DataEd Slides: Data Management Maturity - Achieving Best Practices Using DMM

Presented by Melanie Mecca & Peter Aiken, Ph.D.
Data Management Maturity
Achieving Best Practices using DMM
Copyright 2013 by Data Blueprint
• 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?
- Use Cases and Value Proposition
• Where to next?
• Q & A?
Outline: Design/Manage Data Structures
2

!3
Guided Navigation to Lasting Solutions
• Architecture & technology
neutral
• Industry independent
• Answers: “How are we doing?”
• Guides: “What should we do
next?”
• Baseline for:
o Managing data as a critical
asset
o Creating a tailored data
management strategy
o Accelerating an existing
program
o Engaging stakeholders
o Pinpointing high value
initiatives.
!4
Foundation for Business Results
• Trusted Data – demonstrated,
independently measured capability to
ensure customer confidence in the data
• Improved Risk and Analytics Decisions
–comprehensive and measured DM
strategy ensures decisions are based
on accurate data
• Cost Reduction/Operational Efficiency
–identification of current and target
states supports elimination of
redundant data and streamlining of
processes
• Regulatory Compliance – independently
evaluated and measured DM
capabilities to meet and substantiate
industry and regulator requirements.

• Motivation
• Where to next?
• Q & A?
Outline: Data Management Maturity
5
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
6

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.
7
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-
sequent use).
• Approximately two-thirds of organizational data
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
A
s increasing amounts of data ﬂow within and
between organizations, the problems that can
result from poor data management practices
are becoming more apparent. Studies have
shown that such poor practices are widespread.
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
8

!9
CMMI Institute Background
• Evolved from Carnegie Mellon’s Software Engineering
Institute (SEI) - a federally funded research and
development center (FFRDC)
• Continues to support and provide all CMMI offerings and
services delivered over its 20+ year history at the SEI
o Industry leading reference models - benchmarks and guidelines
for improvement – Development, Acquisition, Services, People,
Data Management
o Training and Certification program, Partner program
• Dedicated training, partner and certification teams to
support organizations and professionals
• Now owned by ISACA (CISO/M, COBIT, IT Governance,
Cybersecurity) and joint product offerings are planned
!10
CMMI – Worldwide Process Improvement
CMMI Quick
Stats:
• Over 10,000
organizations
• 94 countries
• 12 National
governments
• 10 languages
• 500 Partners
• 1950+
Appraisals in
2018

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…
11
!12
DMM and DMBOK
CMMI Institute and DAMA International
are collaborating to:
• Eliminate any confusion between the two tools
and highlight their complementarity
• Extend and enhance data management training
for organizations and professionals
• Provide benefits to DAMA members (members
receive a discount for our public training
classes)

• Motivation
• Where to next?
• Q & A?
13
!14
Data Management Maturity (DMM)SM Model
• DMM 1.0 released August 2014
o 3.5 years in development
o Sponsors – Microsoft, Lockheed
Martin, Booz Allen Hamilton
o 50+ contributing authors, 70+
peer reviewers, 80+ orgs
• Reference model framework of
fundamental best practices
o 414 specific practice statements
o 596 functional work products
o Maturity practices
• Measurement Instrument for
organizations to evaluate
capabilities and maturity,
identify gaps, and incorporate
guidelines for improvements.

!15
“You Are What You DO”
• Model emphasizes behavior
o Proactive positive behavioral
changes
o Creating and carrying out
effective, repeatable processes
o Leveraging and extending across
the organization
• Activities result in work
products
o Processes, standards, guidelines,
templates, policies, etc.
o Reuse and extension = maximum
value, lower costs, happier staff
• Practical focus reflects real-
world organizations – enterprise
program evolving to all hands on
deck.
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
16

!17
DMM Capability Levels
Performed
Managed
Defined
Measured
Optimized
Level
1
Level
2
Level
3
Level
4
Level
5
Risk
Quality
Ad hoc
Reuse
Stress
Clarity
Capability – “We can do
this”
• Specific Practices -
“We’re doing it well”
• Work Products - “We’ve
documented the processes we are
following” (processes, work
products, guidelines, standards,
etc.)
Maturity – “….and we can
prove it”
• Process Stability &
Resilience –  
“Take it to the bank”
• Ensures Repeatability
• Policy, Training,
Quality Assurance, etc.
‹#›
DMM Structure
Core Category
Process
Area
Purpose
Introductory
Notes
Goal(s) of the Process
Area
Core Questions for the Process
Area
Functional Practices (Levels
1-5)
rRelated Process
Areas
Example Work Products
Infrastructure Support
Practices
eExplanatory Model Components Required for Model
Compliance
!18

