Data Management Maturity (DMM)SM Model Ecosystem

Data Management Maturity (DMM)SM
Model Ecosystem
&
Deep Dive: Business Glossary
Portland DAMA Chapter
April 21, 2015
DMM model, CMM Integration, SCAMPI, SCAMPI Lead Appraiser, TSP, and IDEAL are service marks of Carnegie Mellon University.
® CMMI, Capability Maturity Model, Capability Maturity Modeling, CMM, and Carnegie Mellon are registered in the US Patent and Trademark
Office by Carnegie Mellon University.
For more information on CMU/SEI Trademark use, please visit https://www.sei.cmu.edu/legal/marks/index.cfm
SM
Presentation Objectives
• Learn about the DMM
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Model drivers, themes, concepts and structure
How an organization’s program is evaluated
Complementarity with bodies of knowledge and standards
Use cases for the DMM.
• Learn about the DMM ecosystem
• How the DMM Helps the DM Professional
• Training / Certifications
• DMM Partners / DMM Community
• Take a core sample of the DMM
• Deep Dive: Business Glossary (interactive)
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Agenda
• Introduction to the DMM
• What is it - why did we develop it
• Why our industry needs it
• What is its structure and approach
• DMM in Action
• DMM Assessments
• Use Cases
• DMM Ecosystem
• Adoption / Case Studies
• Training / Certifications
• Partner Program
• Business Glossary Exercise
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Data Management Maturity Model
History, Description, Structure
CMMI – Worldwide Capability Building
CMMI Quick Stats:
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Over 10,000
organizations
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94 Countries
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12 National
governments
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10 languages
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500+ Partners
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1600+
Appraisals in
2014
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Data Management Maturity (DMM)SM Model
The DMM was released on
August 7, 2014
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3.5 years in development
4 sponsoring organizations
50+ contributing authors
70+ peer reviewers
80+ organizations involved
320+ practice statements
520+ functional work products
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Fly-Over
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DMM - Guided Navigation to Lasting Solutions
• Reference model framework of fundamental data management
best practices
• Measurement instrument for organizations to evaluate
capability maturity, identify gaps, and incorporate guidelines for
improvements
• Developed by CMMI Institute with our corporate sponsors Booz Allen Hamilton, Microsoft Corporation, Lockheed Martin,
and Kingland Systems
• Microsoft was CMMI Institute’s first Pioneer of the DMM
Assessment – Microsoft IT, February 2013
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Foundation for advanced solutions
You can accomplish Advanced Data
Advanced
Solutions without proficiency in
Data
Basic Data Management Practices,
Solutions
but solutions will:
• MDM
• Take longer
• Analytics
• Big Data
• Cost more
• IOT
• Not be extensible
• Warehousing
• Deliver less
• SOA
• Present
greater
Fundamental Data Management Practices
risk
Data Management Strategy
Data Governance
Data Integration
Metadata Management
Data Management Function
Data Quality Program
Copyright 2013 by Data Blueprint
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DMM Themes
• Architecture and technology neutral – applicable to legacy, DW, SOA,
unstructured data environments, mainframe-to-Hadoop, etc.
• Industry independent – usable by every organization with data
assets, applicable to every industry
• Emphasis on current state – organization is assessed on the
implemented data layer and existing DM processes
• Launch collaborative and sustained capability improvement – for
the life of the DM program [aka, forever].
If you manage data, the DMM will benefit you
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DMM Structure
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DMM Capability Levels
Quality
Reuse
Clarity
Risk
Ad hoc
Stress
Level
5
Optimized
Level
4
Measured
Level
3
Defined
Level
Level
1
2
Performed
Managed
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You Are What You DO
• Model emphasizes behavior
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Creating effective, repeatable processes
Leveraging and extending across the organization
• Activities result in work products
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Processes, standards, guidelines, templates, policies, etc.
