Data to Information Historically

1. What is Management Reporting from a Data Warehouse and What Does It Have to Do with Institutional Research?Emily ThomasStony Brook UniversityAIRPO Winter WorkshopJanuary 2006

2. Data to Information Historically

3. Data to InformationCurrent Technology

4. Hypothesis • New information technologies are creating greater demand for information and the need for data warehouses. • Building a data warehouse requires transforming raw data into reporting measures and categories. • Meeting the demand for information requires creating useful reports and report templates. • Institutional researchers are experts at displaying information and constructing reporting variables. • Therefore participating in the development of institutional reporting programs is a new role for institutional research.

5. What is a data warehouse? A data warehouse is a subject oriented, integrated, non-volatile, and time variant collection of data in support of management’s decisi`ons. (W.H. Inmon, Building the Data Warehouse)

6. What is a data warehouse? A data warehouse is a subject oriented, integrated, non-volatile, and time variant collection of data to describe an organization’s activities and support of management’s decisions.

7. What is a data warehouse? A data warehouse is a subject oriented, integrated, non-volatile, and time variant collection of derived data to describe an organization’s activities and support of management’s decisions.

8. What is a data warehouse? A data warehouse is a subject oriented, integrated, non-volatile, and time variant collection of derived data that are managed and institutionally recognized as a shared data resource used to describe an organization’s activities and support of management’s decisions.

9. “There is no question that the poweruser is the most important person in the corporation in regard to establishment of the data warehouse and the unleashing of the power of informational processing.” (Inmon 1994,116)

10. Reporting Categories • Operations monitoring: Which students are ready to be cleared for graduation? • Operations analysis: Which students were affected by an error in processing graduation clearance? • Management reporting: How many students graduated in each major in each of the last five years? • Management analysis: Did the graduation GPA of recent graduates vary with whether they entered as freshmen or transfer students? • Analytics: Did a new freshman program improve the graduation rate?

11. Reporting Matrix

12. Trends • Information culture and data availability generate increased demand for information. • Web-based report delivery and user-friendly tools facilitate self-service reporting. • Increased reporting generates interest in institution-wide reporting solutions. • New transaction systems add data complexity that motivates warehousing.

13. New Roles for Institutional Research • New responsibilities for designing and implementing disseminated management reporting systems • New responsibilities for shared data designs including data warehouses • New means of ensuring the accuracy of management information: within the data source • Less staff time devoted to meeting simple data requests • New relationships with IT

14. IR and IT

15. Institutional Research Contributions • Assessing reporting needs • Advocating for new forms of information delivery • Defining an institutional reporting strategy/program • Defining warehouse variables and table structure • Selecting an institutional reporting tool • Designing standard reports or templates • Managing a management information delivery system

16. www.stonybrook.edu/HEDW

17. Two Types of Best Practice? (1) Fully-developed data warehouse • Core of an institutional reporting program • Source for all or most reporting • Well-developed data model • Fully defined and documented data management procedures • Substantial institutional commitment and staff http://web.mit.edu/warehouse/

18. Two Types of Best Practice? (2) Pragmatic low-budget approach • Build something. • Identify the data needed to meet key reporting needs • Create tables to meet those needs • Clean, expand, integrate, and document the tables and extend their use

19. Courtesy of Henry Stewart

20. Sources The Data Warehousing Institute. http://tdwi.org Davenport, TH (1997). Information Ecology: Why Technology is Not Enough for Success in the Information Age. New York and Oxford: Oxford University Press. Greenfield, L (1995). The Data Warehousing Information Center. www.dwinfocenter.org. Inmon, WH (1996). Building the Data Warehouse. New York: John Wiley & Sons, Inc. Inmon WH and RD Hackathorn (1994). Using the Data Warehouse. New York: John Wiley & Sons, Inc.

21. Sources Kimball, R, M Ross and W Thornthwaite (1998). The Data Warehouse Lifecycle Toolkit: Expert Methods for Designing, Developing, and Deploying Data Warehouses. New York: John Wiley & Sons, Inc. Kimball, R and M Ross (2002). The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling (second edition). New York: John Wiley & Sons, Inc. Sanders, L (editor), How Technology is Changing Institutional Research. New Directions in Institutional Research, 103. Fall 1999. Serban, AM and J Luan. Knowledge Management: Building a Competitive Advantage in Higher Education. New Directions in Institutional Research, 113, Spring 2002. Wierschem, D, R McBroom and J McMillen. Methodology for Developing an Institutional Data Warehouse. AIR Professional File 88, 2003.