Implementing the Data Maturity Model (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
• 1900+
Appraisals in
2016

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
CMMI Model Portfolio
12
Establish, Manage,
and Deliver
Services
Product
Development /
Software
Engineering
Acquire and
integrate products
/ supply chain
Workforce
development and
management
Rearchitecting to present a more unified/modular offering

!13
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?
14

!15
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.
!16
“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
17
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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
!19
Maintain fit-for-purpose data,
efficiently and effectively
DMM℠ Structure of  
5 Integrated  
DM Practice Areas
20
Manage data coherently
Manage data assets professionally
Data architecture
implementation
Data lifecycle
implementation
Organizational support

!21
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
!22
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

!23
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
!24
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

!25
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
!26
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?
27
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
28

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"
29
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
30

High Marks for IFC's Audit
31
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
32

!33Copyright 2015 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
!34
Using DMM in the State of Arizona
• Policies drive change
in state government
• Base policies on a
widely-accepted
framework

!35
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
!36
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 20 students from
11 agencies March 2017

!37
DMM in Arizona – Next Steps
• Students want advanced training
• Students want to help other
agencies – DMM “Swat Team”
• 2nd Annual Data Management
Conference – April 26, 27
• Participating in Governor’s Goal
Council
• Planning DMM assessments for 3-4
agencies
• DMM adds structure and lends
credibility to the state DM
Program
‹#›
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
!38

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 800 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
!40

!41
Cumulative Benchmark – Multiple organizations
!42
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
• (Near Future) DMM Associate
certification.

ENTERPRISE DATA MANAGEMENT VISION
• The State of Arizona is conducting a multi-year statewide data
management initiative to support:
o The Governor’s “Arizona Management System,” an innovative Lean
based approach instituted by the current governor to streamline & meet
the state’s goal to be: “The #1 state to live, work, play, recreate,
retire, visit, do business, and get an education.”
o Employee education to empower them to employ standards, methods
and tools for data-driven decision-making and disciplined problem
solving, which lead to greater creativity, control, and productivity in
citizen service
o Arizona realizes that trusted, accurate and sustainable data is essential
to successful data-driven decision making and accurate metrics,
and that a state-wide enterprise data management platform is critical
o The state is also focusing on modernizing its data layer and technology
stack to enable needed data sharing and protect privacy.
MULTI-YEAR EDM PROGRAM
• The Arizona Strategic Enterprise Technology office, within the Department
of Administration, developed a five-year Indefinite Delivery Indefinite
Quantity (IDIQ) contract to include the following services (selected):
o Review, assess and measure data management maturity, using the CMMI
Institute’s Data Management Maturity Model as a framework
o Make recommendations for a roadmap to maturity for state agencies to
achieve target maturity levels
o Train and educate ADOA-ASET, business data stewards, and management
staff to perform DMM Assessments and appraisals
o Recommend policies, standards, and processes
o Provide advisory services for best practice implementation.

DMM AND DATA DRIVEN DECISION MAKING
• The CMMI Institute was among the three vendors selected to provide the
IDIQ’s broad range of services over a five-year period, along with Quest
Communications and Data Blueprint
• We are working with the State of Arizona to implement a statewide data
management program to BUILD*IMPROVE*MEASURE its data
management processes and capabilities utilizing the DMM.
• We are teaming with the ADOA-ASET to help construct measurable inter-
and intra-agency DMM programs to:
o Provide education in data management best practices
o Evaluate data management practices in numerous agencies
o Assist the state in evolving a trusted data environment
o Help ASET develop the roles and resources to support the state-wide program.
USING DMM IN THE STATE OF ARIZONA
• Policies are driving collaboration
and change in state government
• Basing data management
policies on a widely-accepted
framework

