an enterprise. Sound data governance programs include a governing body or council, a defined set of procedures and a standard operating procedure.

Extract Transform Load (ETL): Data warehouse process used to extract data from disparate or homogeneous data sources. Transforms the data to align with data structure for storing and analysis.

Learning Analytics: Understanding and optimizing learning and learning environments through the measurement, collection, analysis and reporting of data about learners and learner contexts.

Data Analytics: Is the science of examining raw data with the purpose of drawing actionable information from it, data analytics is used to allow companies and organization to make better business decisions and in the sciences to verify or disprove existing theories.

Data Warehouse: A data warehouse (DW or DWH), also known as an enterprise data warehouse (EDW), is a system used for reporting and data analysis. They are central repositories of integrated data from one or more disparate sources.

Identity Management (IDM): Is a broad administrative area that deals with identifying individuals in a system and controlling their access to resources within that system by associating user rights and restrictions with the identity.

IT Security Awareness: Is the knowledge and behavior of members of an organization regarding the protection of the physical and especially informational assets of the organization.

Future Proofing: Ensuring high future value to cost ratio by anticipating and minimizing the shock and stress effects of future events.

Virtualization: Is the creation of a virtual (rather than actual) version of something such as an operating system, a server, a storage device or network resources.

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