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Data-Ed Webinar: Best Practices with the DMM

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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 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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Data-Ed Webinar: Best Practices with the DMM

  1. 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: July 14, 2015
 Time: 2:00 PM ET
 Presented by: Melanie Mecca & Peter Aiken 1
  2. 2. Presented by Melanie Mecca & Peter Aiken, Ph.D. Data Management Maturity Achieving Best Practices using DMM
  3. 3. Copyright 2013 by Data Blueprint Your Presenters Melanie Mecca • CMMI Institute/
 Director of Data
 Management 
 Products and Services • 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 • 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 7
  4. 4. 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 8
  5. 5. DMMSM Structure 9 Data 
 Governance Data 
 Management
 Strategy Data 
 Quality Data 
 Operations Platform
 Architecture Supporting
 Processes
  6. 6. ‹#› DMM Primer • Reference model of foundational data management practices • Measurement instrument to evaluate capabilities and maturity • Answers the question: “How are we doing?” • Guidelines for: “What should we do next?” • Baseline for: Integrated strategy & high-value specific initiatives / improvements • By CMMI Institute with our Sponsors – Booz Allen Hamilton, Lockheed Martin, Microsoft, and Kingland Systems - and many contributing experts !10
  7. 7. ‹#› DMM Themes • Architecture and technology neutral – applicable to legacy, DW, SOA, unstructured data environments, mainframe-to-Hadoop, etc. • Industry independent – usable by every organization with data assets, applicable to every industry • Emphasis on current state – organization is assessed on the implemented data layer and existing DM processes • Launch collaborative and sustained capability improvement – for the life of the DM program [aka, forever]. If you manage data, the DMM will benefit you 11
  8. 8. Copyright 2013 by Data Blueprint Maslow's Hierarchy of Needs 12
  9. 9. Data Management Practices Hierarchy You can accomplish Advanced Data Practices without becoming proficient in the Foundational Data Management Practices however this will: • Take longer • Cost more • Deliver less • Present 
 greater
 risk
 (with thanks to Tom DeMarco) Advanced 
 Data 
 Practices • MDM • Mining • Big Data • Analytics • Warehousing • SOA Foundational Data Management Practices 13Copyright 2015 by Data Blueprint Slide # Data Platform/Architecture Data Governance Data Quality Data Operations Data Management Strategy Technologies Capabilities
  10. 10. ‹#› Foundation for Business Results • Trusted Data – demonstrated and independently measured capability to assure customer confidence in the data assets • Improved Risk and Analytics Decisions – a comprehensive and measured DM strategy ensures decisions are made based on accurate data • Cost Reduction/Operational Efficiency – clarity about current and target states supports elimination of redundant data and streamlining of DM processes and data stores • Regulatory Compliance – independently evaluated and measured DM capabilities to meet regulator requirements and provide a yardstick within industries.   14
  11. 11. 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 15
  12. 12. 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
  13. 13. 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
  14. 14. 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
  15. 15. ‹#› CMMI Institute Overview • Owned by Carnegie Mellon University • Formed & 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 • Now for-profit, streamlined and focused on responding to business & market requirements • $10 MM business, 24 full-time employees with dedicated training, partner and certification teams to support the ecosystem 19
  16. 16. 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 20
  17. 17. 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… 21
  18. 18. Copyright 2013 by Data Blueprint CMMI Model Portfolio 22 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
  19. 19. ‹#› DMM Drivers and Bio • Data management is broad and complex = challenging • An effective DM program requires a planned strategic effort – not a Project, or a separate Program – a lifestyle. • Organizations needed a comprehensive reference model to precisely evaluate data management capabilities • DMM unifies understanding and priorities of business, IT, and data management. • Foundation for collaborative and sustained capability building. 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 Now–2016 – DMM Ecosystem 23
  20. 20. 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 24
