Toward a Comprehensive Data Management Maturity Model DM3

1
Toward a Comprehensive Data Management Maturity
Model (DM3)

• Brett Champlin, MBA, CSP/CCP
• Senior Lecturer, Adjunct Faculty, MSIS MBA
  Programs, Roosevelt University
• Board of Directors, DAMA International
• Board of Directors, ICCP
• Worker Bee, Corporate America

Presented at DAMA St. Louis, July 24, 2003
2
Poor Software Quality Costs US 60 Billion
Annually
3
Cost of Poor Data Quality 600 Billion Annually!
4
Does Data Quality Affect You?

• 75 of companies report significant problems due
  to defective data
• 92 of claims Medicare paid to community health
  centers over one years time were improper or
  highly questionable
• Wrong price data in retail databases costs
  consumers as much as 2.5 billion in overcharges
  annually
• 96,000 IRS tax refund checks were returned as
  undeliverable one year

Source Larry English, Building Data Warehouse
and Business Information Quality
5
Overview

• What Is a Maturity Model
• Comparison of Existing Models for
  Data/Information Maturity
• Toward a Comprehensive DM3
• Questions/Feedback/Discussion

6
SEI-CMM - The Capability Maturity Model

• The Software Engineering Institute was
  established in 1984 at Carnegie Mellon
  University, Pittsburgh
• Principle customer is the Department of Defense
• U.S. Air Force Project 1987
• Method to select software contractors
• Over 5,000 Assessments performed since 1987

7
Maturity Levels
The quality of a software system is governed by
the quality of the process used to develop and
evolve it Watts Humphrey
Focus on process improvement
Process measured and controlled
Process characterized, fairly well understood
Can repeat previously mastered tasks
Unpredictable and poorly controlled
8
Categories Topics

• Organization
• Policy
• Resources
• Oversight
• Communication
• Training
• Project Management
• Planning
• Tracking
• Project Control
• Subcontracting

• Process Management
• Definition
• Execution
• Analysis
• Control
• Technology
• Insertion

9
Actions (Characteristics) by Level
10
Key Process Areas

• Each Level has several KPAs
• Each KPA has Goals
• Each Goal has a set of Activities

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Level 2 KPAs

• Requirements Management
• Software Project Planning
• Software Project Tracking Oversight
• Software Subcontract Management
• Software Quality Assurance
• Software Configuration Management

12
Level 3 KPAs

• Organization Process Focus
• Organization Process Definition
• Training Program
• Integrated Software Management (Project Mgt)
• Software Product Engineering
• Inter-Group Coordination
• Peer Reviews

13
Level 4 KPAs

• Quantitative Process Management(data gathering
  and analysis)
• Quality Management

14
Level 5 KPAs

• Defect Prevention
• Technology Change Management
• Software Process Change Management

15
Productivity Risk
Optimizing
5
Productivity
Managed
4
3
Defined
2
Risk
Repeatable
1
Initial
16
SEI CMM Assessments
SEI Assessments
17
Other Maturity Models

• 120 and counting
• Training MM
• Release MM
• Configuration MM
• Documentation MM
• Business Rules MM
• E-Business
• Broccoli MM

18
Other Maturity Models
By Category
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Data Related Maturity Models

• Data Categories MM (BC Ministry of Forests)
• Data Management Maturity Model (Agosta)
• Data Management Maturity Model (Dravis)
• Data Management Maturity Model (MITRE)
• Data Management Maturity Measurement (Aiken)
• Data Resource Management MM (Champlin)
• Data Warehouse Information Management MM (Ladley)
• Data Warehousing MM (Marco)
• Der Reifegrad des Datamanagements (Schnider)
• Enterprise-Wide Data Management Maturity Model
  (Parker)
• Information Delivery Maturity Model (Computer
  Associates)
• Information Evolution Model (SAS)
• Information Maturity Framework (BC Ministry of
  Trans Hwys)
• Information Quality Maturity Model (English)
• Stages of an Active Data Warehouse (Brobst)
• Stages of Growth (Nolan)

20
Nolans Stages Model

• 1974-79, Dr. Richard L. Nolan published Stages of
  Growth Models in HBR

21
Nolans Stages Model

• Benchmarks

22
Nolans Stages Model
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Mitre Data Management MM

• About 1995, a team led by Burt Parker
• Basically changed the word software to data
  for the KPAs
• Didnt define Goals and Activities
• But, they looked at the larger context
• Information Management
• DM Purpose Model
• DM Normative Model
• DM Utility Model
• DM Roles

