Data-Ed Online: Data Management Maturity Model

Copyright 2013 by Data Blueprint
Welcome: Data Management Maturity - Achieving Best Practices using DMM
The Data Management Maturity (DMM) model is a framework for
the evaluation and assessment of an organization's data
management capabilities. The model allows an organization to
evaluate its current state data management capabilities,
discover gaps to remediate, and strengths to leverage. The
assessment method reveals priorities, business needs, and a
clear, rapid path for process improvements. This webinar will
describe the DMM, its evolution, and illustrate its use as a
roadmap guiding organizational data management
improvements.
Key Takeaways:
• Our profession is advancing its knowledge and has a wide
spread basis for partnerships
• New industry assessment standard is based on successful
CMM/CMMI foundation
• Clear need for data strategy
• A clear and unambiguous call for participation 
 
Date: August 12, 2014 
Time: 2:00 PM ET 
Presented by: Melanie Mecca & Peter Aiken
1

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view it afterwards?
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3

Your Presenters
Melanie Mecca
• SEI/CMMI  
Institute/DMM  
Program Director
• 30+ years designing and
implementing strategies and
solutions for private and public
sectors
• Architecture/Design experience in:
– Data Management Programs
– Enterprise Data Architecture
– Enterprise Architecture
– Data Warehousing
• DMM primary author from Day 1
Peter Aiken
• 30+ years data mgt.
• Multiple Int. awards/recognition
• Founding Director,  
Data Blueprint (datablueprint.com)
• Associate Professor of IS (vcu.edu)
• Past, President, DAMA
International (dama.org)
• 9 books and dozens of articles
• 500+ empirical practice
descriptions
• Multi-year immersions w/
organizations as diverse as
US DoD, Nokia, Deutsche Bank,
Wells Fargo, Walmart, and the
Commonwealth of Virginia
4

Presented by Melanie Mecca & Peter Aiken, Ph.D.
Data Management Maturity
Achieving Best Practices using DMM

DMM Primer
• Reference model of foundational data management capabilities
– Measurement instrument to evaluate capabilities and maturity
– Answers the question: “How are we doing?”
– Guidelines for: “What should we do next?”
– Baseline for: An integrated strategy, specific improvements
• CMMI Institute with our Sponsors - Booz Allen Hamilton,
Lockheed Martin, Microsoft, Kingland Systems - and
contributing experts
• CMMI Institute conducted Assessments for: Microsoft;  
Fannie Mae; Federal Reserve System Statistics;  
Ontario Teachers’ Pension Plan; and Freddie Mac
• Sponsors conducted assessments for: the Securities  
and Exchange Commission; Treasury, Office of  
Financial Research; and CISCO.
6

• Motivation
- Are we satisfied with current performance of DM?
• How did we get here?
- Building on previous research
• What is the Data Management Maturity Model?
- Ever heard of CMM/CMMI?
• How should it be used?
- “All models are wrong … ”
• Where to next?
• Q & A?
Outline: Design/Manage Data Structures
8

Maslow's Hierarchy of Needs
9

Foundation for Advanced Solutions
You can accomplish
Advanced Data Practices
without becoming proficient
in the Basic Data
Management Practices
however this will:
• Take longer
• Cost more
• Deliver less
• Present  
greater 
risk Basic Data Management Practices
Advanced  
Data  
Practices
• MDM
• Mining
• Big Data
• Analytics
• Warehousing
• SOA
10
Data Management Function
Data Management Strategy Data Governance
Data Quality Program
Metadata Management

0%
15%
30%
45%
60%
1994 1993 1998 2000 2002 2004 2009
16%
27%
26%
28%
34%
29%
32%
53%
33%
46%
49%
51%
53%
44%
31%
40%
28%
23%
15%
18%
24%
Failed Challenged Succeeded
IT Project Failure Rates(moving average)
11
Source: Standish Chaos Reports as reported at: http://www.galorath.com/wp/software-project-failure-costs-billions-better-estimation-planning-can-help.php

General Literature
Hardware
Computer Systems Organization
Software/Software Engineering
Data
Theory of Computation
Mathematics of Computing
Information Technology and Systems
Computing Methodologies
Computer Applications
Computing Milieux
Data 6.2%
Software 19%
Computing
Methodologies
23%
Information
Technology 8%
Under represented research
• Hundreds of IT failures
• 100% data root cause
• In IT - no focus
• Few are data educated
• Underrepresented in research (Academic/‘Advisory’)
12

