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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 …

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.

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

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  • 1. 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
  • 2. Copyright 2013 by Data Blueprint Two Most Commonly Asked Questions 1. Will I get copies of the slides 
 after the event? 2. Is this being recorded so I can 
 view it afterwards? 2
  • 3. Copyright 2013 by Data Blueprint Get Social With Us! Like Us on Facebook www.facebook.com/ datablueprint Post questions and comments Find industry news, insightful content and event updates. Join the Group Data Management & Business Intelligence Ask questions, gain insights and collaborate with fellow data management professionals 
 
 
 
 
 
 
 
 Live Twitter Feed Join the conversation! Follow us: @datablueprint @paiken Ask questions and submit 
 your comments: #dataed 3
  • 4. Copyright 2013 by Data Blueprint 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
  • 5. Presented by Melanie Mecca & Peter Aiken, Ph.D. Data Management Maturity Achieving Best Practices using DMM
  • 6. 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
  • 7. DMMSM Structure 7
  • 8. 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? - “All models are wrong … ” • Where to next? • Q & A? Outline: Design/Manage Data Structures 8
  • 9. Copyright 2013 by Data Blueprint Maslow's Hierarchy of Needs 9
  • 10. Copyright 2013 by Data Blueprint 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
  • 11. 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 Copyright 2013 by Data Blueprint 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
  • 12. 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% Copyright 2013 by Data Blueprint 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
  • 13. Copyright 2013 by Data Blueprint 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
  • 14. Copyright 2013 by Data Blueprint 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
  • 15. 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? - “All models are wrong … ” • Where to next? • Q & A? Outline: Data Management Maturity 15
  • 16. Copyright 2013 by Data Blueprint 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
  • 17. Copyright 2013 by Data Blueprint 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
  • 18. Copyright 2013 by Data Blueprint 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
  • 19. Copyright 2013 by Data Blueprint CMMI – Worldwide Process Improvement • Quick Stats: – Over 10,000 
 organizations – 94 countries – 12 national 
 governments – 10 languages – 500 Partners – 1373 
 appraisals 
 in 2013 19
  • 20. Copyright 2013 by Data Blueprint 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
  • 21. Copyright 2013 by Data Blueprint CMMI Model Portfolio 21 Establish, Manage, and Deliver Services Product Development / Software Engineering Acquire and integrate products / supply chain Workforce development and management
  • 22. 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
  • 23. 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
  • 24. 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
  • 25. 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? - “All models are wrong … ” • Where to next? • Q & A? Outline: Data Management Maturity 25
  • 26. 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
  • 27. 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
  • 28. 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
  • 29. 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
  • 30. • 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
  • 31. 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
  • 32. 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
  • 33. 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
  • 34. 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.
  • 35. Desirable Practice Characteristics 35 Definition Implementation Unambiguous Verifiable Complete Modifiable Correct Understandable Consistent Relevant Concise Implementation Independent Atomic Orthogonal
  • 36. 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
  • 37. DMMSM Structure 37
  • 38. 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. Copyright 2013 by Data Blueprint 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
  • 39. DMM Process Areas
 Data Management Strategy 39 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
  • 40. DMM Process Areas
 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
  • 41. DMM Process Areas
 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
  • 42. 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 DMM Process Areas
 Platform & Architecture 42 A collaborative approach to architecting the target state 
 with appropriate standards, controls, and toolsets
  • 43. DMM Process Areas
 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
  • 44. DMM Process Areas
 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
  • 45. 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? - “All models are wrong … ” • Where to next? • Q & A? Outline: Data Management Maturity 45
  • 46. 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
  • 47. 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
  • 48. Copyright 2013 by Data Blueprint 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
  • 49. Copyright 2013 by Data Blueprint 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 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)
  • 50. Data Management Strategy Data Governance Data Platform & Architecture Data Quality Data Operations 0 1 2 3 4 5 Client Industry Competition All Respondents Copyright 2013 by Data Blueprint Comparative Assessment Results Challenge Challenge Challenge 50
  • 51. 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.
  • 52. 52
  • 53. Score Ranges – DMM-Assessed Organizations 53
  • 54. 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 .
  • 55. 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
  • 56. 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
  • 57. 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
  • 58. 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? - “All models are wrong … ” • Where to next? • Q & A? Outline: Data Management Maturity 58
  • 59. Copyright 2013 by Data Blueprint 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”
  • 60. 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
  • 61. 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
  • 62. DMM Certification • Enterprise Data Management Expert – Prerequisites • DMM Advanced Concepts • Meet qualifications • Application / Resume / Interview – Complete Course – Pass Exam – Assessment Observation – Certification Awarded 62
  • 63. DMM Partner Program 63 DMM Evolution Community Certified Individuals Priority Access Early Insight Beta Testing
  • 64. Copyright 2013 by Data Blueprint Upcoming Events September Webinar:
 Data Governance 
 September 9, 2014
 
 ! Sign up here: • www.datablueprint.com/webinar-schedule • www.Dataversity.net ! ! ! ! ! ! Brought to you by: 64
  • 65. Copyright 2013 by Data Blueprint Questions? + = 65
  • 66. For more information ! • Feel free to email me: • mmecca@cmmiinstitute.com ! • And visit our web site: • http://cmmiinstitute.com/DMM 66
  • 67. 10124 W. Broad Street, Suite C Glen Allen, Virginia 23060 804.521.4056