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Mitre Data Management MM

• Level 4 KPAs
• Quantitative Process Management(data gathering
  and analysis)
• Quality Management
• Level 5 KPAs
• Defect Prevention
• Technology Change Management
• Process Change Management

• Level 2 KPAs
• Data Requirements Management
• Data Project Planning
• Data Project Tracking Oversight
• Data Contract Management
• Data Quality Assurance
• Data Configuration Management
• Level 3 KPAs
• Organization Process Focus
• Organization Process Definition
• Training Program
• Integrated Data Management (Project Mgt)
• Data Product Engineering
• Inter-Group Coordination
• Peer Reviews

From "Data Management Maturity Model" Burton G.
Parker, et. al., MITRE Software Engineering
Center, McLean, Virginia July 1995 Parker, B.,
Enterprise-wide Data Management Process Maturity
Framework, Handbook of Database Management,
Auerbach, 1999
25
Mitre Data Management MM

• Purpose Model
• Data Access
• Functional Data Integration
• Enterprise Data Integration
• Data Management Infrastructure

From "Data Management Maturity Model" Burton G.
Parker, et. al., MITRE Software Engineering
Center, McLean, Virginia July 1995 Parker, B.,
Enterprise-wide Data Management Process Maturity
Framework, Handbook of Database Management,
Auerbach, 1999
26
Mitre Data Management MM

• Normative Model
• Data Program Management
• Enterprise Data Engineering
• Functional Data Engineering
• Data Operations

From "Data Management Maturity Model" Burton G.
Parker, et. al., MITRE Software Engineering
Center, McLean, Virginia July 1995 Parker, B.,
Enterprise-wide Data Management Process Maturity
Framework, Handbook of Database Management,
Auerbach, 1999
27
Mitre Data Management MM

• Utility Model
• Effectiveness
• Efficiency
• Quality

From "Data Management Maturity Model" Burton G.
Parker, et. al., MITRE Software Engineering
Center, McLean, Virginia July 1995 Parker, B.,
Enterprise-wide Data Management Process Maturity
Framework, Handbook of Database Management,
Auerbach, 1999
28
Mitre - Managements Role
From "Data Management Maturity Model" Burton G.
Parker, et. al., MITRE Software Engineering
Center, McLean, Virginia July 1995 Parker, B.,
Enterprise-wide Data Management Process Maturity
Framework, Handbook of Database Management,
Auerbach, 1999
29
Mitre Data Management MM

• Also looked at Roles
• Mapped Functional Responsibilities and Roles to
  KPAs and Levels

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English Information Quality MMM

• Developed earlier, but published in 1999
• Chapter 13 in his book
• Builds directly from Crosbys model
• Stage 1 Uncertainty (Ad Hoc)
• Stage 2 Awakening (Repeatable)
• Stage 3 Enlightenment (Defined)
• Stage 4 Wisdom (Managed)
• Stage 5 Certainty (Optimizing)

Source Larry English, Building Data Warehouse
and Business Information Quality
31
English Information Quality MMM

• Maps Stages by characteristics against
  Measurement Categories
• Management Understanding and Attitude
• Information Quality Organization Status
• Information Quality Problem Handling
• Cost of Information Quality as of Revenue
• Information Quality Improvement Actions
• Summation of Company Information Quality Posture

Source Larry English, Building Data Warehouse
and Business Information Quality
32
English Information Quality MMM
Source Larry English, Building Data Warehouse
and Business Information Quality
33
English Information Quality MMM

• Moving from Stage 1 to Stage 2 (Awakening)
• Break the gridlock of the status quo. Identify
  problems with poor-quality information.
• Appoint an information quality leader
• Enterprise-wide data resource management function
• Enterprise-wide data standards must exist
• All new application and data development must be
  defining data from a shared, cross-functional
  perspective
• Quality of critical information is being assessed
• Costs of poor information quality are being
  quantified
• Data is being cleaned up so it can be trusted and
  used

Source Larry English, Building Data Warehouse
and Business Information Quality
34
English Information Quality MMM

