Enterprise Data Management:

1. Enterprise Data Management: Where We’ve Been and Where We’re Headed Cindy Walker WalkerBurr, Inc.

2. Agenda • Introductions • What Has History Taught Us? • Historical Business and Technology Trends • Data Management Trends and Lessons Learned • What Does the Future Hold? • Future Business and Technology Trends • Data Management in the Third Millennium • Discussion

3. Agenda • Introductions

4. Enterprise Data Managers -Who Are We? • We are data administrators, database administrators, business analysts, business managers, data modelers, repository administrators, application developers, senior executives. • We always take the enterprise perspective. • We struggle to make enterprise-wide data sharing a reality. • We want applications to use data designed for sharing across the enterprise.

5. Enterprise Data Management Principles • Data is an enterprise resource that must be managed from an enterprise perspective. • High quality data must be readily accessible by anyone who has a legitimate need. • Organizations are stewards of enterprise data rather than owners of that data.

6. Quick SurveyPlease take 1 minute to jot down your answers. • What was your greatest data management challenge during the 1980’s? • During the 1990’s? • Today?

7. Quick Survey (These are my answers) • 1980’s: • Selling the Benefits/Getting Buy-In • Gaining Consensus • 1990’s: • Selling the Benefits/Getting Buy-In • Gaining Consensus • Today: • Selling the Benefits/Getting Buy-In • Gaining Consensus

8. Agenda • Introductions • What Has History Taught Us? • Historical Business and Technology Trends • Data Management Trends and Lessons Learned • What Does the Future Hold? • Future Business and Technology Trends • Data Management in the Third Millennium • Discussion

9. Total Quality Management Business Process Reengineering Balanced Scorecard Learning Organizations Electronic Data Interchange Knowledge Management E-Business/E-Gov Personal Computers Client/Server Email Data Warehouse/Mining Business Intelligence Tools Y2K Packaged Enterprise Applications Internet XML Historical Trends (1980-2000) Business Technology

10. Major Government Milestones • ITMRA (CIO Act) • GPRA • E-GOV (President Clinton’s Memo)

11. Electronic Government THE WHITE HOUSE Office of the Press Secretary ________________________________________________________________________ For Immediate Release December 17, 1999 December 17, 1999 MEMORANDUM FOR THE HEADS OF EXECUTIVE DEPARTMENTS AND AGENCIES SUBJECT: Electronic Government My Administration has put a wealth of information online. However, when it comes to most Federal services, it can still take a paper form and weeks of processing for something as simple as a change of address. While Government agencies have created "one-stop-shopping" access to information on their agency web sites, these efforts have not uniformly been as helpful as they could be to the average citizen, who first has to know which agency provides the service he or she needs. There has not been sufficient effort to provide Government information by category of information and service -- rather than by agency -- in a way that meets people's needs….

12. Define all data elements from an enterprise perspective (define each data element once) Uniquely define and name each discrete data element Document these data elements names and definitions in a central data dictionary system Map non-standard elements to standard elements Develop Enterprise Data Architecture Develop Subject Area Databases Demonstrate our Value Data Administration Methodologies for Information Engineering Data Naming and Definition Standards Data Dictionary/Directory Systems Zachman Framework for Information Systems Architectures Computer-Aided Software Engineering Tools Broad and “soft” benefit promises Data Management Trends:1980’sGoal: Right Data to Right Person at Right Time What We Were Trying to Do: How We Were Trying to Do It:

13. Define all data elements from an enterprise perspective (define each data element once) Uniquely define and name each discrete data element Document these data elements names and definitions in a central metadata repository system Map non-standard elements to standard elements Develop Enterprise Data Architecture Demonstrate our value Measure and improve Data Quality Develop Data Warehouses Data Administration/Stewardship Data Modeling Techniques (ERD and Star Schema) Data Naming and Definition Standards Metadata Repositories Zachman Framework for Information Systems Architectures Data and Object Modeling Tools DBMS’s and Data Warehouse toolsets ROI, Balanced Scorecards, Broad and “soft” benefits Data Management Trends:1990’sGoal: Right Data to Right Person at Right Time What We Were Trying to Do: How We Were Trying to Do It:

14. Where Are We Now? • “Nearly 25 years have passed since Peter Chen introduced the entity-relationship diagram, yet many data management organizations still struggle for acceptance as a valued partner of any project team.” (Terry Moriarty, “Data Modeling is Dead! Long Live Data Modelers”.) • “Efforts to achieve fully integrated systems, wherein each individual in the enterprise works with the same system and uses various combinations of the same data, have been ongoing for over 25 years.…few have achieved …a fully integrated state.” (Vince Guess, “Data Management and Where To Start”) • “It’s impossible to build a system that predicts who the right person at the right time even is, let alone what constitutes the right information.” (Carol Hildebrand, “Does KM = IT?”) • “What enterprises really want is something like a data warehouse, but much, much more than that.” (Richard Winter, “It’s About Data Integration”)

15. Lessons Learned - The “Duhs”

16. Lessons Learned • EVERYONE in the enterprise shares responsibility and accountability for enterprise data management.

17. Gather,Create Organize, Store Select, Synthesize Distribute Definition of Enterprise Data Management • The application of best practices to manage data and information as valuable enterprise assets. • Data is managed throughout its life cycle with the same rigor and discipline as other assets, including money, people, equipment, and facilities, are managed. Corporate Data Life Cycle

18. Gather/Create Organize Distribute Select, Synthesize Organizational Model for Enterprise Data Management Business Units IRM Information Producers Data Analysts, DBA’s ? Systems Analysts, Application Developers Information Consumers

19. Define Data Policy Resolve Data Conflicts Define Data/Establish Data Sensitivity Levels Set Data Quality Standards/Assess DQ Organizational Model for Enterprise Data Management Information Policymakers IRM Data Administrators Information Definers Data Administrators

20. Lessons Learned Get the enterprise perspective into the analysis process EARLY!

21. Lessons Learned • Technology is NOT the solution to our enterprise data management problems.

22. Lessons Learned • “Long-term success, not methodological orthodoxy, is the measure of analytic methods’ fitness….Data modeling is dead. Long live data modelers!” (Terry Moriarty) Translated: JUST DO IT!

23. Lessons Learned • Human behavior changes much more slowly than technology advances. Significant human behavior modification is required to succeed at enterprise data management.

24. Lessons Learned • Nothing is more critical than a well-articulated business vision represented through enterprise business, data, application, and technology architectures.

25. Agenda • Introductions • What Has History Taught Us? • In the Beginning….. • Historical Business and Technology Trends • Data Management Trends and Lessons Learned • What Does the Future Hold? • Future Business and Technology Trends • Data Management in the Third Millennium • Discussion

26. E-Commerce: Catalyst for Enterprise Data Management BAD DATA B2G B2C G2C B2B

27. B2 B1 Data Warehouse(s) B3 Data Warehouse(s) A1 E-Commerce Data Source(s) B4 Data Mart Data Mart Data Mart A2 Data Mart A3 Pubs C

28. Digital Tower of Babel “B2B e-commerce is the ultimate challenge in program-to-program data sharing…. Where data must be exchanged among partners and competitors, among dissimilar cultures and languages, and among different hardware and software platforms, we’re facing a digital Tower of Babel.” Don Estes, “It’s the Data Stupid!” EAI Journal, September 2000. Semantic layer (the data meaning) Context layer (where and how used) Logical layer (basic data attributes) Physical layer (hardware)

29. Our Challenge for The New Millennium* Goal: Manage Data across the Enterprise. Make it possible to • Quickly and cost-effectively identify and source the data needed to support a new packaged application • Define a given data element once in the enterprise • Know the derivation of a given data element from its root sources • Make business rules about data and have them apply across the enterprise • Invest in some architecture, direction, and set of standards to clean up the mess * Source: Richard Winter, “It’s About Data Integration”, Intelligent Enterprise Magazine, January 1,2000, volume 3, Number 1.