Bad Data Decisions Spiral
13
Bad data decisions
Most CIOs are
not data
knowledgable
Poor treatment of
organizational data
assets
C-level decision
makers are not
data knowledgable
Poor 
quality 
data
Poor organizational outcomes

What does it mean to treat data as an organizational asset?
• Assets are economic resources
– Must own or control
– Must use to produce value
– Value can be converted into cash
• An asset is a resource controlled by
the organization as a result of past
events or transactions and from
which future economic benefits are
expected to flow to the organization
[Wikipedia]
• With assets:
– Formalize the care and feeding of data
• Cash management - HR planning
– Put data to work in unique and
significant ways
• Identify data the organization will need  
[Redman 2008]
14

• Motivation
• Where to next?
• Q & A?
Outline: Data Management Maturity
15

Motivation
• "We want to move our data management
program to the next level"
– Question: What level are you at now?
• You are currently managing your data,
– But, if you can't measure it,
– How can you manage it effectively?
• How do you know where to put time, money,
and energy so that data management best
supports the mission?
"One day Alice came to a fork in the road and
saw a Cheshire cat in a tree. Which road do I
take? she asked. Where do you want to go?
was his response. I don't know, Alice
answered. Then, said the cat, it doesn't
matter."
Lewis Carroll from Alice in Wonderland
16

DoD Origins
• US DoD Reverse Engineering
Program Manager
• We sponsored research at the
CMM/SEI asking
– “How can we measure the
performance of DoD and our
partners?”
– “Go check out what the Navy is up to!”
• SEI responded with an integrated
process/data improvement
approach
– DoD required SEI to remove the data
portion of the approach
– It grew into CMMI/DM BoK, etc.
17

Acknowledgements
version (changing data into other forms, states, or
products), or scrubbing (inspecting and manipulat-
ing, recoding, or rekeying data to prepare it for sub-
Increasing data management practice maturity levels can positively impact the
coordination of data ﬂow among organizations,individuals,and systems. Results
from a self-assessment provide a roadmap for improving organizational data
management practices.
Peter Aiken, Virginia Commonwealth University/Institute for Data Research
M. David Allen, Data Blueprint
Burt Parker, Independent consultant
Angela Mattia, J. Sergeant Reynolds Community College
s increasing amounts of data ﬂow within and
between organizations, the problems that can
result from poor data management practices
Measuring Data Management
Practice Maturity:
A Community’s
Self-Assessment MITRE Corporation: Data Management Maturity Model
• Internal research project: Oct ‘94-Sept ‘95
• Based on Software Engineering Institute Capability
Maturity Model (SEI CMMSM) for Software Development
Projects
• Key Process Areas (KPAs) parallel SEI CMMSM KPAs, but
with data management focus and key practices
• Normative model for data management required; need to:
– Understand scope of data management
– Organize data management key practices
• Reported as not-done-well by those who do it
18

CMMI – Worldwide Process Improvement
• Quick Stats:
– Over 10,000  
organizations
– 94 countries
– 12 national  
governments
– 10 languages
– 500 Partners
– 1373  
appraisals  
in 2013
19

Source: Applications Executive Council, Applications Budget, Spend, and Performance Benchmarks: 2005 Member Survey Results, Washington D.C.: Corporate Executive Board 2006, p. 23.
Percentage of Projects on Budget
By Process Framework Adoption
…while the same pattern generally holds true for on-time performance
Percentage of Projects on Time
By Process Framework Adoption
Key Finding: Process Frameworks are not Created Equal
With the exception of CMM and ITIL, use of process-efficiency  
frameworks does not predict higher on-budget project delivery…
20

CMMI Model Portfolio
21
Establish, Manage, and
Deliver Services
Product Development /
Software Engineering
Acquire and integrate
products / supply chain
Workforce development
and management

DMM Drivers and Bio
• Data management broad and
complex = challenging
• An effective enterprise data
management program requires
a planned strategic effort
• Organizations needed a
comprehensive reference
model to precisely evaluate
data management capabilities
• DMM was targeted to unify
understanding, interests, and
priorities of lines of business,
IT, and the data management
function
• Foundation for collaborative and
sustained process
improvement.
22
Late 2009 – Gleam in the eye
Jan 2011 – Launch development
Sep 2012 – CMMI
Transformation
Apr 2014 – Industry Peer Review
Aug 2014 – DMM 1.0 Released
DMM Timeline