• Moving from Stage 2 to Stage 3 (Enlightenment)
• Develop personal relationships with sponsors
• Provide formal education to Sr Mgt in IQ
  principles
• Assure the quality of data standards
• Assess quality of next most important data
• Assess costs of non-quality information
• Quantify costs of redundant application and data
  development
• Value-centric data development methodologies
• Information policy implemented by senior
  management
• Information modeling tools used effectively
• Data definition maintained in shared
  repositories, data definition available to
  knowledge workers
• Some data sharing is occurring
• Enterprise information models govern database
  design
• 14 points of IQ are being implemented
• IQ organization is being formalized
• IQ training available for all staff levels
• IQ improvement process implemented and performed
• Information stewardship is started

Source Larry English, Building Data Warehouse
and Business Information Quality
35
English Information Quality MMM

• Moving from Stage 3 to Stage 4 (Wisdom)
• Continuing education for management
• Implementing information stewardship formally
• Measure results of IQ improvement process
• Improving data development processes to reuse
  architected data
• Changing funding for data development
• New incentives and performance measures for IQ
• Data captured electronically at source, eliminate
  unnecessary intermediate steps
• 14 points of IQ fully implemented and maturing
• All major processes controlled and IQ levels
  maintained
• Significant data sharing and reuse
• Significant reduction of redundant data
• Stewardship exists for most important data
• IQ training exists for all staff
• Data defect prevention is routine
• Data design defect prevention is routine
• Business rules and data integrity removed from
  applications and implemented around data types

Source Larry English, Building Data Warehouse
and Business Information Quality
36
English Information Quality MMM

• Moving from Stage 4 to Stage 5 (Certainty)
• Optimize IQ improvement processes
• Eliminate unnecessary redundant data processes
• Implement stewardship for all data
• Feedback mechanisms in place for all information
  processes
• Data development being implemented from an
  assemble to order approach
• Stewardship exists for all information
• IQ defect prevention is routine

Source Larry English, Building Data Warehouse
and Business Information Quality
37
Information Maturity Framework

• Principles
• Represent a continuum of maturity in information
  resource management
• Be simple to understand and apply
• Feature increasing standardization of data
  definitions
• Feature increasing accessibility of data
• Feature increasing custodianship of both data and
  data definitions
• Feature increasing applicability of data to
  business needs

MESIO Guy Friswell, DMR Group Peter Flagg,
Flagg Assoc.Dick Payne, Gerry Moore, Peter
Gordon, Wayne Cart, Jon Buckle, and Richard
Dixon, Ministry of Ttransportation Highways,
1995
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IMF Information Maturity Classifications

• Four Possible Levels
• Unstructured
• Uncontrolled
• Shared
• Integrated

MESIO Guy Friswell, DMR Group Peter Flagg,
Flagg Assoc.Dick Payne, Gerry Moore, Peter
Gordon, Wayne Cart, Jon Buckle, and Richard
Dixon, Ministry of Ttransportation Highways,
1995
39
IMF Information Maturity Levels
MESIO Guy Friswell, DMR Group Peter Flagg,
Flagg Assoc.Dick Payne, Gerry Moore, Peter
Gordon, Wayne Cart, Jon Buckle, and Richard
Dixon, Ministry of Ttransportation Highways,
1995
40
IMF CSF Performance Assessment Method
CSF To Be
CSF As Is
MESIO Guy Friswell, DMR Group Peter Flagg,
Flagg Assoc.Dick Payne, Gerry Moore, Peter
Gordon, Wayne Cart, Jon Buckle, and Richard
Dixon, Ministry of Ttransportation Highways,
1995
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Der Reifegrad des Datenmanagements
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Walter Schnider, Systor

• Entwicklungsstufen Development Steps
• Functional Orientation
• Database Administration
• Data Modeling / Data Administration
• Data Management
• Information Management

• Ziele Aim/Goal
• Information as a business resource
• Characteristic responsibility for data management
• Data standardization
• Use of DBMS
• Isolated uses
• Treiber Driver
• Information as an operational resource
• Process orientation
• Integration requests
• Technology

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Walter Schnider, Systor
Development Steps
Aim/Goal
Driver
Information as a business resource
Information Management
Information as an operational resource
Responsibility for data management
Data Management
Process orientation
Data standardization
Data Modeling / Data Administration
Database Administration
Integration requests
Use of DBMS
Functional Orientation
Isolated uses
Technology
1960
1970
1980
1990
2000
Year
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Schnider, Systor
45
Schnider, Systor
Criteria/Development Steps