Reuse and extension = maximum value, lower costs, happier staff
• Process Areas were designed to stand alone for
evaluation
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Reflects real-world organizations
Simplifies the data management landscape for all parties
Flexible for multiple purposes
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DMM Structure
Core Category
Process Area
Purpose
Introductory Notes
Goal(s) of the Process Area
Core Questions for the Process Area
Related
Process Areas
r
Functional Practices (Levels 1-5)
Example Work Products
Infrastructure Support Practices
Explanatory
e
Model Components
Required for Model Compliance
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The DMM in Action
How the DMMSM Helps an Organization
Common language
Gradated path step-by-step
improvements
Shared
understanding of
progress
Acceleration
Functional work
products to aid
implementation
Unambiguous
practice
statements for
clear
understanding
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How the DMMSM helps the DM Professional
“Help me to help you” – quick education for roles, shared
concepts, complexity, connectedness
Integrated 360 degree view – Program level - energizes
collaboration, increased involvement of lines of business
Actionable and implementable initiatives, grounded in business
strategy and organization’s imperatives
Strong support for business cases for funding of rapid achievements
Certification path – defined skillset and industry recognition
When Should I Employ the DMM?
• Use Cases - assess current capabilities before:
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Developing or enhancing DM program / strategy
Embarking on a major architecture transformation
Establishing data governance
Expansion / enhancement of analytics
Implementing a data quality program
Implementing a metadata repository
Designing and implementing multi-LOB solutions:
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Master Data Management
Shared Data Services
Enterprise Data Warehouse
Implementing an ERP
Other multi-business line efforts.
Like an Energy audit or
an executive physical
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Foundation for Business Results
• Trusted Data – demonstrated and independently measured
capability to ensure customer confidence in the data assets
• Improved Risk and Analytics Decisions – executing a
comprehensive and measured DM strategy ensures decisions
are made based on accurate data
• Cost Reduction/Operational Efficiency – identification of
current and target states enables elimination of redundant data
and streamlining of DM processes and systems
• Regulatory Compliance – independently evaluated and
measured DM capabilities to meet industry and regulator
requirements
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Starting the Journey - DMM Assessment Method
• The DMM can be used by small group – however, to maximize its value
as a catalyst for forging shared perspective and program acceleration
our method provides:
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Interactive launch collaboration event with a broad range of stakeholders
Capabilities evaluated collectively by consensus affirmations
Facilitates unification of factions - everyone has a voice / role
Solicits key business input through supplemental interviews
Verifies evaluation with work product reviews (evidence)
Report and executive briefing presents Scoring, Findings, Observations,
Strengths, and targeted specific Recommendations.
• Audit-level rigor will be introduced in 2016 to serve as a maturity
benchmark, leveraging the CMMI SCAMPI A appraisal method
To date, over 300 individuals from business, IT, and data management in early adopter organizations have
employed DMM 1.0 - practice by practice, work product by work product - to evaluate their capabilities.
DMM Assessment One-Page
Sample Organization
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Next Step Sample – DM Roadmap
Comprehensive and Realistic Roadmap for the Journey
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Summary - Why Do a DMM Assessment?
• Tour de force – learning precisely how you are doing
• Makes gaps highly visible for all stakeholders – e.g. pain
awareness, step one in going to the doctor
• Brings factions and siloed organizations together – i.e.
making an organization-wide program possible
• Creates common concepts, perspective, and terminology
• Creates a shared vision and purpose – i.e., lights a path
for working together to improve the data assets
• Priorities begin to clarify
• Provides a baseline for monitoring progress over time
• Industry-wide standard begets confidence
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DMM Assessment Drivers – Early Adopters
• Microsoft – Integrated Information Management supporting
transition to the Real-Time Enterprise, enhance data governance
• Fannie Mae – Validation of EDM program and governance, discovery
for new business priorities
• Federal Reserve System Statistics – Validation of inherent strengths,
discovery of gaps, leverage capabilities across the Banks
• Ontario Teachers’ Pension Plan – Evaluation of well-rounded
program, voice of the customer, governance expansion
• Freddie Mac – Evaluation of current state to prepare for a SingleFamily-wide data management program launch
• Global Internet Equipment Manufacturer – Enhance corporate
strategy to improve Customer data
• Center for Army Analysis – Enhance data asset strength to support
analytics for logistics, targeting, deployments for warfighting effort.
Every organization will have its own meta-drivers for the Assessment and results
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Microsoft
The Real Time Enterprise
Virtually everything in business today is an undifferentiated
commodity, except how a company manages its information. How
you manage information determines whether you win or lose.
– Bill Gates
Business Processes
Processes achieve business
results
People
People make decisions
Information
Decisions are driven by
Information
Technology
Technology speeds the delivery
of information
[ 25 ]
Strategic Enterprise
Architecture
Microsoft
Establishing a Common Data Management Language
Data Management Maturity Model
[ 26 ]
Microsoft
Maturity Levels Related to Real Time Data
Real –Time
Competitive Advantage
5
Velocity
Operational
Effectiveness
Level
Optimized
Processes are improved on a continuous basis and
advocated at the executive management level.
