Title: 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
96000 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 5000 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
11 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 19 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 23 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
24 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
30 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 38 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 41 Der Reifegrad des Datenmanagements 42 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
43 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 44 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
46 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 48 CAs Information Delivery MM http//ca.com/cleverpath/solution/info_delivery_mo del.htm 49 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 organizations 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
52 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
53 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)
56 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
58 What Can You Do
Participate in the dialog
Voice your opinion
Share your insights
59 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
60 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 (847) 667-1747Visit my faculty websitehttp//faculty.roosevelt.edu/Champlin/ 61 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 Intl 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 Intl 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.cfmNavID68EdID5618
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.cfmNavID68EdID5 618
62 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/s/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
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