1. Jeremy Brinkman Director of Administrative Systems University of Northwestern Ohio jbrinkman@unoh.edu Great Lakes Users’ Group Conference August 10-11, 2009

2.  Reporting Evolution  Why a Data Warehouse?  Planning  Technologies Used  Design  Implementation  Challenges  Resources

3.  Legacy ERP System ◦ (1981-2005) ◦ Transactional reporting  Migration to Datatel Colleague / SQL ◦ (2005-Current) ◦ Transactional reporting ◦ Snapshot Reporting  Colleague and the Data Warehouse ◦ (2007-Current) ◦ Transactional reporting ◦ Snapshot reporting ◦ Point-in-time data reporting ◦ Ad-hoc reporting

4.  Departments can produce their own reports ◦ Empowers end users ◦ Reduces the burden on IT  Capture point-in-time data ◦ Daily financial standing ◦ Daily financial aid award packaging status ◦ Active student programs in a term  Reduce performance hit on transactional database server  Simplify reporting ◦ SQL version of the Colleague database has 3,000+ tables!  Combine disparate data sources “A lot of times, people don't know what they want until you show it to them.” – Steve Jobs in BusinessWeek, May 25 1998

5. SELECT DISTINCT B.STC_PERSON_ID FROM TERMS A INNER JOIN STUDENT_ACAD_CRED B ON A.TERMS_ID=B.STC_TERM INNER JOIN STC_STATUSES C ON B.STUDENT_ACAD_CRED_ID=C.STUDENT_ACAD_CRED_ID AND C.POS=1 INNER JOIN STUDENTS_LS D ON B.STC_PERSON_ID=D.STUDENTS_ID AND D.STU_ACAD_PROGRAMS IS NOT NULL INNER JOIN STPR_DATES E ON (D.STUDENTS_ID+'*'+D.STU_ACAD_PROGRAMS)=E.STUDENT_PROGRAMS_ID WHERE A.TERMS_ID='2008FAL' AND C.STC_STATUS NOT IN ('X','C','D') AND E.STPR_START_DATE <=A.TERM_END_DATE AND (E.STPR_END_DATE IS NULL OR E.STPR_END_DATE >= A.TERM_START_DATE) ORDER BY B.STC_PERSON_ID SELECT DISTINCT STUDENT_ID FROM STUDENT_TERM_PROGRAM A INNER JOIN STUDENT_TERM B ON A.STUDENT_TERM_KEY=B.STUDENT_TERM_KEY WHERE B.TERM_CODE='2008FAL' AND B.ATTENDED_IND='Y' ORDER BY STUDENT_ID Colleague SQL Query (5 tables required, complex joins) Same Query in the Data Warehouse (2 tables required, simple joins)

6.  Meet with the departments with the most ad-hoc data requests ◦ Financial Aid ◦ Admissions ◦ Registration and Advising  Review existing reports for KPIs  Involve the decision makers  Leverage your tech-savvy users to help drive adoption

7.  Star vs. Snowflake Schema  Fact and Dimension tables  Metadata  Conformed Dimensions  Type One, Two, and Three Slowly Changing Dimensions  Measures  Key Performance Indicators (KPIs)  Inmon Model vs. Kimball Model

8.  Datatel Colleague Release 18  SQL Server 2005  Business Intelligence Development Studio (BIDS) ◦ To develop the data load processes  SQL Server Integration Services (SSIS) ◦ To move data from Colleague to the warehouse ◦ Datatel Data Orchestrator ODS  We started before ODS was an option, so we kept SSIS.  ODS is a nice tool to get the data from Colleague into SQL, especially for UniData shops  SQL Server Management Studio (SSMS)  Business Objects Enterprise XI R2  Web Intelligence (WEBI) ◦ Ad-hoc report development  Data Modeling Software ◦ ERWin ◦ Power Architect

9.  Uppercase naming in the database  Yes/No fields end in _IND  Coded fields end in _CODE  Description fields end in _DESC  Date fields end in _DATE  All tables will have a unique key field that identifies the record. ◦ These fields will include the table name and end in _KEY  Use descriptive names for tables and fields

10.  Star Schema Used  Avoid the Snowflake!  Central Fact Table  Supporting Dimension Tables  Financial Aid Awards by Term Example

12.  Identify data that will be shared among many subject areas in the warehouse (conformed dimensions) ◦ Person Bio/Demo ◦ Academic Term ◦ Student  Build your dimensions with the assumption that they will be used with other subject areas in the warehouse  Start with a few basic, useful fields per dimension  Focus on one department first ◦ Financial Aid was a good starting point for UNOH because they requested the most ad-hoc data and had more tech-savvy users  Focus on one subject area to produce a quick “win” ◦ Financial Aid Awards by Term ◦ These users will be your evangelists!

13.  SQL Server Integration Services (SSIS) moves the data from the Colleague database to the warehouse  Data is loaded from multiple sources  Datatel ODS can also be used as an intermediate data store  Load the dimension tables first, then the fact tables

14. Load a fact table Load a dimension table

15.  Centralized reporting system  Web Intelligence provides ad hoc reporting from the data warehouse  Subject-based data is organized into Universes ◦ Universes store that metadata for the fields in the data warehouse  The Universes add a user-friendly layer to the reporting model

16.  Identify the data subject area  Design and build the dimension tables ◦ Data modeling software  Design and build the fact table ◦ Data modeling software  Build the ETL package and load the tables ◦ Business Intelligence Development Studio ◦ SQL Server Integration Services  Build the Business Objects Universe ◦ Business Objects Designer  Publish the Universe

17.  WEBI Demo WEBI Demo

18.  Getting users to understand the concept of a data warehouse  Learning the technical concepts ◦ We are still learning!  Determining where to place the data ◦ Fact or dimension table  Finding educational data warehouse examples ◦ Business uses a Time dimension, education uses Term (in most cases)

19.  The Data Warehouse Toolkit: Practical Techniques for Building Dimensional Data Warehouses ◦ by Ralph Kimball ◦ ISBN: 0-471-15337-0  Other Campus Data Warehouse Sites  Google

21. Jeremy Brinkman Director of Administrative Systems University of Northwestern Ohio jbrinkman@unoh.edu Great Lakes Users’ Group Conference August 10-11, 2009