Level
4
Measured
Established metrics. Variance management across the
process lifecycle.
Level
Batch
Enabling Capabilities
Level
1
3
Defined
Level
2
Performed
Managed
Established processes, improved over time. Tailored
to meet specific needs predictably and consistently.
Formalized processes. Infrastructure supports at business
unit level. Clearly defined roles and responsibilities.
Ad-hoc processes. Emphasis on data repair . Transitory improvements.
[ 27 ]
Strategic Enterprise
Architecture
Microsoft
CMMI Assessment Recommendations
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Unified effort to maximize data
sharing and quality
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Monitor and measure adherence
to data standards
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Data
Management
Operations
Data
Management
Strategy
Data
Governance
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Integrate data governance
structures
Prioritize policies, processes,
standards, to support corporate
initiatives
Map key business processes
to data
Leverage Meta Data
repository
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Platform &
Architecture
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Leverage best practices for
data archival and retention
Maximize shared services
utilization
Data Quality
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[ 28 ]
Top-down approach to
prioritization
Up-stream error prevention
Common Data Definitions
Strategic Enterprise
Architecture
Microsoft
Key Lessons
 In the world of Devices and Services, Data Management is a pillar of
effectiveness
 DMM is a key tool to facilitate the Real-Time Enterprise journey
 Active participation of cross-functional teams from Business and IT is key for
success
 Employee education on the importance of data and the impact of data
management is a good investment
 Build on Strengths!
Microsoft IT Annual Report may be found at:
http://aka.ms/itannualreport
[ 29 ]
Strategic Enterprise
Architecture
DMM Ecosystem
Organizations & Professionals
2015 – Building the DMM Ecosystem
Results / Assets
Partner Program
/ Outreach
Certifications
Product
Suite
DMM
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DMM Ecosystem - Product Suite Overview
Results / Assets
Partner Program
/ Outreach
• Data Management Maturity (DMM)
Model
o Comprehensive document with
descriptions, practice statements and
work products
Product
Suite
• Assessments
o Structured, facilitated working
sessions resulting in detailed
current/future state executive report
DMM
• Training & Certification
o Introductory, Advanced and Expert
courses with associated certifications
Certifications
• Formal Measurement/Appraisal (2016)
o Benchmark measurement and scoring
of capability/maturity level
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DMM Ecosystem – Training
Results / Assets
Partner Program
/ Outreach
Certifications
Product
Suite
Training Classes
• Building EDM Capabilities (3 days)
• eLearning Building EDM Capabilities
(self-paced, web-based)
• Mastering EDM Capabilities (5 days)
• Enterprise Data Management Expert
(5 days)
DMM
• Future – EDM Lead Appraiser (5 days)
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Building EDM Capabilities Schedule
DAY 1
DAY 2
Module 1
Course Introduction
Module 5
Category: Data Quality
Module 2
DMM Model and Method
Module 6
Category: Data Operations
Module 3
Category: Data Management
Strategy
Module 7
Category: Platform and
Architecture
DAY 3
Module 8
Category: Supporting
Processes
Module 9
Capability Implementation
& Process Improvement
Module 10
Wrap-up
Module 4
Category: Data Governance
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Advanced Schedule
DAY 1
Module 1
Course
Introduction
Module 2
Interpreting the
Model
Module 3
Work Products
Module 4
Governance
Management:
Deep Dive
DAY 2
DAY 3
DAY 4
Module 6
Level 3
Module 10
Related Process
Areas
Module 14
DQ Program: Team
Presentations
Module 7
Business Glossary:
Deep Dive
Module 11
Data Quality
Program: Team
Module 15
Consulting Skills
Module 8
Metadata
Management
Module 12
Measurement and
Metrics
Module 9
Key Process Areas
Module 13
DM Strategy
Concepts
DAY 5
Module 16
Change
Management
Module 17
DMS DMF COM
Module 18
DMM Future State
Module 19
EDME Class
Preview &
Application
Module 5
Data Management
Responsibilities
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EDME Schedule
DAY 1
Module 1
Introduction
Module 2
EDME Role
/Assessment
Drivers / Benefits
Module 3
Scoping the
Assessment
Module 4
Client Preparation
Module 5
Communications,
Negotiations, Client
Management
DAY 2
DAY 3
Module 6
Assessment
Preparation
Module 12
Work Product
Review
Module 7
Presentation Skills
Module 13
Schedule Options
Module 8
Workshop Prep
Module 14
Mock Assessment
Prep
Module 9
Conducting the
Workshop
Module 10
Facilitation Skills