  21. 21. ‹#› DMM and DMBOK CMMI Institute and DAMA International are forming a collaborative partnership 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) • Harmonize DMM and DMBOK offerings as they develop 25
  22. 22. 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
  23. 23. ‹#›27
  24. 24. ‹#› 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 = maximize value, lower costs, happier staff • Process Areas were designed to stand alone for evaluation • Reflects real-world organizations • Flexible for multiple purposes • Whole model • Selected Category(ies) • Specific Process Areas • Relationships are indicated because operationally, “everything is connected” 28
  25. 25. 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 29
  26. 26. ‹#› 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 30
  27. 27. ‹#› Capability and Maturity Disambiguation 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 – “Take it to the bank” • Ensures Repeatability • Policy • Training • Quality Assurance, etc. 31
  28. 28. ‹#› 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 32
  29. 29. Maintain fit-for-purpose data, efficiently and effectively DMM℠ Structure of 
 5 Integrated 
 DM Practice Areas 33 Copyright 2015 by Data Blueprint Manage data coherently Manage data assets professionally Data architecture implementation Data lifecycle implementation Organizational support
  30. 30. DMM Process Areas
 Data Management Strategy 34 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
  31. 31. DMM Process Areas
 Data Governance 35 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
  32. 32. DMM Process Areas
 Data Quality 36 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
  33. 33. 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 37 A collaborative approach to architecting the target state 
 with appropriate standards, controls, and toolsets
  34. 34. DMM Process Areas
 Data Operations 38 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
  35. 35. DMM Process Areas
 Supporting Processes 39 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
  36. 36. 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 40
  37. 37. ‹#› 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 41
  38. 38. 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 42
  39. 39. 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" 43 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)
  40. 40. 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 44 Copyright 2015 by Data Blueprint
  41. 41. High Marks for IFC's Audit 45 Copyright 2015 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
  42. 42. 1 2 3 4 5 DataProgramCoordination OrganizationalDataIntegration DataStewardship DataDevelopment DataSupportOperations 2007 Maturity Levels 2012 Maturity Levels Comparison of DM Maturity 2007-2012 46 Copyright 2015 by Data Blueprint
  43. 43. 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
  44. 44. 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. 48
  45. 45. ‹#› DMM Assessment Summary
 Sample Organization 49
  46. 46. ‹#› Next Step Sample – DM Roadmap Comprehensive and Realistic Roadmap for the Journey 50
  47. 47. ‹#› Cumulative Benchmark – Multiple organizations 51
  48. 48. ‹#› Summary - Why Do a DMM Assessment? • Engage the lines of business through education • Tour de force – learn precisely “How are we doing?” • Clarifies priorities – “What should we do next?” • Industry-wide standard begets confidence • Heals factions and silos – (i.e., improves climate for an organization-wide program) • Creates: • Common concepts, perspective, and terminology • A shared vision and purpose • Baseline for monitoring progress over time 52
  49. 49. 53 Establishing a Common Data Management Language Data Management Maturity Model Microsoft
  50. 50. Strategic Enterprise Architecture Data Manag ement Operat ions Platfor m & Archite cture Data Quality Data Gover nance Data Manag ement Strateg y 54 CMMI Assessment Recommendations • Unified effort to maximize data sharing and quality • Monitor and measure adherence to data standards • Top-down approach to prioritization • Up-stream error prevention • Common Data Definitions • Leverage best practices for data archival and retention • Maximize shared services utilization • Map key business processes to data • Leverage Meta Data repository • Integrate data governance structures • Prioritize policies, processes, standards, to support corporate initiatives Microsoft
  51. 51. Strategic Enterprise Architecture ▪ In the world of Devices and Services, Data Management is a pillar of effectiveness ▪ DMM is a key tool to facilitate the Real-Time Enterprise journey ▪ Active participation of cross-functional teams from Business and IT is key for success ▪ Employee education on the importance of data and the impact of data management is a good investment ▪ Build on Strengths! 55 Key Lessons Microsoft IT Annual Report may be found at: http://aka.ms/itannualreport Microsoft