Who Wrote It and Why
• Authors with deep knowledge and experience in designing and
implementing data management
– Industry skills - MDM, DQ, EDW, BI, SOA, big data, governance,
enterprise architecture, data architecture, business and data
strategy, platform implementation, business process engineering,
business rules, software engineering, etc.
• Consortium approach – proven approaches
– Broad practical wisdom - What works
– DM experts combined with reference model architects and business
knowledge experts from multiple industries
– Extensive discussions resulting in consensus
• We wrote it for all of us
– To quickly and accurately measure where we are
– To accelerate the journey forward with a clear path  
and milestones
23

DMM Product Suite Timeline 
• Peer review comments received May 30
• 1200 helpful comments, 70 individuals from 45 organizations
• Partner program launched June 1
• 10 Partners to-date
• Partners sponsor individuals for certification
– A voice in the evolution of the model
– Participate in development of derivative products
• DMM 1.0 Full suite of courses leading to certification – Fall 2014
– Three sequential courses leading to certification and licensing of
EDMEs to facilitate assessments and assist organizations in
implementing data management process improvements.
– First course available Sep 2014, final course in our initial suite Dec
2014
– Future DMM Lead Appraiser course / additional certification Summer
2015.
24

• Motivation
• Where to next?
• Q & A?
25

Data Management Maturity (DMM)SM Model
• The DMM was released on
August 7, 2014
– 20+ years preparation
– 3.5 years in development
– 4 Sponsoring organizations
– 50+ contributing authors
– 70 peer reviewers
– 80+ organizations
– 230 content pages
– 300+ Practice Statements
– 300+ Work Products
26

DMM’s Orientation
• Data assets = vital infrastructure component
• An assessment against the DMM is a strategic initiative
• Takes aim at the biggest challenges:
– Clearly communicating to the business
– Aligning of business with IT/DM
– Organization-wide perspective
• State of the practice vs. state of the art
• Industry independent.
27

It Takes a Village
• The lines of business own the data
they create and manage
• Typically, not fully aware of the
implications, e.g.
– Determine acceptable quality levels
– Work with peers to clarify shared data
– Pinpoint what they need to know about
their data, etc.
• DMM emphasizes business decisions
• Organization-wide perspective is
needed – ‘my needs’ and ‘their
needs’ become ‘our needs’
• It is a powerful tool to create a shared
vision and unify diverse audiences
28

What the DMM is Not
• Not a compendium of all data
management knowledge
• Does not address every topic and sub-
topic that’s important
• 35+ years of evolution
• Foundational thinkers
• Talented vendors
• Wealth of collective experience
• Fully mature industry practices.
• Too much specificity = 1000+ pages
• Not a cookbook
• Doesn’t identify the “one best way”
29

• Process Area sections - (Purpose, Introduction, Goals,
Questions, Related Process Areas, Practice Statements
and Work Products) - are consistent with each other
• Orthogonal with other process areas (Can stand alone)
• Practice statements are grouped by level
• Set of statements is sufficiently detailed to convey
understanding
• Condensed statements with judicious abstraction
• Maturity factors - Infrastructure Support Practices (Generic
Practices in the CMMI)
Model Development Principles
30
The next few slides are a Quick Tour of principles applied to build the DMM

You Are What You DO
• Model emphasizes behavior –
– Creating effective, repeatable processes
– Leveraging and extending across the organization
• Activities result in work products
– Processes, standards, guidelines, templates, policies, etc.
– Reuse and extension = maximum value
• Non-prescriptive – technology, architectural approaches,
organizational structures, etc.
• Too much specificity = 1000+ pages = overwhelming and
forces organization into non-optimal solutions
31

Independent Process Areas
• Every organization performs data
management disciplines
• What is emphasized is what grows –
changing priorities
• Can become piecemeal – focus on highest
pain, not root causes
• DMM Process Areas were designed to
stand alone for evaluation
• Reflects real-world organizations
• Simplifies the data management landscape
for all parties
• Because “everything is connected”
relationships are indicated
32