Maintain fit-for-purpose data,
efficiently and effectively
DMM℠ Structure of  
5 Integrated  
DM Practice Areas
19
Manage data coherently
Manage data assets professionally
Data architecture
implementation
Data lifecycle
implementation
Organizational support
!20
Planning for and managing
data assets as a critical
component of infrastructure,
emphasizing an organization-
wide approach and program
versus project by project,
data store by data store.
8
Data Management Strategy

!21
9
Implementing the building,
nurturing, sustaining, and
controlling power of collective
decision-making, and harnessing
staff expertise for
collaborative development of
knowledge management
Data Governance
!22
10
Comprises a 360 degree and
extensible approach to
improving the quality of
data organization-wide by
thoughtful planning and
integrated best practices.
Data Quality

!23
11
Ensures that
requirements for data
are specified and linked
to business processes
and metadata, enables
data lineage and
authoritative sources,
and exercises controls
and quality improvements
for data provided.
DMM Operations
!24
12
Key considerations for
developing a well-
organized data layer
that meets business
needs, with appropriate
technologies, enabling
integration,
interoperability, and
data provisioning.
Platform and Architecture

!25
Supporting Processes
Practices that
implement organization
and control for all
data management
processes, such as:
developing and
monitoring metrics;
managing risks,
configurations, process
quality and work
products.
• Motivation
• Where to next?
• Q & A?
26

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
27
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"
28
Data Management Strategy
Data Governance
Platform & Architecture
Data Quality
Data Operations
Focus:
Implementation
and Access
Focus:
Guidance and
Facilitation
Optimized(V) 
Measured(IV) 
Defined(III) 
Managed(II) 
Initial(I)

Development guidance
Data Adminstration
Support systems
Asset recovery capability
Development training
0 1 2 3 4 5
Client Industry Competition All Respondents
Data Management Practices Assessment
Challenge
Challenge
Challenge
Data Program
Coordination
Organizational Data
Integration
Data Stewardship
Data Development
Data Support
Operations
29
High Marks for IFC's Audit
30
Leadership & Guidance
Asset Creation
Metadata Management
Quality Assurance
Change Management
Data Quality
0 1 2 3 4 5
TRE ISG IFC Industry Benchmarks Overall Benchmarks

1
2
3
4
5
DataProgramCoordination
OrganizationalDataIntegration
DataStewardship
DataDevelopment
DataSupportOperations
2007 Maturity Levels 2012 Maturity Levels
Comparison of DM Maturity 2007-2012
31
!32Copyright 2019 by Data Blueprint Slide #
improving how the state prices and sells its goods and services, and more efficiently matching
citizens to benefits when they enroll.
“The first year of our data internship partnership has been a success,” said Governor McAuliffe.
“The program has helped the state save time and money by making some of our internal
processes more efficient and modern. And it has given students valuable real-world experience. I
look forward to seeing what the second year of the program can accomplish.”
“Data is an important resource that becomes even more critical as technology progresses,” said
VCU President Michael Rao, Ph.D. “VCU is uniquely positioned, both in its location and
through the wealth of talent at the School of Business, to help state agencies run their data-
centric systems more efficiently, while giving our students hands-on practice in the development
of data systems.”
During their internships, pairs of VCU students work closely with state agency CIOs to identify
specific business cases in which data can be used. Participants gain practical experience in using
data to drive re-engineering, while participating CIOs have concrete examples of how to make
better use of data to provide innovative and less costly services to citizens.
"Working with the talented VCU students gave us a different perspective on what the data was
telling us,” said Dave Burhop, Deputy Commissioner/CIO of the Virginia Department of Motor
Vehicles.
“The VCU interns provided an invaluable resource to the Governor’s Coordinating Council on
Homelessness,” said Pamela Kestner, Special Advisor on Families, Children and Poverty.
“They very effectively reviewed the data assets available in the participating state agencies and
identified analytic content that can be used to better serve the homeless population.”
“It's always useful to have ‘fresh eyes’ on data that we are used to seeing,” said Jim Rothrock,
Commissioner of the Department for Aging and Rehabilitative Services. “Our interns challenged
us and the way we interpret data. It was a refreshing and useful, and we cannot wait for new
experiences with new students.”
The data internships support Governor McAuliffe’s ongoing initiative to provide easier access to
open data in Virginia. The internships also support treating data as an enterprise asset, one of
four strategic goals of the enterprise information architecture strategy adopted by the
Commonwealth in August 2013. Better use of data allows the Commonwealth to identify
opportunities to avoid duplicative costs in collecting, maintaining and using information; and to
integrate services across agencies and localities to improve responses to constituent needs and
optimize government resources.
Virginia Secretary of Technology Karen Jackson and CIO of the Commonwealth Nelson Moe
are leading the effort on behalf of the state. Students who want to apply for internships should
contact Peter Aiken (peter.aiken@vcu.edu) for additional information.
Governor's Data Interns Program

