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