Module 15
Mock Assessment
Execution
(Individual)
DAY 4
DAY 5
Module 16
Synthesizing
Results
Module 19
Present Executive
Briefing (Team)
Module 17
Final Report &
Executive Briefing
Module 20
Post-Assessment
Consulting
Module 18
Briefing
Presentation /
Prepare Report
and Briefing
Module 21
Consulting Skills /
Change
Management
Module 22
EDME Certification
/ Wrap-up
Module 11
Interviewing Skills
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DMM Ecosystem - Certifications
Results / Assets
Partner Program
/ Outreach
Certifications
Product
Suite
Certifications:
Credentials and Credibility
• Enterprise Data Management
Expert (EDME) – Assessing and
Launching the DM Journey
• DMM Lead Appraiser (DMM LA)
– Benchmarking and Monitoring
Improvements
DMM
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DMM Ecosystem – Partner Program
Results
Reporting
Partner Program
/ Outreach
Certifications
Product
Suite
DMM
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DMM Ecosystem – Results and Assets
Results
Results / Assets
Partner Program
/ Outreach
Certifications
• Benchmarking
• Web publication of approved
appraisals
• Case studies
• Best Practice Examples
Product
Suite
DMM Assets
DMM
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Translations (#1 Portuguese)
Seminars
Self-Assessment Tool
Profiles
White Papers
Academic Courses
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DMM Events thru Jul 2015
• First EDME Course – Apr 13-17 DC
• Building EDM Capabilities eLearning – April 14
• Enterprise Data World Mar 30 – Apr 2
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DMM Seminar with Peter Aiken
DMM Case Studies – Freddie Mac, FRS Statistics
Building EDM Capabilities - May 13-15 – Seattle
Building EDM Capabilities – May 26-28 - Toronto
DGIQ – Jun 8-11 – Assertive Data Quality – Seminar
Mastering Capabilities – Jun 22-26
DMM Intro – Jul 6-8 - Dublin / Trinity College
EDME – Jul 20-24
Webinar – CMMI Institute, May
Webinar - DataVersity, July
DMM White Paper series - DataVersity
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Deep Dive / Exercise
Business Glossary
When You Focus on Your Shared Data……..
You may have discovered that there are:
• No ‘authoritative’ data stores, just many data
stores with overlapping data
• Various views about critical data element sets,
differing values for the same element, etc. –
challenging to be definitive
• Lack of agreement on names, definitions
• Varying calculations used around the firm
• Disagreements about what, how much, and
the extent of data description and lineage
needed for reporting and audit
• Etc., etc. etc. – (complexity and issues)
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……the business may not agree about the data
We’ll explore one selected DMM process area as an
example of what an organization needs to do
Business Glossary
A common understanding of terms and definitions
about data supporting business processes for all
stakeholders
• Provides a shared, approved foundation for understanding and integration
of data across the organization.
• Each term refers to a specific, atomic fact; each definition is unique
• Properties (facts) are standardized and applied to each term.
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Why create a business glossary?
• Creates clarity among lines of business, which improves
communications by resolving diverse uses of terms for data
models, EDW, data marts, ontologies, reports, etc.
• Everyone understands everyone else – standardized usage
• Meaning is clear and unambiguous for every business
process
• Critical for effective design of integrated data
• Data models are higher quality and accomplished more quickly
• Deepens stakeholder understanding of the data
• Foundation for all metadata – central core
• Anchor point - data lineage
• Starting point - mapping data to business processes
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And more reasons why………….
• Improves quality and shortens time to delivery of rearchitected data layer components
• Repositories and master data hubs
• Internal and external data interfaces
• Shared data services
• Perfect project for start-up data governance groups –
substantive task, clear outcomes
• Allows development of confident, accurate analytics /
reporting.
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Business Glossary
The business glossary provides a shared, approved foundation for
understanding and integration of data across the organization. It is
an important support for:
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Risk analysis and reporting
Data integration
Metadata management
Data quality assessments and profiling
Data store consolidation, custom-to-COTS migrations
Business process improvement and automation
Compliance and audit
…and just about every other data-related initiative.