Practice Statement Principles
• Functional practice statements should adhere to these
quality criteria:
– Unambiguous (hard to assess "appropriate")
– What, not how (does not specify implementation method)
– Orthogonal (non-overlapping)
– Precise and demonstrated by evidence (work products)
– Each statement represents one idea
33

Practice Statement Elaborations
• Some statements are intuitively obvious – Yes, No, or Partial
• Others may require additional information to understand
– Contextual information to explain what is meant by the singular
statement
– Expand upon the statement for operational use, acceptable
assessment evidence, implementation suggestions, etc.
– Establish boundaries for the statement - what is included versus
what is not
• Roughly 75% of practice statements have elaborations
34
3.3 A defined process for specifying benefits and costs for data quality initiatives is employed
to guide data quality strategy implementation.!
!
The data quality strategy should provide justification for the value and importance of implementation
outcomes. A clear value proposition should be established for executing the strategy. Determination of the
benefits and costs of data quality may include an ROI analysis, cost implications of defects, and business
opportunities tied to improvements.

Desirable Practice Characteristics
35
Definition Implementation
Unambiguous Verifiable
Complete Modifiable
Correct Understandable
Consistent Relevant
Concise Implementation Independent
Atomic Orthogonal

Infrastructure Support Practices = Maturity
• Adopted from CMMI:
– Level 2 - Institutionalize as a Managed Process
• Establish an Organizational Policy
• Plan the Process
• Provide Resources
• Assign Responsibility
• Train People
• Manage Configurations
• Identify and Involve Relevant Stakeholders
• Monitor and Control the Process
• Objectively Evaluate Adherence
• Review Status with Higher Level Management
– Level 3 - Institutionalize Organizational Standards
• Establish Standards
• Provide Assets that Support the Use of the Standard Process
• Plan and Monitor the Process Using a Defined Process
• Collect Process-Related Experiences to Support Future Use
36

One concept for process
improvement, others include:
• Norton Stage Theory
• TQM
• TQdM
• TDQM
• ISO 9000 
!
and focus on understanding
current processes and
determining where to make
improvements.
DMM Capability Maturity Model Levels
Our DM practices are informal and ad hoc,
dependent upon "heroes" and heroic efforts
Performed
(1)
Managed
(2)
Our DM practices are defined and
documented processes performed at
the business unit level
Our DM efforts remain aligned with
business strategy using
standardized and consistently
implemented practices
Defined
(3)
Measured
(4)
We manage our data as a asset using
advantageous data governance practices/structures
 
Optimized
(5) 
DM is strategic organizational capability,
most importantly we have a process for
improving our DM capabilities
38

DMM Process Areas 
Data Management Strategy
39
Name Description
Data Management Strategy Goals, objectives, principles, business value, prioritization,
metrics, and sequence plan for the data management program
Communications!

Communications strategy for data management initiatives and
mechanisms to ensure business, IT, and data management
stakeholders are aligned with bi-directional feedback
Data Management Function Structure of data management organization, responsibilities and
accountability, interaction model, staffing for data management
resources, executive oversight
Business Case Decision rationale for determining what data management
initiatives should be funded based on benefits to the
organization and financial considerations
Data Management Funding Funding justification for the data management program and
initiatives, operational and financial metrics
Create, communicate, justify and fund a unifying vision for data management

Data Governance
40
DataGovernance
GovernanceManagement Structure of data governance, governance processes and
leadership, metrics development and monitoring
BusinessGlossary Creation, change management, and compliance for terms,
definitions, and properties
Metadata Management Strategy, classification, capture, integration, and accessibility of
business, technical, process, and operational metadata
Active organization-wide participation in key initiatives and critical decisions
essential for the data assets

Data Quality
41
Data Quality

Data Quality Strategy Plan and initiatives for the data quality program, aligned with
business objectives and impacts
Data Profiling Analysis of semantic data content in physical data stores for
meaning and defect detection
Data Quality Assessment Assessment and improvement of data quality, business rules
and known issues analysis, measuring impact and costs
Data Cleansing Mechanisms to clean data, reporting and tracking of data
issues for correction with impact and cost analysis
A business-driven strategy and approach to assess quality, detect defects, and cleanse data

Platform & Architecture
Architectural Approach Architectural strategy, frameworks, and standards for implementation
planning
Architectural Standards Data standards for representation, access, and distribution
Data Management Platform Technology and capability platforms selection for data distribution and
integration into consuming applications
Data Integration Integration and reconciliation of data from multiple sources into target
destinations, standards and best practices, data quality processes at point
of entry
Historical Data, Archiving and
Retention
Management of historical data, archiving, and retention requirements
42
A collaborative approach to architecting the target state  
with appropriate standards, controls, and toolsets