• Criteria v1.0
• Order
• Client Flow
• Organization / Processes
• Development Organization
• Personnel
• Methodology
• Technology
• Business Culture

• Development Steps
• Function orientation
• Database Administration
• Data Modeling / Data Administration
• Data management
• Information management

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Schnider, Systor
47
Schnider - Organization
Information Technology
Application Development
Data Management
Data Center Operations
Data Architecture
Data Warehousing
Database Administration
Information Center
Data Portfolio Management
Information Readiness
Database Design
Data Modeling
Analysis Tools
Database Infrastructure
Data Administration
Meta Data Systems
Database Maintenance
Data Migration
Data Protection
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CAs Information Delivery MM
http//ca.com/cleverpath/solution/info_delivery_mo
del.htm
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IDR Dr. Peter Aiken DM3

• The Institute for Data Research has developed a
  methodology, called Data Management Maturity
  Measurement (DM3) for rapidly assessing and
  rating the maturity of an organization's data
  management practices against similar
  organizations from our database.
• IDR can provide practice assessments in the areas
  of
• Data Program Coordination
• Enterprise
• Data Integration
• Data Stewardship/Quality
• Data Development
• Data Support Operations

Source http//idatar.com/services/data_assessment
.htm, July 2003
50
IDR Dr. Peter Aiken DM3
Source http//idatar.com/services/data_assessment
.htm, July 2003
51
A Comprehensive Data Maturity Model

• Product/Service?
• Data (conceptual, logical, physical)
• Semantics (Metadata, taxonomy, rules)
• Analytical Capabilities (DW, BI, etc)
• Roles?
• Portfolio Manager
• Project/Program Manager
• Architect
• Modeler/Designer
• Data Steward
• Database Administrator

• How would it be used?
• Assessment
• Prescription
• KPAs -?
• Use CMM KPAs?
• Affinity AnalysisFunctions to Process?
• Organizational Directive?
• Scale/Data Categories?
• Industry
• Enterprise
• Organization
• Workgroup
• Individual

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Some other considerations

• Processes
• Resource or Product Life Cycle?
• Plan
• Buy (acquire)
• Build (develop)
• Deliver (deploy)
• Service (maintain)
• Retire

• Functions
• Portfolio Mgt (resource)
• Program Mgt (services)
• Architecture (Infrastructure)
• Engineering (Integration)
• Operations (Storage Retrieval)
• What else?

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Process/Function Model
Functional Areas
Customers
Plan
Buy
Build
Deliver
Service
54
Revised Functional Model

• Functional Model
• Data Resource Management
• Data Program Management
• Enterprise Data Architecture/Engineering
• Functional Data Engineering
• Data Operations

55
Revised Purpose Model

• Purpose Model
• Data Access
• Data Requirements
• Enterprise Data Integration
• Data Management Infrastructure
• Portfolio Management (Valuation/Cost Mgt)

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Data Mgt Process Model

• Plan Data Resources
• Data Requirements Analysis Design
• Alignment to IT Business Strategies
• Acquire Data Resources
• Buy it
• Capture it
• Build Data Resources
• Data Architecture Infrastructure
• Meta Data, Data Warehousing
• Sell Data Resources
• Data Products
• Infomate Business Products
• Distribute Data Resources
• Access Availability
• Service Data Resources
• Backup, Recovery
• Quality Measurement

57
Revised Quality Model

• Quality Model
• Service Effectiveness
• Delivery Efficiency
• Product Quality

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What Can You Do?

• Participate in the dialog
• Voice your opinion
• Share your insights

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DAMA DM3?

• Do you want DAMA Intl to publish a standard DM3?
• How many of you would use it?
• How many of you would contribute to it?
• What else should I ask?

