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 participationDate: August 12, 2014Time: 2:00 PM ETPresented by: Melanie Mecca & Peter Aiken

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Your PresentersMelanie 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

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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.

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DMMSM Structure

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• 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

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Maslow's Hierarchy of Needs

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Foundation for Advanced SolutionsYou can accomplish Advanced Data Practices without becoming proficient in the Basic Data Management Practices however this will: • Take longer • Cost more • Deliver less • Present

greaterrisk Basic Data Management Practices

Advanced Data

Practices • MDM • Mining • Big Data • Analytics • Warehousing • SOA

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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)

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Source: Standish Chaos Reports as reported at: http://www.galorath.com/wp/software-project-failure-costs-billions-better-estimation-planning-can-help.php

General LiteratureHardwareComputer Systems OrganizationSoftware/Software EngineeringDataTheory of ComputationMathematics of ComputingInformation Technology and SystemsComputing MethodologiesComputer ApplicationsComputing 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’)

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Bad Data Decisions Spiral

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Bad data decisions

Most CIOs are not data

knowledgable

Poor treatment of organizational data

assets

C-level decision makers are not

data knowledgable

Poorqualitydata

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]

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• Motivation








• Where to next?

• Q & A?

Outline: Data Management Maturity

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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

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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.

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Acknowledgements

0018-9162/07/$25.00 © 2007 IEEE42 Computer P u b l i s h e d b y t h e I E E E C o m p u t e r S o c i e t y

version (changing data into other forms, states, orproducts), or scrubbing (inspecting and manipulat-ing, recoding, or rekeying data to prepare it for sub-sequent use).

• Approximately two-thirds of organizational datamanagers have formal data management training;slightly more than two-thirds of organizations useor plan to apply formal metadata management tech-niques; and slightly fewer than one-half manage theirmetadata using computer-aided software engineer-ing tools and repository technologies.3

When combined with our personal observations, theseresults suggest that most organizations can benefit fromthe application of organization-wide data managementpractices. Failure to manage data as an enterprise-, cor-porate-, or organization-wide asset is costly in terms ofmarket share, profit, strategic opportunity, stock price,and so on. To the extent that world-class organizationshave shown that opportunities can be created throughthe effective use of data, investing in data as the onlyorganizational asset that can’t be depleted should be ofgreat interest.

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 andbetween organizations, the problems that canresult from poor data management practicesare becoming more apparent. Studies haveshown that such poor practices are widespread.

For example,

• PricewaterhouseCoopers reported that in 2004, onlyone in three organizations were highly confident intheir own data, and only 18 percent were very con-fident in data received from other organizations.Further, just two in five companies have a docu-mented board-approved data strategy (www.pwc.com/extweb/pwcpublications.nsf/docid/15383D6E748A727DCA2571B6002F6EE9).

• Michael Blaha1 and others in the research communityhave cited past organizational data management edu-cation and practices as the cause for poor databasedesign being the norm.

• According to industry pioneer John Zachman,2 orga-nizations typically spend between 20 and 40 percentof their information technology budgets evolving theirdata via migration (changing data locations), con-

Measuring Data ManagementPractice 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

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CMMI – Worldwide Process Improvement• Quick Stats:

– Over 10,000 organizations

– 94 countries

– 12 national governments

– 10 languages

– 500 Partners

– 1373 appraisals in 2013

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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 performancePercentage of Projects on Time By Process Framework Adoption

Key Finding: Process Frameworks are not Created EqualWith the exception of CMM and ITIL, use of process-efficiency frameworks does not predict higher on-budget project delivery…

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CMMI Model Portfolio

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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.

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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

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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.

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• Motivation








• Where to next?

• Q & A?


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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

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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.

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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

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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”

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• 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

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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

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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

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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

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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

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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

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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

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DMMSM Structure

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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

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DMM Process AreasData Management Strategy

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Name Description

Data Management Strategy

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

DMM Process AreasData Governance

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Data Governance Governance Management Structure of data governance, governance processes and

leadership, metrics development and monitoring Business Glossary 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

DMM Process AreasData Quality

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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 distributionData 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

DMM Process AreasPlatform & Architecture

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A collaborative approach to architecting the target state with appropriate standards, controls, and toolsets

DMM Process AreasData Operations

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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

DMM Process AreasSupporting Processes

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Supporting Processes Adapted from CMMIMeasurement 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?


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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)

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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

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Assessment Components

Data Management Practice Areas


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 – PerformedOur DM practices are ad hoc and dependent upon "heroes" and heroic efforts

2 – ManagedWe 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 – MeasuredWe 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

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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"

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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)


Data Governance

Data Platform & Architecture

Data Quality

Data Operations

0 1 2 3 4 5Client Industry Competition All Respondents


Comparative Assessment ResultsChallenge

Challenge

Challenge

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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

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DMM Section / Process Area DMBOK 2.0 Knowledge AreaFocuses 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

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.

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

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How the DMMSM Helps the Organization

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Gradated path -step-by-step improvements

!Unambiguous practice statements for clear understanding

Functional work products to aid implementation

Common language Shared understanding of progress

Acceleration

How the DMMSM helps the DM Professional

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“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?


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George Edward Pelham Box

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• 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

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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

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DMM Certification

• Enterprise Data Management Expert

– Prerequisites

• DMM Advanced Concepts

• Meet qualifications

• Application / Resume / Interview

– Complete Course

– Pass Exam

– Assessment Observation

– Certification Awarded

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DMM Partner Program

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DMM Evolution

Community Certified Individuals

Priority Access

Early Insight

Beta Testing


Upcoming EventsSeptember Webinar:

Data Governance September 9, 2014

!Sign up here: • www.datablueprint.com/webinar-schedule • www.Dataversity.net !!!!!!Brought to you by:

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Questions?

+ =

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For more information

!• Feel free to email me: • [email protected]

!• And visit our web site: • http://cmmiinstitute.com/DMM

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

data-ed online: data management maturity model

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