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Panel Presentation Academic Reporting and Data Analysis

Learn about data warehousing concepts, transitioning to production, and the latest tools available. Understand the benefits of a data warehouse and how it improves access to information for users and institutions.

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Panel Presentation Academic Reporting and Data Analysis

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  1. Panel PresentationAcademic Reporting and Data Analysis Gerry Dizinno St. Mary’s University Debbie Head Kennesaw State University Susan Nies IntelliSolve Group, Inc. Shaun Williams IntelliSolve Group, Inc.

  2. Agenda • Overview of Data Warehousing concepts and terminology • Transitioning from Concept to Reality • Issues now that the data warehouse is in production • Some latest tools available and the old standards

  3. Differences Between a Data Warehouse and Simple Extractions More extensive data Controlled environment for data validation Pre-determined timing for the extracts Software to view/manipulate data

  4. Current Environment Facts • There is a wealth of information (data) in the transactional system • Users are knowledgeable about the data • The challenge is that the users cannot get access to the data! • The missing piece is the technology to leverage the information and user knowledge

  5. User Expectations • Access and Tools to analyze and report. Easy access to both current and historical information (for comparison purposes) • Data should be easily accessible and easily navigable through simple software tools • Results of queries and reports should be easily understandable • Data should be available regularly and reliably

  6. Institution’s Expectations • Continue to improve the institution, for example through better student retention and student success • Respond to requests for information from inside and outside the institution

  7. The Benefits of a Data Warehouse • Faster response to requests for information • Consistent result to queries • Minimal data processing skills required • Maximizes your ability to understand your institution • Ability to Access Historical Data regardless of time period changes

  8. The IntelliSolve Data Warehouse Model • Limit scope to target deliverable in 90 - 120 days • Keeps momentum and interest high • Results can be put into action quickly • Design for long term solution • Implementation plan should include incremental deliverables • Use off-the-shelf tools wherever practical • Don’t reinvent the wheel

  9. Data Base Design for a Transaction System

  10. Data Warehouse Design TIME_KEY DAY_OF_WK WK_OF_MONTH MONT_OF_YR DAY MONTH YEAR ETC. STUDENT_KEY STUDENT_ID STUDENT_SSN STUDENT_CASS STUDENT_STATUS STUDENT_ETHNICITY ETC. TIME_KEY STUDENT_KEY FACULTY_KEY COURSE_KEY FINANCIAL_AID_KEY TERM_KEY LOCATION_KEY FTE_COUNT HEAD_COUNT ETC. FACULTY_KEY FACULTY_ID FACULTY_NAME FACULTY_HIRE_DATE FACULTY_COURSE_ID FACULTY_GROUP ETC. TERM_KEY TERM_ID TERM_DESC TERM_NOTE ETC. COURSE_KEY COURSE_ID COURSE_DESC COURSE_GROUP ETC. LOCATION_KEY LOC_BLD_ID LOC_ROOM_ID LOC_CAPACITY ETC.

  11. Warehouse KENNESAW Where we are and how we got there…

  12. The KSU Story In Brief • Student System was the data jail and dictator • Created stressful times in the functional offices • Could not get (reliable) data during the first few terms

  13. Short History at KSU New HR Relational System 3-years to realize what we needed New Relational Student System Unexpected Politics OR What’s in it for ME? Legacy Financials Untold Number of Shadow Systems

  14. The Politics or “What’s in for me?” syndrome New CIO Bought into the idea in theory De facto small Data Warehousing Team Established IntelliSolve interviewed 50+ users Resistance encountered with ownership/protectiveness issues Excitement generated but now cooled

  15. Politics or “What’s in it for me?” (continued) • Overcoming (or at least dealing) with the politics: • Link the project to institutional and administrators’ priorities • Presenting something to the one VP who needs something and can’t get it! (In process) • Promising portal to VPs (CIO’s request) • Addressing some attitudes that all data are dirty so no warehouse will ever clean it up

  16. Benefits Consistency Standardized data sources Reliable numbers from numerous sources Enable users to get their own answers Transactional System protected from queries Thinking about the DW • Costs • Length of time to see results of any kind • Intensity of personnel to understand, clean and manage the data • Education of the users • Actual dollars in tight financial times

  17. Where We Started or Wrong Assumptions • Assumed key administrators would be supportive of effort since it meant quicker responses • Thought the technical staff would appreciate the concept of static data • Did not want to assume responsibility for data extraction • Did not have a thorough understanding of the new relational table structure or fields

  18. Lessons Learned? • Illustrate key questions (priority of the moment) that had to go unanswered for a time • Educate technical staff on the importance of consistent data…census date • Change our attitude (earlier) about client/server environment • Use incremental solutions - little steps at a time

  19. St. Mary’s University Data Warehouse Project

  20. What They Don't Tell You (at least very loudly) in "Data Warehouse School" • Maintenance Needs • Consistency Needs • Training Needs

  21. Data Warehouse Lifespan • Portability • Source data can change or additional sources • Target database can change • Information access tools can change • Scalability • Start small, but plan for orders of magnitude more data and users • Reliability • Ensure repeatable processes

  22. Maintenance and Consistency Needs • Prior to each extract: accuracy checks • After each extract: checks for consistency, calculations • Modifications

  23. The Cycle 2. Process 3. Technology 1. People

  24. Retention Cube Built from a Data Warehouse • Enables cohort analysis • Easily allows looking at the cohort by different demographic and other characteristics

  25. The Solution • OLAP Cubes • Reporting Tools • Data Mining tools

  26. Sources of Information • Adamson, Christopher and Venerable, Michael, Data Warehouse Design Solutions, 1998 • Brobst, Klauer, and, Poe, Building A Data Warehouse for Decision Support, 1998 • Kimball, Ralph, The Data Warehouse Toolkit, 1996 • www.IntelliSolve.com

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