  52. 52. 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
  53. 53. ‹#› How the DMMSM helps the DM Professional “Help me to help you” – education for roles, complexity, connectedness Integrated 360 degree program level view – launches collaboration, increased involvement of lines of business Actionable and implementable initiatives Strong support for business cases Certification path – defined skillset and industry recognition 57
  54. 54. The DMM Ecosystem 58
  55. 55. ‹#› DMM Ecosystem - Product Suite Overview • Data Management Maturity Model o Comprehensive document with descriptions, practice statements and work products o Enterprise license option • Assessments o Structured, facilitated working sessions resulting in detailed current/future state executive report • Training & Certification o Introductory, Advanced and Expert courses with associated certifications • Formal Measurement/Appraisal (2016) o Benchmark measurement and scoring of capability/maturity level 59
  56. 56. ‹#› DMM Ecosystem – Training Results / Assets Partner Program / Outreach Certifications Product Suite DMM Training Classes • Building EDM Capabilities (3 days) • eLearning Building EDM Capabilities (self- paced, web-based) (10 hours) • Mastering EDM Capabilities (5 days) • Enterprise Data Management Expert (5 days) • Future – EDM Lead Appraiser (5 days) On-site courses available at your location 60
  57. 57. ‹#› DMM Ecosystem - Certifications Certifications: Credentials and Credibility • Enterprise Data Management Expert (EDME) – Assessing and Launching the DM Journey • DMM Lead Appraiser (DMM LA) – Benchmarking and Monitoring Improvements 61
  58. 58. ‹#› DMM Ecosystem – Partner Program 62
  59. 59. ‹#› DMM Ecosystem – Results and Assets Results • Case studies • Best Practice Examples • Benchmarking • Web publication of approved appraisals DMM Assets • Translations (#1 Portuguese) • Seminars (RDA, Governance, Quality) • DMM Compass • Profiles – Regulatory • Academic Courses • White Papers / Articles 63
  60. 60. 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 64
  61. 61. 
 Top Operations Job Top Data Job 65 Copyright 2015 by Data Blueprint 
 Top Job 
 Top
 IT
 Job 
 Top Marketing Job 
 Data Governance Organization 
 Top
 Data 
 Job • Dedicated solely to data asset leveraging • Unconstrained by an IT project mindset • Reporting to the business • There is enough work to justify the function and not much talent • The CDO provides significant input to the Top Information Technology Job • 25 Percent of Large Global Organizations Will Have Appointed Chief Data Officers By 2015 Gartner press release. Gartner website (accessed May 7, 2014). January 30, 2014. http://www.gartner.com/ newsroom/ id/2659215? • By 2020, 60% of CIOs in global organizations will be supplanted by the Chief Digital Officer (CDO) for the delivery of IT-enabled products and digital services (IDC) The Case for the Chief Data Officer Recasting the C-Suite to Leverage Your MostValuable Asset Peter Aiken and Michael Gorman 
 Top 
 Finance 
 Job
  62. 62. The "waterfall" development model - creates more, new data siloes 66 Copyright 2015 by Data Blueprint SoftwareData
  63. 63. Data is not a Project • Durable asset – An asset that has a usable 
 life more than one year • Reasonable project 
 deliverables – 90 day increments – Data evolution is measured in years • Data – Evolves - it is not created – Significantly more stable • Readymade data architectural components – Prerequisite to agile development • Only alternative is to create additional data siloes! 67 Copyright 2015 by Data Blueprint
  64. 64. Evolving Data is Different than Creating New Systems 68 Copyright 2015 by Data Blueprint Common Organizational Data 
 (and corresponding data needs requirements) New Organizational Capabilities Systems Development Activities Create Evolve Future State (Version +1) Data evolution is separate from, external to, and precedes system development life cycle activities!
  65. 65. 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 69
  66. 66. For more information • Feel free to email me: • mmecca@cmmiinstitute.com • And visit our web site: • http://cmmiinstitute.com/DMM 71
  67. 67. Trends in Data Modeling August 11, 2015 @ 2:00 PM ET/11:00 AM PT Data Quality Engineering Sepember 8, 2015 @ 2:00 PM ET/11:00 AM PT Sign up here: www.datablueprint.com/webinar-schedule or www.dataversity.net Copyright 2013 by Data Blueprint 72 Upcoming Events
  68. 68. 10124 W. Broad Street, Suite C Glen Allen, Virginia 23060 804.521.4056

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