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Enterprise Data Management:. Where We’ve Been and Where We’re Headed Cindy Walker WalkerBurr, Inc. Agenda. Introductions What Has History Taught Us? Historical Business and Technology Trends Data Management Trends and Lessons Learned What Does the Future Hold?

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enterprise data management

Enterprise Data Management:

Where We’ve Been and

Where We’re Headed

Cindy Walker

WalkerBurr, Inc.

agenda
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
agenda3
Agenda
  • Introductions
enterprise data managers who are we
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.
enterprise data management principles
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.
quick survey please take 1 minute to jot down your answers
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?
quick survey these are my answers
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
agenda8
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
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

Historical Trends (1980-2000)

Business

Technology

major government milestones
Major Government Milestones
  • ITMRA (CIO Act)
  • GPRA
  • E-GOV (President Clinton’s Memo)
electronic government
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….

data management trends 1980 s goal 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

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:

data management trends 1990 s goal 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

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:

where are we now
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”)
lessons learned
Lessons Learned
  • EVERYONE in the enterprise shares responsibility and accountability for enterprise data management.
definition of enterprise data management

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

organizational model for enterprise data management

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

organizational model for enterprise data management19

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

lessons learned20
Lessons Learned

Get the enterprise perspective into the analysis process EARLY!

lessons learned21
Lessons Learned
  • Technology is NOT the solution to our enterprise data management problems.
lessons learned22
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!

lessons learned23
Lessons Learned
  • Human behavior changes much more slowly than technology advances. Significant human behavior modification is required to succeed at enterprise data management.
lessons learned24
Lessons Learned
  • Nothing is more critical than a well-articulated business vision represented through enterprise business, data, application, and technology architectures.
agenda25
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
slide27

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

digital tower of babel
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)

our challenge for the new millennium
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.