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

 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

 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

 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.

 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.

 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.

 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

 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

 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)

 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

 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

 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

 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.

 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.)

 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 Fulltime Freshmen grouping so average SAT, gpas, ages, ethnicity, gender, CPC, LSP could be gathered just about that group?
 What else?

 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?)

 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

http://www.usg.edu/usgweb/sitcap/

 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

 Questions, comments, suggestions
 Meet the trail bosses of the ADM:
 Lori Jarrard
 Glenn Fernandez