DMM SUPPORTS ARIZONA STRATEGY
• Metrics – AMS requires metrics-
driven decisions, and DMM provides
a measurement methodology
• Enterprise Architecture - DMM
provides gap analysis and a path
forward for data sharing
• Emphasis on Lean - DMM drives
towards eliminating silos for
improved effectiveness and
efficiency
DMM IN ARIZONA – RECENT ACTIVITIES
• Introduced DMM at annual Arizona Data
Management Conference in January 2016
• Wide buy-in from multiple agencies
• Held CMMI Institute capability briefing for
key state data management personnel
• “Building EDM Capabilities” training for
20 students from 11 agencies in March 2017

2ND ANNUAL DATA MANAGEMENT CONFERENCE
• Hosted by ADOA-ASET, led by our POC Jeff Wolkove, Enterprise
Architect
• Over 75 attendees representing 40+ agencies
• Keynote speaker was State Chief Operating Officer, Henry Darwin
• Presented at state-wide Data Management Conference April 2017
o Moderated data management panel discussion
o Overview - Data Management Capability Evaluation
o Role of Data Governance in EDM
o Break Down Silos by Managing Data as Meaning (seminar)
DMM IN ARIZONA – NEXT STEPS
• Building EDM Capabilities attendees want advanced training in
the DMM
• Students want to help other agencies – DMM “Swat Teams”
• ASET is participating in Governor’s Goal Council representing
data management issues
• Planning DMM assessments for 3-4 agencies
• Department of Corrections - Jun 2017
• Department of Water Resources - Jul 2017
• Health Care Cost Containment System – Aug 2017
• And more in the works….
• DMM adds structure and lends credibility to the state Data
Management Program
• Arizona has set a target for 35 cabinet-level agencies to achieve
DMM Level 3 in the next three years.

OPPORTUNITIES FOR STATES AND TERRITORIES
• Arizona is providing leadership and vision which can be leveraged for other states and territories
• Analytics and automation, internet of things, unstructured data are testing status quo of staffing,
technology, and data management needs
• States are undertaking modernization of their technologies and processes
• The Arizona RFP has been provided to New York and Virginia, which has begun data management
capability evaluation for multiple agencies through the Virginia Commonwealth University graduate
school
• We presented a webinar on state-wide data management programs, featuring Jeff Wolkove, to the
National Association of State Chief Information Officers (NASCIO) in April 2017
• We would like to working with additional states; this will increase adoption of the DMM as a de facto
global standard and provide opportunities for our Partners and EDMEs.
WHAT CAN GO WRONG?
• It’s estimated that the average hospital has 8-12% duplicate records, and as many as 10% of incoming patients
are misidentified
• Sharing of patient data from disparate providers increases likelihood of duplicates for HIEs due to defects in MPIs
• Preventable medical errors may include:
o Misdiagnosis and incorrect treatment procedures
o Incorrect or wrong dose of medication
o Incorrect blood type
o Unknown medication allergies
o Repeated diagnostic tests
• Cost to correct a duplicate record estimated at $1000
• Claims may be rejected
• Increased risk of lawsuits.

IMPROVED DATA QUALITY LEADS TO MANY BENEFITS
• Decreased operational risk through improvements to the quality of patient demographic
data. Specifically, patient safety is protected, and the delivery of patient care improves 
• Increased operational efficiency, requiring less manual effort to fix data issues, fewer
duplicate test orders for patients, and adoption of standard data representations 
• Improved interoperability and data integration through adopting data standards and
data management practices that are followed by staff across the patient lifecycle 
• Improved staff productivity by expending fewer hours on detecting and remediating data
defects to perform their tasks - Increased staff awareness for contributing to and
following processes that improve patient identity integrity
THE DMM WAS SELECTED AS THE FOUNDATION
• An HHS ONC Community of Practice (CoP) for patient demographic data quality was convened in 2015 with government,
health care provider, and industry association members. The CoP analyzed available data management frameworks to
determine which encompassed the practices that health care providers and staff needed to improve patient data quality
across the entire organization. 
• They determined that the DMM was most closely aligned with the needs of the health care
industry for a comprehensive standard, due to its:
o Behavioral, fact-based approach
o Organization-wide scope
o Built-in path for improvements
o Technology and architecture neutrality
o Enables a rapid and precise evaluation of the current state of data management practices

