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

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ince its release in 2014, the CMMI/Data Management Maturity (DMM)℠ model has become the de facto standard for planning and implementing programmatic improvements to organizational Data Management programs. It permits organizations to evaluate its current-state Data Management capabilities and discover gaps to remediate and strengths to leverage. The DMM reveals priorities, business needs, and a clear, rapid path for process improvements. This webinar will describe the DMM framework for assessing an organization's Data Management capabilities, 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 widespread basis for partnerships
- New industry assessment standard is based on successful CMM/CMMI foundation
- A clear need for Data Strategy
- A clear and unambiguous call for participation

Published in: Data & Analytics
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DataEd Slides: Data Management Maturity - Achieving Best Practices Using DMM

  1. 1. 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
  2. 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.  
  3. 3. 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: Data Management Maturity 5 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 6
  4. 4. 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. 7 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- 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
  5. 5. !9 CMMI Institute Background • Evolved from Carnegie Mellon’s Software Engineering Institute (SEI) - a federally funded research and development center (FFRDC) • Continues to support and provide all CMMI offerings and services delivered over its 20+ year history at the SEI o Industry leading reference models - benchmarks and guidelines for improvement – Development, Acquisition, Services, People, Data Management o Training and Certification program, Partner program • Dedicated training, partner and certification teams to support organizations and professionals • Now owned by ISACA (CISO/M, COBIT, IT Governance, Cybersecurity) and joint product offerings are planned !10 CMMI – Worldwide Process Improvement CMMI Quick Stats: • Over 10,000 organizations • 94 countries • 12 National governments • 10 languages • 500 Partners • 1950+ Appraisals in 2018
  6. 6. 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… 11 !12 DMM and DMBOK CMMI Institute and DAMA International are collaborating to: • Eliminate any confusion between the two tools and highlight their complementarity • Extend and enhance data management training for organizations and professionals • Provide benefits to DAMA members (members receive a discount for our public training classes)
  7. 7. 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: Data Management Maturity 13 !14 Data Management Maturity (DMM)SM Model • DMM 1.0 released August 2014 o 3.5 years in development o Sponsors – Microsoft, Lockheed Martin, Booz Allen Hamilton o 50+ contributing authors, 70+ peer reviewers, 80+ orgs • Reference model framework of fundamental best practices o 414 specific practice statements o 596 functional work products o Maturity practices • Measurement Instrument for organizations to evaluate capabilities and maturity, identify gaps, and incorporate guidelines for improvements.
  8. 8. !15 “You Are What You DO” • Model emphasizes behavior o Proactive positive behavioral changes o Creating and carrying out effective, repeatable processes o Leveraging and extending across the organization • Activities result in work products o Processes, standards, guidelines, templates, policies, etc. o Reuse and extension = maximum value, lower costs, happier staff • Practical focus reflects real- world organizations – enterprise program evolving to all hands on deck. One concept for process improvement, others include: • Norton Stage Theory •TQM •TQdM •TDQM • ISO 9000
 and focus on understanding current processes and determining where to make improvements. 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 16
  9. 9. !17 DMM Capability Levels Performed Managed Defined Measured Optimized Level 1 Level 2 Level 3 Level 4 Level 5 Risk Quality Ad hoc Reuse Stress Clarity Capability – “We can do this” • Specific Practices - “We’re doing it well” • Work Products - “We’ve documented the processes we are following” (processes, work products, guidelines, standards, etc.) Maturity – “….and we can prove it” • Process Stability & Resilience – 
 “Take it to the bank” • Ensures Repeatability • Policy, Training, Quality Assurance, etc. ‹#› DMM Structure Core Category Process Area Purpose Introductory Notes Goal(s) of the Process Area Core Questions for the Process Area Functional Practices (Levels 1-5) rRelated Process Areas Example Work Products Infrastructure Support Practices eExplanatory Model Components Required for Model Compliance !18
  10. 10. Maintain fit-for-purpose data, efficiently and effectively DMM℠ Structure of 
 5 Integrated 
 DM Practice Areas 19 Copyright 2019 by Data Blueprint Manage data coherently Manage data assets professionally Data architecture implementation Data lifecycle implementation Organizational support !20 Planning for and managing data assets as a critical component of infrastructure, emphasizing an organization- wide approach and program versus project by project, data store by data store. 8 Data Management Strategy
