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Enterprise Data Management:Where We’ve Been and

Where We’re HeadedCindy Walker

WalkerBurr, Inc.

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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

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Agenda

Introductions

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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.

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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.

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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?

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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

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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

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Historical Trends (1980-2000)

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

Business Technology

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Major Government Milestones

ITMRA (CIO Act) GPRA E-GOV (President

Clinton’s Memo)

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Electronic GovernmentTHE WHITE HOUSE

Office of the Press Secretary ________________________________________________________________________

For Immediate Release December 17, 1999December 17, 1999MEMORANDUM FOR THE HEADS OF EXECUTIVE DEPARTMENTS AND AGENCIESSUBJECT: Electronic GovernmentMy 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….

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Data Management Trends:1980’sGoal: Right Data to Right Person at Right Time

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

What We Were Trying to Do: How We Were Trying to Do It:

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Data Management Trends:1990’sGoal: Right Data to Right Person at Right Time

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

What We Were Trying to Do: How We Were Trying to Do It:

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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”)

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Lessons Learned - The “Duhs”

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Lessons Learned

EVERYONE in the enterprise shares responsibility and accountability for enterprise data management.

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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.

Gather,Create

Organize,Store

Select, Synthesize

Distribute

Corporate Data Life Cycle

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Organizational Model for Enterprise Data Management

Gather/Create Organize

Select, SynthesizeDistribute

Information Consumers

Business Units IRMDataAnalysts,DBA’s

Systems Analysts,Application Developers

Information Producers

?

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Organizational Model for Enterprise Data Management

Information Definers

Information Policymakers

IRM

DataAdministrators

Resolve Data ConflictsDefine Data Policy

Define Data/Establish Data Sensitivity Levels

Set Data Quality Standards/Assess DQ

DataAdministrators

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Lessons Learned

Get the enterprise perspective into the analysis process EARLY!

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Lessons Learned

Technology is NOT the solution to our enterprise data management problems.

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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!

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Lessons Learned

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

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Lessons Learned

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

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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

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E-Commerce: Catalyst for Enterprise Data Management

BAD DAT

A

B2BB2CB2G

G2C

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Data Warehouse(s)

DataMart

DataMart

Data Warehouse(s)

E-CommerceData

Source(s)

DataMart

A1

A2

A3Pubs

DataMart

B2

B3

B4

B1

C

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Digital Tower of Babel

Semantic layer (the data meaning)

Context layer (where and how used)

Logical layer (basic data attributes)

Physical layer (hardware)

“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.

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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.