2. Dirty Data Peep Show Implementing a Small Data Warehouse
to Jumpstart Data Management
Diane Muntal, Mike Ellison UNLV Office of Institutional Analysis
& Planning
AIR 2004 Forum, Boston
May 31, 2004

3. Overview How it all started Data vs. Technology Project Goals
Data and Security
Guiding Principles
Demonstration
Strategic Decisions
Implications for Future

4. How it all started
Historically, administrative systems developed at UNLV to address single operations
Legacy systems were primary repositories of campus data
Data management was decentralized
Data management was focused on the priorities of individual operations

5. Data Management Demand increased for management information
Applied new hardware and software to address need for information
Projects sidetracked
Inadequate documentation of the data
Inconsistencies with the data

6. Data vs. Technology Common Assumption Reality
Introducing technologies to existing processes will make things better
Reality
“Paving over the cow path” seldom works
I could
have told you that.

7. Data vs. Technology
“The most important thing isn’t the technology; it’s rethinking and redesigning your business.” - Susan Bostrom, Senior Vice President, Cisco Systems

8. Data vs. Technology
“Buying computers and software is easy; rethinking and redesigning the way we work to take full advantage of them is not.” - David Wessel, Wall Street Journal, Feb. 2001

9. Improving the Data Address data integrity
Create taxonomy of academic and administrative activities
Provide greater access to data

10. Data Access
“Making data more accessible also serves to improve data quality over time. As people use the data, errors can be corrected as they are found.” - Scott Thorne, MIT

11. How We Provided Access Created a data warehouse that would:
Extend our capacity to provide management information to campus employees
Allow us to monitor data for accuracy and consistency
Allow us make recommendations for improvements to accuracy and consistency

12. Project Goals Construct specific set of data
Provide a readily accessible location
Use understandable names and definitions
Provide easy-to-use tools
Create a simple and fast approval process
Provide secure access

13. Guiding Principles Open Access
You need it to do your job
User-friendly Technology (criteria forms)
Well-informed by focus groups
Data Dictionary
All elements defined; look-up tables
User Responsibility
Had to encourage communication

14. About the Data… What are its attributes? Census data Unedited
Point in time snapshot useful for trend analysis
Term data
Unedited
What’s on SIS is what you’ll see

15. Official, core student data system
Stores student transactions
Clients access the DataNet web application to run reports
Extracts from SIS populate the IAP Data Warehouse
Census vs. Production Data
Accessed by DataNet web application

16. Introducing…

17. Security E.A.P. Enthusiastic Data Users Visit IAP Home Page to
FINALLY, AN EASIER WAY TO GET THE DATA I NEED! I MUST REQUEST ACCESS TO DATANET…
WOW! THIS MANDATORY DATANET TRAINING IS TOTALLY WORTH THE $25!
Certificate ed to User
Security
Data Steward (Associate Registrar)
Data User requesting access
Supervising Dean/Director
E.A.P.
Enthusiastic Data
Users Visit IAP
Home Page to
Initiate Electronic
Approval Process
(E.A.P.)
Certificate Account Created
Electronic Form routed back to IAP

18. Strategic Decisions Get a wide range of support
Get input at strategic intervals
Get feedback from users
Share praise

19. Implications for the Future
“Using the data for more purposes also helps improve the design of future systems.”
-Scott Thorne, MIT

20. Implications… Provides access to levels that “know” the data
Creates allies in data management
Develops a cadre of informed users
Opens the door to pending initiatives
Creates new opportunities

21. Lessons the Elbonians Taught Us

22. Thank You

23. Questions?