Westward Ho: New Frontiers

1. Westward Ho: New Frontiers USG Academic Data Mart Project Update Georgia Summit (September 8-10, 2004) Savannah, GA Presented by: Charles Gilbreath (GSU) and Debbie Head (KSU)

2. What A Data Warehouse Is Not! • Transactional systems are designed to respond rapidly to individual events such as registering for a course, paying fees, etc. • Transactional structure is highly normalized (broken into many small pieces) • Transactional systems are not designed for queries

3. What a Data Warehouse Is! • A data warehouse is a set of tables that are designed to respond quickly to queries. • They are denormalized (data may be repeated within a table) • They are designed to store history. • They are designed to bring pieces together from different transactional systems, such as Student Information, Human Resources, Facilities, etc. • They may contain multiple “data marts” that store related data

4. USG Academic Data Mart (ADM) Defined • The USG Academic data mart is designed to incorporate the institutional data from the legacy systems of SIRS, CIR, FARS, RUR, Graduate Salary Survey, High School Feedback, and Learning Support/Core Curriculum. • The data collected in the Academic Data Mart can be used by institutions for both local and official reporting needs.

5. ADM in simple terms…. • Final objective of Enterprise-wide data warehouse will be a hybrid of old reporting systems (SIRS, CIR ,etc) with new data structures that will consider institutional needs • Transactional systems (Banner) will feed the data directly to the warehouse. • Data elements are arranged in tables in a database managed by OIIT. Data structures reflect institutional needs.

6. ADM expectations • The data fields were selected initially based on the data fed to SIRS and CIR. • It will expand beyond those elements when it proves its functionality • Still working on how to load longitudinal data • Canned reports, SER for example, will be available • Sharing of reports generated by others in our group so you won’t have to “recreate the wheel” each time.

7. Why Does IRP Care about the ADM? • For institutions with limited resources, people and equipment, they can access their own data to do internal analyses as desired. • Brings the USG more in line with the current technology in terms of housing and using data • Takes the “data jail” concept and lets us actually get some data out • Hopefully brings some consistency and understanding about what goes into reporting • Reporting should become easier. • Data warehouse tables should match production tables

8. Data Warehouse Structure • ERD – Entity Relationship Diagrams show the main table (FACT table) and how other tables (Dimensions) connect to the main table. It is a detailed scheme of the many elements within each component • Find these at this link: http://www.usg.edu/usgweb/sitcap/usg123_aca/index.phtml?id=pmd/pmd_br

9. What data are accessible? • There are 5 different data components of the ADM organized into “data marts” that are collections of associated data: • Class Session – (Class schedule/catalog) • Student Profile – (Demographics) • Course Enrollment – (Registrations) • Student Term Enrollment – (Student Record) • Student Test Results – (Test scores)

10. Class Session • Class Session data are extracted from Banner • Includes “course catalog” data such as Course number, section, times and days offered, credit hour value of the course • Does not include credit hours generated or number of students enrolled

11. Student Profile • This data mart will provide much of SIRS data. • Includes many of the SIRS data fields • You can access and report and clean the data prior to releasing it to OIIT. • Editing reports should let us “scrub” it better before it goes into the “official” warehouse

12. Student Course Enrollment • Will load some of CIR enrollment data • Will be the source for Credit Hour Production Reports by the USG. • Will link to the Class Session Component so that individual student enrollment information can be accessed

13. Student Term Enrollment • Contains data on each student enrolled in one or more courses in an academic term • Contains cumulative data for each student • Is linked to demographic, geographic, etc. data for each student.

14. Student Test Results • Contains information on detail level of test results as recorded in Banner • Allows selection on individual test types (ACT, SATV, SATM, etc.) • Allows selection by student characteristics (ethnicity, sex, etc.)

15. Getting Data Back Out • Business Objects – pre-selected sets of data • What makes sense in terms of types of information we (IRP) need to know? • For example: A predefined First-time Full-time Freshmen grouping so average SAT, gpas, ages, ethnicity, gender, CPC, LSP could be gathered just about that group? • What else?

16. Process for Meeting IRP’s needs • Identify data needs and generate list of desired reports • Timetable for us and OIIT • What is review process for requests of reports? (Does IRP recommend a standing data warehouse committee?)

17. Standing Data Warehouse Committee • Identify data needs and pass on to report developers (Some developers may be OIIT and some may be IRP members) • Facilitate sharing reports • Develop a process for recommending changes to data warehouse structure • Members reflect data warehouse user community

18. Finding Information About What is in the ADM! http://www.usg.edu/usgweb/sitcap/

19. ERD Web site

20. Using Discoverer • Reporting tool provided by OIIT • Administered at system level • Allows us to see our own institutional data • Can build our own ad hoc reports • If a report that would be beneficial to all, submit it to committee for review and approval to be put in the master list of available reports

21. Round the campfire • Questions, comments, suggestions • Meet the trail bosses of the ADM: • Lori Jarrard • Glenn Fernandez