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Thank You

• I appreciate your comments and will be pleased to
  answer any questions that you may have.You may
  contact me for further discussion at

Brett Champlin Work bchampli_at_allstate.comHome
brett_at_thechamplins.com DAMA VP_Online_Services_at_d
ama.org Work [phone]Visit my faculty
websitehttp//faculty.roosevelt.edu/Champlin/
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Collected Data Maturity Models

• 1 - Stages of Growth for IS - Nolan, Richard L
  Harvard, 1974, http//sutherla.tripod.com/mgmt550/
  orglearn.html
• 2 - Data Management MM - Parker, Burton MITRE,
  1994, proceedings of DAMA Int'l Symposium, 1996
• 3 - Information Maturity Model - collaboration
  between Guy Friswell of DMR Group Inc., Peter
  Flagg of Peter Flagg and Associates, and Dick
  Payne, Gerry Moore, Peter Gordon, Wayne Carr, Jon
  Buckle and Richard Dixon of the Ministry of
  Transportation and Highways British Columbia
  Ministry of Transportation Highways, 1995,
  Information Resource Management Plan - January 1,
  1995Appendix IV Method For Establishment Of
  Strategic Improvement Opportunities
• 4 -Data Resource Management MM - Champlin, J.
  Brett Roosevelt University/DAMA Chicago, 1996
• 5 - Enterprise-wide Data Management - Parker,
  Burton Paladin Integration Engineering, 1998,
  Guidelines for Implementing Data Resource
  Management, DAMA Int'l, 2001
• 6 - Information Quality MMM - English, Larry
  Information Impact International, Inc., 1999,
  Improving Data Warehouse and Business Information
  Quality
• 7 - Data Management MM (Der Reifegrad des
  Datamanagements) - Schnider, Walter SYSTOR AG,
  2000, http//www.kpp-consulting.ch/Downloadbereich
  /Datenmanagement-Assesment.pdf
• 8 - Data Management MM - Dravis, Frank
  Firstlogic.com, 2001, ICIQ-2001 Conference
  proceedings
• 9 - Data Categories MM - Janzen, Jeremey British
  Columbia Ministry of Forests, 2002,
  http//www.wilshireconferences.com/MD2002/Sessions
  .htmJanzen
• 10 - Data Warehouse Information Management MM -
  Ladley, John 2002, http//www.dmreview.com/maste
  r.cfm?NavID68EdID5618
• 11 - Data Warehousing MM - Marco, David
  Enterprise Warehousing Solutions, 2002, DM
  Review, Sept. 2001, pg. 80
• 12 - Information Delivery Maturity Model -
  Computer Associates, 2002, http//ca.com/cleverpat
  h/solution/info_delivery_model.htm
• 13 - Data Management MM - Agosta, Lou Giga
  Information Group, 2002, The Case for a Data
  Management Capability Maturity Model, Giga.com
• 14 - Data Management Maturity Measurement (DM3)
  Aiken, Peter Institute for Data Research, 2003,
  http//idatar.com/services/data_mgmt_assessment.pd
  f
• 15 - Stages of an Active Data Warehouse, Brobst,
  Stephen and Raney, Joe NCR Teradata, IRM-UK
  newsletter, 2003
• 15 - Information Evolution Model, SAS,
  http//www.dmreview.com/master.cfm?NavID68EdID5
  618

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Select References/Sources

• Beyond the Data Warehouse Information
  Management Maturity, John Ladley, DM Review, Aug
  2002
• The Evidence for CMM-based Software Process
  Improvement SEMA-3.01, CMU, 2001
• A History of the Capability Maturity Model for
  Software, Mark C. Paulik, SEI-CMU, 2001
  (http//www.sei.cmu.edu/cmm/slides/cmm-history.pdf
  )
• Improving Data Warehouse and Business
  Intelligence Information Quality, Larry English,
  1999
• Information Resource Management Plan, British
  Columbia Ministry of Transportation and Highways,
  January 1, 1995
• Managing the Crises in Data Processing, Richard
  Nolan, Harvard Business Review, mar-Apr, 1979
• Managing the Four Stages of EDP Growth, Cyrus
  Gibson Richard Nolan, Harvard Business Review,
  Jan-Feb, 1974
• Managing the Software Process, Watts Humphrey,
  1989, Addison-Wesley
• Meta Data Knowledge Management Capability
  Maturity Model, parts 1-4, David Marco, DM
  Review, Aug Nov 2002.
• A Process Improvement Approach to Enterprise
  Data Management, Burton G. Parker, MITRE,
  Proceedings of the 8th DAMA International
  Symposium, April, 1996
• Quality Is Free The Art of Making Quality
  Certain, Philip Crosby, 1979, McGraw-Hill