DEVELOPMENT ACTIVITIES WITH HHS ONC
• CMMI Institute was commissioned to develop a practical, implementable framework for HHS
ONC - focused on advancing the goal of improved patient data quality:
o Describing the organizational behavior reflecting sound data management principles in a health care context
o Specific data management practices in the context of patient demographic data
o Set of work products (policies, plans, processes, standards, etc.) supporting the new or improved capabilities 
• Working with HHS ONC and our prime, Audacious Inquiry, the team researched ONC
publications, industry surveys and studies. We then analyzed the DMM to determine the process
areas (topics / disciplines) and functional practices addressing the scope:
o Demographic data used for record matching
o Relevant to the patient care lifecycle and stakeholders
o Most critical to assessing and monitoring demographic data quality.
ONC ‘s “Patient Identification and Matching Final Report,” February 7, 2014
DMM TRANSFORMATION RESULTS
• Eliminated Process Areas less directly related to the scope - 19 versus 25 
• Practice statements were transformed into questions – 76 versus 414 statements
o Invites self-administration, elicits discussion
o Encourages stakeholder representation across patient care lifecycle 
• Five capability Levels 1-5 became three capability Tiers – Foundational,
Building, and Advanced 
• Example work products – 152 versus 596 
• 96 versus 240+ pages 
• Contextual examples throughout
Patient Demographic Data Quality
Framework

DESIGNED FOR A COLLABORATIVE APPROACH
• Managing data is
primarily a people
problem, not a ‘system
problem’
• Patient data primarily
originates in Registration
• However - it can be
modified at any point
throughout the patient
care lifecycle
No one individual knows everything about the patient data
Industry-wide, realization of the long-term goal,
adoption of consistent data standards and standard
matching algorithms, would increase interoperability
and minimize duplicates
• Staff who have access to the
patient record, should be engaged
in decisions about standards,
formats, term, values, etc.
• PDDQ questions are designed to
be posed in a group setting
• Consensus decisions about the
current state
• Key processes and standards
should be agreed to, standardized,
and followed.
HHS ONC ADVOCATES A NEW ROLE
• Who should lead data quality plans and improvements within a health care provider organization? ONC proposed
that a Data Quality Coordinator (DQC) for patient demographic data should be designated for each organization
o Leads the PDDQ evaluation effort
o Coordinates process improvement efforts, for example:
• Establishing data governance
• Defining terms, formats and values for demographic data
• Developing quality rules
• Creating a data quality plan for the organization
o In a small practice, one individual or a part-time individual
• In a large practice, may be a group led by a DQC 
• Establishing this role demonstrates commitment

AUDIENCE FOR THE PDDQ
• The PDDQ was designed for any organization creating, managing, or aggregating
patient data (e.g., master patient index, MPI):
o Hospitals and Health systems which deliver patient care
o Health Information Organizations (HIOs) which facilitate health information exchange (HIE) among
and between multiple stakeholders
o Master Data Management and Master Patient Index solution vendors which provide patient
databases that enable enterprise wide patient data management
o Health Information Exchange (HIE) vendors which enable doctors, nurses, pharmacists, other
health care providers and patients to electronically access and securely share patient data
o Inpatient or outpatient Electronic Health Record (EHR) vendors who manage patient data
from clinicians
EVALUATION QUESTIONS – DATA QUALITY PLANNING
No context or elaborations are displayed in the table above

CONTEXTUAL INFORMATION
• Practice Evaluation Questions are supported by an explanation using health care examples
• Example from Data Profiling:
2.1 Hasthe organization definedanapproachandmethodfor profilingadataset?
A data profiling method is a planned approach to analyzing data sets that is not
A data profiling method is a planned approach to analyzing data sets that is not restricted to a
specific technology solution. The method serves as a process guide that defines the types of
analyses to be performed, their rationale, relevant scenarios, high-level activity steps, tests and
rules to be applied, as well as report templates for results. The goal is to define the process
steps and supporting work products so they are reusable across various data stores.
One of the most common types of advanced profiling methods is aimed at the identification and
resolution of duplicate records in a data set. The patient matching algorithms used across the
healthcare industry are a classic example of both the target objective, and the difficulty in
stabilizing profiling methods that work. Most algorithms for determining duplicates may require
trial and error, as well as customization that is achieved through numerous iterations of data
analysis and standardization.