  11. 11. !21 9 Implementing the building, nurturing, sustaining, and controlling power of collective decision-making, and harnessing staff expertise for collaborative development of knowledge management Data Governance !22 10 Comprises a 360 degree and extensible approach to improving the quality of data organization-wide by thoughtful planning and integrated best practices. Data Quality
  12. 12. !23 11 Ensures that requirements for data are specified and linked to business processes and metadata, enables data lineage and authoritative sources, and exercises controls and quality improvements for data provided. DMM Operations !24 12 Key considerations for developing a well- organized data layer that meets business needs, with appropriate technologies, enabling integration, interoperability, and data provisioning. Platform and Architecture
  13. 13. !25 Supporting Processes Practices that implement organization and control for all data management processes, such as: developing and monitoring metrics; managing risks, configurations, process quality and work products. 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: Data Management Maturity 26
  14. 14. 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 27 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" 28 Data Management Strategy Data Governance Platform & Architecture Data Quality Data Operations Focus: Implementation and Access Focus: Guidance and Facilitation Optimized(V)
 Measured(IV)
 Defined(III)
 Managed(II)
 Initial(I)
  15. 15. Development guidance Data Adminstration Support systems Asset recovery capability Development training 0 1 2 3 4 5 Client Industry Competition All Respondents Data Management Practices Assessment Challenge Challenge Challenge Data Program Coordination Organizational Data Integration Data Stewardship Data Development Data Support Operations 29 Copyright 2019 by Data Blueprint High Marks for IFC's Audit 30 Copyright 2019 by Data Blueprint 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
  16. 16. 1 2 3 4 5 DataProgramCoordination OrganizationalDataIntegration DataStewardship DataDevelopment DataSupportOperations 2007 Maturity Levels 2012 Maturity Levels Comparison of DM Maturity 2007-2012 31 Copyright 2019 by Data Blueprint !32Copyright 2019 by Data Blueprint Slide # improving how the state prices and sells its goods and services, and more efficiently matching citizens to benefits when they enroll. “The first year of our data internship partnership has been a success,” said Governor McAuliffe. “The program has helped the state save time and money by making some of our internal processes more efficient and modern. And it has given students valuable real-world experience. I look forward to seeing what the second year of the program can accomplish.” “Data is an important resource that becomes even more critical as technology progresses,” said VCU President Michael Rao, Ph.D. “VCU is uniquely positioned, both in its location and through the wealth of talent at the School of Business, to help state agencies run their data- centric systems more efficiently, while giving our students hands-on practice in the development of data systems.” During their internships, pairs of VCU students work closely with state agency CIOs to identify specific business cases in which data can be used. Participants gain practical experience in using data to drive re-engineering, while participating CIOs have concrete examples of how to make better use of data to provide innovative and less costly services to citizens. "Working with the talented VCU students gave us a different perspective on what the data was telling us,” said Dave Burhop, Deputy Commissioner/CIO of the Virginia Department of Motor Vehicles. “The VCU interns provided an invaluable resource to the Governor’s Coordinating Council on Homelessness,” said Pamela Kestner, Special Advisor on Families, Children and Poverty. “They very effectively reviewed the data assets available in the participating state agencies and identified analytic content that can be used to better serve the homeless population.” “It's always useful to have ‘fresh eyes’ on data that we are used to seeing,” said Jim Rothrock, Commissioner of the Department for Aging and Rehabilitative Services. “Our interns challenged us and the way we interpret data. It was a refreshing and useful, and we cannot wait for new experiences with new students.” The data internships support Governor McAuliffe’s ongoing initiative to provide easier access to open data in Virginia. The internships also support treating data as an enterprise asset, one of four strategic goals of the enterprise information architecture strategy adopted by the Commonwealth in August 2013. Better use of data allows the Commonwealth to identify opportunities to avoid duplicative costs in collecting, maintaining and using information; and to integrate services across agencies and localities to improve responses to constituent needs and optimize government resources. Virginia Secretary of Technology Karen Jackson and CIO of the Commonwealth Nelson Moe are leading the effort on behalf of the state. Students who want to apply for internships should contact Peter Aiken (peter.aiken@vcu.edu) for additional information. Governor's Data Interns Program