Data Operations
43
Data Operations
Data Requirements Definition Process and standards for developing, prioritizing, evaluating, and
validating data requirements
Data Lifecycle Mapping of data to business processes as data flows from one process to
another
Provider Management Standardization of data sourcing process, SLAs, and management of data
provisioning from internal and external sources
Systematic approach to address business drivers and processes,  
building knowledge for maximizing data assets

Supporting Processes
44
Supporting Processes Adapted from CMMI
Measurement and Analysis Establishing and reporting metrics and statistics for each
process area within the data management program, supports
managing to performance milestones
Process Management Management and enforcement of policies, processes, and
standards, from creation to dissemination to sun-setting
Process Quality Assurance Evaluation and audit to ensure quality execution in all data
management process areas
Risk Management Identifying, categorizing, managing and mitigating business and
technical risks for the data management program
Configuration Management Establishing and maintaining the integrity of data management
artifacts and products, and management of releases
Systematic approach to address business drivers and processes,  
building knowledge for maximizing data assets

• Motivation
• Where to next?
• Q & A?
45

Why the DMM is useful
• Powerful educational tool
• A gradated path for improvement - collective wisdom to
guide practical action and implementation
• WHAT to implement, not HOW – how is situationally,
technically, and culturally dependent
• Unparalleled tool for performing thorough and efficient gap
analyses - in record time - for identifying both:
• Capabilities needing strengthening
• Strengths you can build on and extend
• Undiscovered capabilities
• Builds financial, moral, and labor support (coalition of the
willing)
46

Measurement = Confidence
• Activity-focused and
evidence-based evaluation
of the data management
program
• Allows organizations to gauge
their data management
achievements against peers
• Fuels enthusiasm and funding
for improvement initiatives
• Enhances an organization’s
reputation – quality and
progress
47

Assessment Components
Data Management Practice Areas
Data Management
Strategy
DM is practiced as a
coherent and
coordinated set of
activities
Data Quality
Delivery of data is
support of
organizational
objectives – the
currency of DM
Data  
Governance
Designating specific
individuals caretakers
for certain data
Data Platform/
Architecture
Efficient delivery of
data via appropriate
channels
Data Operations
Ensuring reliable
access to data
Capability
Maturity Model
Levels
Examples of practice
maturity
1 – Performed
Our DM practices are ad hoc and
dependent upon "heroes" and
heroic efforts
2 – Managed
We have DM experience and have
the ability to implement disciplined
processes
3 – Defined
We have standardized DM
practices so that all in the
organization can perform it with
uniform quality
4 – Measured
We manage our DM processes so
that the whole organization can
follow our standard DM guidance
5 – Optimized
We have a process for improving
our DM capabilities
48

Industry Focused Results
• CMU's Software  
Engineering Institute (SEI) Collaboration
• Results from hundreds organizations in
various industries including:
✓ Public Companies
✓ State Government Agencies
✓ Federal Government
✓ International Organizations
• Defined industry standard
• Steps toward defining data management
"state of the practice"
49
Data Governance
Data Quality
Data Operations
Focus:
Implementation
and Access
Focus:
Guidance and
Facilitation
Optimized(V) 
Measured(IV) 
Defined(III) 
Managed(II) 
Initial(I)

Data Governance
Data Platform & Architecture
Data Quality
Data Operations
0 1 2 3 4 5
Client Industry Competition All Respondents
Comparative Assessment Results
Challenge
Challenge
Challenge
50

Starting the Journey - DMM Assessment Method
• DMM can be used as a standalone guide!
• To maximize its value as a catalyst - forging shared perspective and accelerating
the program, our method:!
– Provides interactive launch collaboration event with broad range of stakeholder!
– Evaluates capabilities collectively by consensus affirmations!
– Facilitates unification of factions - everyone has a voice / role
– Solicits key business input through supplemental interviews!
– Verifies capability evaluation with work product reviews (evidence)!
– Report and executive briefing presents Scoring, Findings, Observations, Strengths,
and targeted specific Recommendations. !
• In the near future, audit-level rigor will be introduced to serve as a benchmark of
maturity, leveraging the CMMI Appraisal method.
To date, over 200 individuals from business, IT, and data management in early adopter organizations have
employed the DMM - practice by practice, work product by work product - to evaluate their capabilities.