!33
Using DMM in the State of Arizona
• Policies drive change
in state government
• Base policies on a
widely-accepted
framework
!34
DMM supports Arizona Strategy
• Metrics - DMM provides
measurement methodology
• Enterprise Architecture -
DMM provides gap analysis
and a path forward
• Emphasis on Lean - DMM
drives towards
eliminating silos for
improved efficiency

!35
DMM in Arizona – Current State
• Introduced DMM at annual
Arizona Data Management
Conference in January, 2016
• Wide buy-in from multiple
agencies
• “Building EDM Capabilities”
training for 24 students from
11 agencies May 2019
!36
DMM in Arizona
• Students want advanced training
• Students want to help other
agencies – DMM “Swat Team”
• 3rd Annual Data Management
Conference – Spring 2019
• Participating in Governor’s
Goal Council
• To date, 5 agencies have
conducted comprehensive
assessments against the DMM
• DMM adds structure and lends
credibility to the state DM
Program

Five Agencies Conducted DMM Assessments
• Department of Water Resources (Jul 2017)
• Department of Corrections (Aug 2017)
• Health Care Cost Containment System (Sep 2017)
• Department of Economic Services (May 2018)
• Department of Transportation (May 2019)
• Gaps, strengths/achievements, organizational themes, specific fixes
• 5-12 recommended initiatives were proposed for rapid progress
Though each is unique, there were many shared themes, including:
• Data sharing inter- and intra-agency, data provisioning
• Lack of a centralized data management organization – Priority #1
• Lack of agency-wide data governance – Priority #2
• Lack of agency-wide data management strategy – Priority #3
• Lack of a business glossary and metadata strategy – Priority #4
• Agencies are standing up governance leveraging ADOA ASET’s Governance model
Data Stewards  
Computer-Based Training
" ADOA requested an outline for a Data Stewards course to teach key
data management concepts and disciplines
" Audience scope included thousands of employees across Arizona
state agencies, business line, managerial and technical staff
" Overall theme could be summarized as:
○ ‘I’m a Chief Data Officer; I need business engagement’
○ ‘What do all data stewards need to know to be effective?’
○ ‘Clarity = Power;’ ‘Knowledge = Motivation’
" Our team led development and partnered with KIK Consulting to
benefit from additional governance implementation experience
" We created course content – slides and explanatory audio narration
" ADOA Training implemented the content into CBT via its authoring
software
Introduction
Intermediate
Advanced
Apply!
Innovate!
This course will be offered to thousands of Arizona agency staff
– to date over 120 people have completed the course

Outline of Data Stewards Course
" Four 30-minute on-line narrated
modules
" Knowledge about data empowers
people
" Business glossary
" Defining and gathering metadata
" How to read a data model
" Business data requirements
" Forming data work groups
" Improving data quality
" Active leadership for Data Stewards
Compressed delivery of approaches,, skills,
and techniques - everything the data steward
needs to know to be effective
‹#›
Natural events for employing the DMM
• Use Cases - assess current capabilities
before:
• 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:
• 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
!40

Starting the Journey - DMM Assessment Method
• To maximize the DMM’s value as a catalyst for
forging shared perspective and accelerating
programs, our method provides:
– Collaboration launch event with a broad range of
stakeholders
– Capabilities evaluated by consensus affirmations
– Solicits key business input through supplemental
interviews
– Verifies evaluation with work product reviews
(evidence)
– Report and executive briefing presents Scoring,
Findings, Observations, Strengths, and customized
specific Recommendations.
To date, over 1,400 assessment participants from business, IT, and data management
have employed DMM 1.0 - practice by practice, work product by work product - to
evaluate their capabilities.
‹#›
DMM Assessment Summary 
Sample Organization
!42

!43
Cumulative Benchmark – Multiple organizations
!44
DMM Training and Certification
Current Offerings
• Building EDM Capabilities
o Instructor-Led 3-day
interactive class
o eLearning –web-based 8-10 hour
class
• Advancing EDM Capabilities
o Instructor-led 5 day
interactive class
• Enterprise Data Management
Expert (EDME)
o Instructor-led 5 day
interactive class, preparation
for EDME certification
• CMMI now offers the EDM
Associate certification

• Motivation
• Where to next?
• Q & A?
45
Questions?
+ =
46

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