  17. 17. !33 Using DMM in the State of Arizona • Policies drive change in state government • Base policies on a widely-accepted framework !34 DMM supports Arizona Strategy • Metrics - DMM provides measurement methodology • Enterprise Architecture - DMM provides gap analysis and a path forward • Emphasis on Lean - DMM drives towards eliminating silos for improved efficiency
  18. 18. !35 DMM in Arizona – Current State • Introduced DMM at annual Arizona Data Management Conference in January, 2016 • Wide buy-in from multiple agencies • “Building EDM Capabilities” training for 24 students from 11 agencies May 2019 !36 DMM in Arizona • Students want advanced training • Students want to help other agencies – DMM “Swat Team” • 3rd Annual Data Management Conference – Spring 2019 • Participating in Governor’s Goal Council • To date, 5 agencies have conducted comprehensive assessments against the DMM • DMM adds structure and lends credibility to the state DM Program
  19. 19. Five Agencies Conducted DMM Assessments • Department of Water Resources (Jul 2017) • Department of Corrections (Aug 2017) • Health Care Cost Containment System (Sep 2017) • Department of Economic Services (May 2018) • Department of Transportation (May 2019) • Gaps, strengths/achievements, organizational themes, specific fixes • 5-12 recommended initiatives were proposed for rapid progress Though each is unique, there were many shared themes, including: • Data sharing inter- and intra-agency, data provisioning • Lack of a centralized data management organization – Priority #1 • Lack of agency-wide data governance – Priority #2 • Lack of agency-wide data management strategy – Priority #3 • Lack of a business glossary and metadata strategy – Priority #4 • Agencies are standing up governance leveraging ADOA ASET’s Governance model Data Stewards 
 Computer-Based Training " ADOA requested an outline for a Data Stewards course to teach key data management concepts and disciplines " Audience scope included thousands of employees across Arizona state agencies, business line, managerial and technical staff " Overall theme could be summarized as: ○ ‘I’m a Chief Data Officer; I need business engagement’ ○ ‘What do all data stewards need to know to be effective?’ ○ ‘Clarity = Power;’ ‘Knowledge = Motivation’ " Our team led development and partnered with KIK Consulting to benefit from additional governance implementation experience " We created course content – slides and explanatory audio narration " ADOA Training implemented the content into CBT via its authoring software Introduction Intermediate Advanced Apply! Innovate! This course will be offered to thousands of Arizona agency staff – to date over 120 people have completed the course
  20. 20. Outline of Data Stewards Course " Four 30-minute on-line narrated modules " Knowledge about data empowers people " Business glossary " Defining and gathering metadata " How to read a data model " Business data requirements " Forming data work groups " Improving data quality " Active leadership for Data Stewards Compressed delivery of approaches,, skills, and techniques - everything the data steward needs to know to be effective ‹#› Natural events for employing the DMM • Use Cases - assess current capabilities before: • Developing or enhancing DM program / strategy • Embarking on a major architecture transformation • Establishing data governance • Expansion / enhancement of analytics • Implementing a data quality program • Implementing a metadata repository • Designing and implementing multi-LOB solutions: • Master Data Management • Shared Data Services • Enterprise Data Warehouse • Implementing an ERP • Other multi-business line efforts. Like an Energy audit or an executive physical !40
  21. 21. Starting the Journey - DMM Assessment Method • To maximize the DMM’s value as a catalyst for forging shared perspective and accelerating programs, our method provides: – Collaboration launch event with a broad range of stakeholders – Capabilities evaluated by consensus affirmations – Solicits key business input through supplemental interviews – Verifies evaluation with work product reviews (evidence) – Report and executive briefing presents Scoring, Findings, Observations, Strengths, and customized specific Recommendations. To date, over 1,400 assessment participants from business, IT, and data management have employed DMM 1.0 - practice by practice, work product by work product - to evaluate their capabilities. ‹#› DMM Assessment Summary
 Sample Organization !42
  22. 22. !43 Cumulative Benchmark – Multiple organizations !44 DMM Training and Certification Current Offerings • Building EDM Capabilities o Instructor-Led 3-day interactive class o eLearning –web-based 8-10 hour class • Advancing EDM Capabilities o Instructor-led 5 day interactive class • Enterprise Data Management Expert (EDME) o Instructor-led 5 day interactive class, preparation for EDME certification • CMMI now offers the EDM Associate certification
  23. 23. 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: Data Management Maturity 45 Copyright 2013 by Data Blueprint Questions? + = 46
  24. 24. 10124 W. Broad Street, Suite C Glen Allen, Virginia 23060 804.521.4056

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