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Business Glossary is the Anchor
Metadata
Business
Glossary
Business
Rules
Quality
Rules
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Business Glossary (BG)
Governance
Management
Data
Governance
Metadata
Management
Business
Glossary
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Business Glossary (BG)
Purpose
Supports a common understanding of terms and definitions about structured and
unstructured data supporting business processes for all stakeholders.
Definition
The business glossary is an approved, governed compendium of business term names and
definitions. The process developed will define how the organization creates, approves, updates,
and promulgates consistent business terms and definitions, fostering shared data usage across
the organization. Consistent terms and definitions, with corresponding metadata, are essential to
managing corporate data in the context of meaning.
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BG Goals
The language that represents the data is unambiguously aligned with the
language of the business.
The organization has created a comprehensive, approved business
glossary
The organization follows the standards for naming, definitions, and
metadata associated with business terms.
Organization-wide access to the business glossary allows all stakeholders
to achieve a common understanding of standard business terms.
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BG Goals - Continued
Data governance facilitates the review, approval, and consistent usage of
business terms.
A compliance and enforcement process ensures consistent application of
business terms as new data requirements and projects arise.
The organization has a communication plan and process in place for
continuous feedback on the usefulness of the glossary to data users and
other stakeholders.
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Business Glossary Functional Practices
Level
1
Level
2
Level
3
Level
4
Level
5
Logical data
models refer to
business terms
Business terms are defined
Unique business
terms
Business
glossary is
linked into new
development
Business terms
are known and
accessible
Progress metrics
Monitoring for
correctness of
business terms
New development
uses the business
glossary
Business terms are
linked to logical
and physical
representations
Metrics-based
improvements
Standard industry
business terms
Business glossary management follows a process
Governance
monitors for
compliance
Business term
change
management
Integrated into
metadata
repository
Continuous improvement and contribution to industry standards & best practices
Inclusion of business rules and ontology structures
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Capability Levels of BG
Level 1
Business terms are
defined for a
particular
purpose; logical
data model
attributes are
created from
business terms.
Level 2
Business glossary
processes for
definition,
properties, usage,
and maintenance
are defined;
standard terms are
published and
available; each term
has a unique name
and definition; data
store development,
integration, and
consolidations use
business terms.
Level 3
Approved business
terms used in shared
repositories, data
transfer mechanisms,
ontologies, semantic
models, etc.;
organization-wide
governance for glossary
compliance; Business
terms mapped to logical
and physical names;
Impact analysis prior to
changes; metrics
employed to measure
progress and
compliance.
Level 4
Level 5
The business
glossary is
integrated into the
organization’s
metadata
repository; it
incorporates
standard industry
terms and
properties as
appropriate.
statistical analysis
is performed to
assess progress
and adjust targets.
The business
glossary is enhanced
with all applicable
business rules, and
ontology / semantic
structures.
Optimization
techniques are
employed to
develop extensions;
the organization
publishes case
studies and white
papers on effective
management of
business terms.
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How do you know you’re doing a good job?
• Has your organization created a business glossary of standard business
terms?
• How are business terms, definitions, and corresponding metadata created,
approved, verified, and managed?
• Are business terms referenced as the first step in the design of application
data stores and repositories?
• What role does data governance perform in creating, approving, managing,
and updating business terms?
• Do you cross-reference and map standard business terms to businessspecific usages (synonyms, business unit glossaries, logical attributes,
physical data elements, etc.)?
• Does the organization employ a defined process for stakeholders to provide
feedback about business terms?
• How is the business glossary enhanced to reflect changes and additions?
• Is a compliance process implemented to ensure that business units and
projects are correctly applying business terms?
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What Obstacles Can Hamper Progress?
• Difficulty to persuade diverse lines of business to shift
their terminology and/or map to new approved terms
• Often left to the logical model and doesn’t get promoted
• Resistance to change of terms
• Applications always lag behind – multi-year transition
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•
•
•
•
•
Difficulty in selling time, scope, and cost
Effort to search for, combine , and create approved terms
Governance challenge to focus and gain agreements
Lack of approved, clear, prioritized phased approach
No current or planned central repository
Lack of approved standards for business terms and
properties.