SAMPLE SUMMARY DIAGRAM
• This example depicts an
organization with strong
data management
capabilities 
• HHS ONC recommends that
all health care organizations
with patient data work to
achieve a score of Tier 3 in
all PDDQ Process Areas 
• The online PDDQ score
graphic is a horizontal
stacked bar chart

HOW TO INTERPRET THE SCORES
Process Area scores may fall into a range from 0 to 3:
• 0 - 1 Foundational: Data is managed as a requirement for projects and
processes
• 1 - 2 Building: Building on the successful completion of Tier 1, data is
increasingly managed as a critical infrastructural asset
• 2 - 3 Advanced features capabilities that comprise completion of the
practices needed for a sound and sustainable program for managing
patient demographic data across the health care lifecycle.
COLLABORATION AND COOPERATION
• Patient data is a common thread across the entire provider organization
o Health care units tend to focus on their mission –diagnostic imaging, pharmacy,
laboratory, claims
o Capturing or modifying patient data differently magnifies the potential for
duplicates 
• PDDQ questions zero in on key processes that may already be performed,
but typically not examined organization-wide 
• Participants can raise issues, make collective decisions (How Are We
Doing?), and suggest improvements 
• The DQC leads the coordination of implementation activities, monitors
progress

PRECISE SNAPSHOT OF CURRENT STATE
• PDDQ means that health care organizations don’t have to guess about how they’re managing their
patient data
• A PDDQ evaluation of current practices will:
• Enable clear identification of any gaps
• Enable staff to understand that their activities affect the quality of patient data (not just ‘system problems’)
• Create awareness about practices not followed for all care areas
• Discover useful work products elsewhere in the organization, and discover missing work products (e.g., no data
entry standards)
• Give everyone a voice - each learns what others are doing, leading to acceptance
of results and further cooperation
• Set a baseline for monitoring progress
ACTIONABLE IMPROVEMENTS
• Once gaps and strengths, have been identified, the organization can quickly move to establishing
new capabilities and creating supporting work products – for example:
o Data Governance – determining the care areas and roles that are engaged with patient demographic
data, forming governance group
o Business Terms – collaborative agreements on demographic data – definitions, values approved by
stakeholders across the lifecycle
o Data Quality Plan – organization-wide plan for improving data quality, which may include new quality
rules, registration procedures, data profiling, frequent monitoring, matching algorithm upgrades, etc. 
• The organization determines its priorities and a timeline to meet objectives, including resources
and roles.

IF YOU’RE A HEALTH CARE DATA PROFESSIONAL
• The PDDQ can assist you in many ways:
o Speeds up your analysis of organization’s DM capabilities
o Engages stakeholders and provides consensus pre-approval for initiatives
• Stakeholders see the gaps – definitive and understand recommended improvements
• Facilitates funding and resource commitments
o Opportunity for multiple implementation projects
o Opportunity for new role – Data Quality Coordinator 
• Taken together, helps accelerate your career 
• Recommended first step – master the PDDQ 
• Achieve EDME Certification – master the recommended method
ACCESS THE PDDQ
• https://www.healthit.gov/playbook/pddq-
framework/

• Motivation
• Where to next?
• Q & A?
69
LEARN MORE
• http://cmmiinstitute.com/patient-demographic-data-quality-pddq-framework - PDDQ page
• http://cmmiinstitute.com/sites/default/files/resource_asset/PDDQ_White_Paper_0.pdf - PDDQ white
paper
• http://cmmiinstitute.com/sites/default/files/resource_asset/PDDQ%20Article.pdf - Article - Improving
Patient Data Quality: An Introduction to the Patient Demographic Data Quality (PDDQ) Framework

Questions?
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Implementing the Data Maturity Model (DMM)

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