Dirty Data Peep Show Implementing a Small Data

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

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

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

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

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

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

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

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

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

How We Provided Access £ Created n n n 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

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

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

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

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

Introducing…

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

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

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

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

Lessons the Elbonians Taught Us

Thank You

Questions?