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Typical Implementation Problems
• Whose job is it to lead? Often no core shepherd group – (e.g., data
management function is not sufficiently empowered or resourced)
• Data Management Function
• Data Governance Groups
• Getting organized
• Agreeing on properties beyond name and definition
• Agreeing on standards and approach
• Where to store the data so that it can be efficiently used and
maintained
• Difficulty to persuade diverse lines of business to shift their
terminology and/or map to new approved terms
• Seems overly technical and bookish
• No one wants to change their databases or reports
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Common Pitfalls During Construction
• Failing to gain input from / align with key business
processes results in:
• Poor / erratic quality of terms
• Critical blind spots
• Disconnection between business and data
• Failing to start with / align with highly shared data fosters:
• Disharmony / loss of will
• Inefficiency / confusion
• Little recognition for results
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….and What Helps You Succeed?
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Executive mandate
Commitments from all relevant stakeholders
Clear governance activities and decisions from the start
Phased plan
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Accounting for major initiatives
Aligned with priorities in the data management strategy
Scope of each phase specified
Central repository - empowers the business
Metrics track progress and help maintain momentum
Clear issues resolution process - escalation
Training for participants
(And if you are so fortunate) having an Enterprise Data Model or
business area data models
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Flexibility Needed – All Avenues will be Taken
• Top-Down (Subject Area by priority)
• Middle In (Key data stores)
• Bottom-Up (Reports)
• Which stretch of road you choose first depends on:
• Urgency / key priorities
• Available staff / data stewards
• Major initiatives (e.g., EDW redesign, MDM)
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Discovering Terms - System Approach
Pros
Cons
Rich descriptions
Bias to prior system
Easier to validate
Deja vu all over again
Data lineage
Lots of data model changes
Realize benefits quickly
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Subject Area Approach
Pros
Cons
Refined descriptions
Difficult to validate
Surface strategic issues sooner
Data lineage is complex
Fosters collaboration
More preparation time
Thorough
Delayed benefit realization
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Sample Business Term Standards - Excerpt
Standard
Definition
Descriptive
Business Term name clearly and concisely
defines an element in the enterprise
Consistent
Consistent wording in the Business Term name
enables users looking at the term to know
exactly how the element and its associated
attribute are used
Unique
Business Term name has only one meaning
throughout the enterprise
Minimal
Acronyms
Business Term name and/or definition should
not include acronyms. However if an acronym
is included it: 1) must be unique at the
enterprise level; 2) needs to be approved by
the Data Governance Group; 3) and must be
defined in the acronym repository.
No
Abbreviations
Business Term and definition should not
contain abbreviations
No Special
Characters or
Numbers
Business Term name and definition cannot
contain any special characters. Use of numbers
should be limited as well.
Example
Use “Issuer Mailing Address” or “Issuer Shipping
Address” instead of “Address
Consistent use of “Code” (which includes a list of
valid values) or “Indicator” (Yes or No). The use of
“Flag” in a business term name is not allowed.
“Employee Identification Number” refers only to
the number generated by the HR System of
Record. This is different than the “Party
Identification Number” which is used to identify an
organization or individual.
Valid usage could include industry standards such
as EDI and EFT.
Invalid usage or usage requiring Governance Group
approval might include:
• SLA (Service Level Agreement)
• DOAT (Account Termination Date)
• AMT
• PMT
• CD
becomes “amount”
becomes “payment”
becomes “code”
Some of the special characters not allowed include
underscore (_), number (#), dollar ($), and
ampersand (&).
Email Address 1 and E-mail Address 2 should be
Primary E-mail Address and Secondary E-mail
Address.
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Exercise:
Evaluate an Organization’s
Business Glossary Capabilities
Let’s put the DMM to Work
1. Please take a few minutes to read the Business Glossary
Process Area handout – Purpose, Introduction, Goals, and
Questions
2. Have an organization in mind – (pick one and stick to it)
3. Write your answer to each Practice Statement – Fully Met,
Partially Met, or Not Met - (F, P, or N)
4. Future work doesn’t count – e.g. “We plan to start our
Business Glossary effort next quarter”
5. We’ll take a class straw poll, statement by statement – a
collective finger in the wind - “How are organizations doing
with their Business Glossary?”
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Resources and Contact
DMM Model
http://cmmiinstitute.com/data-management-maturity
DMM Training Schedule and Registration
http://cmmiinstitute.com/training
DMM Partner Program
http://partners.clearmodel.com/become-a-partner/become-partner-dmm/
Questions about the DMM
kmorton@cmmiinstitute.com (Kyle Morton)
mmecca@cmmiinstitute.com (Melanie Mecca)
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