Score Ranges – DMM-Assessed Organizations
53

DMM Section / Process Area DMBOK 2.0 Knowledge Area
Focuses on activities performed, with corresponding work
products, providing a baseline path of successive improvements,
and is aimed at a detailed snapshot evaluation and future audit /
benchmark
Comprehensive and thorough distillation of the core set of industry
knowledge and best practices, comprising a sound basis for
training and implementation.
Data Management Strategy!
• Data Management Strategy!
• Communications!
• Data Management Function!
• Business Case!
• Data Management Funding
Data Governance!
• Governance Management!
• Business Glossary!
• Metadata Management
Data Governance!
Meta-data
Data Quality!
• Data Quality Strategy!
• Data Profiling!
• Data Quality Assessment!
• Data Cleansing
Data Quality
Topic Comparison with DMBOK
54
.

DMM Section / Process Area DMBOK 2.0 Knowledge Area
Platform and Architecture!
• Architectural Approach!
• Architectural Standards!
• Data Management Platform!
• Data Integration!
• Historical Data, Archiving, and Retention
!
Data Architecture!
Data Integration & Interoperability!
Data Warehousing & Business Intelligence!
Data Modeling & Design
Data Operations!
• Data Requirements Definition!
• Data Lifecycle!
• Provider Management
Supporting Processes!
• Measurement and Analysis!
• Process Management!
• Process Quality Assurance!
• Risk Management!
• Configuration Management
Data Security!
Data Storage!
Reference & Master Data!
Documents & Content
Comparison
55

How the DMMSM Helps the Organization
56
Gradated
path -step-
by-step
improveme
nts
!
Unambiguo
us practice
statements
for clear
understandi
ng
Functional
work
products to
aid
implementa
tion
Common
language
Shared
understandi
ng of
progress
Acceleration

How the DMMSM helps the DM Professional
57
“Help me to help you” – platform for your customers –
conveys roles, shared concepts, complexity, connectedness
Provides an integrated 360 degree view - energizes
collaboration, increased involvement of lines of business
Actionable and implementable initiatives, grounded in
business strategy and organization’s imperatives
Enhances business cases for funding of rapid achievements
Qualifications – the “A Team” for the global standard
Certification path – defined skillset and industry recognition

• Motivation
• Where to next?
• Q & A?
58

George Edward Pelham Box
59
• His name is associated with results in statistics such as:
– Box–Jenkins models
– Box–Cox transformations
– Box–Behnken designs
• Perhaps more known for his quote:
– “Essentially, all models are wrong, but some are useful”

Executive Perspective
• The TDJ’s best friend
– Lines of business forge a shared
perspective
– Lines of business understand
current strengths and
weaknesses
– Lines of business understand  
their roles
– Reveals critical needs for the  
data management program
– Winning hearts and minds -
motivates all parties to
collaborate for improvements
60

DMM Training 
• DMM Introduction - for all audiences
– Themes, categories, and process areas
– Implementation benefits
– Challenges and lessons learned
• DMM Advanced Concepts
– Implementation of DMM-compliant processes
– Detailed understanding of the DMM
• Enterprise Data Management Expert
– Evaluate an organization against the DMM
– Lead process improvement programs
61

DMM Certification
• Enterprise Data Management Expert
– Prerequisites
• DMM Advanced Concepts
• Meet qualifications
• Application / Resume / Interview
– Complete Course
– Pass Exam
– Assessment Observation
– Certification Awarded
62

DMM Partner Program
63
DMM
Evolution
Community
Certified
Individuals
Priority
Access
Early
Insight
Beta
Testing

Upcoming Events
September Webinar: 
Data Governance
 
September 9, 2014 
 
!
Sign up here:
• www.datablueprint.com/webinar-schedule
• www.Dataversity.net
!
!
!
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Brought to you by:
64

Questions?
+ =
65

For more information
!
• Feel free to email me:
• mmecca@cmmiinstitute.com
!
• And visit our web site:
• http://cmmiinstitute.com/DMM
66

10124 W. Broad Street, Suite C
Glen Allen, Virginia 23060
804.521.4056

Data-Ed Online: Data Management Maturity Model

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