Se ha denunciado esta presentación.
Utilizamos tu perfil de LinkedIn y tus datos de actividad para personalizar los anuncios y mostrarte publicidad más relevante. Puedes cambiar tus preferencias de publicidad en cualquier momento.

DataEd Slides: Data Governance Strategies

567 visualizaciones

Publicado el

Much like project management and home improvements, Data Governance sounds a lot simpler than it actually is. In a nutshell, Data Governance can be explained as “managing data with guidance.” In general, the perceived utility of these programs increases with the specificity of desired data and processing improvements. Whether restarting or starting your Data Governance programs, it is critical to be guided by a periodically revised Data Strategy that links support for organizational strategy to specific operational data improvements. Understanding these and other aspects of governance is necessary to eliminate the ambiguity that often surrounds the implementation of effective Data Management and stewardship programs.

This webinar will:
- Illustrate what Data Governance functions are required for effective Data Management, how they fit with other Data Management practice areas, and why Data Governance has been tricky for many organizations
- Illustrate the utility of a detailed focus and set of narratives to facilitate understanding of your business objectives and imperatives that demand governance
- Provide direction for selling Data Governance to organizational management as a specifically motivated initiative.

Learning Objectives:
- Reorient the focus of Data Governance to an improvable process
- Recognize guiding principles and lessons learned
- Understand foundational Data Governance concepts based on the DAMA DMBOK

Publicado en: Datos y análisis
  • Sé el primero en comentar

DataEd Slides: Data Governance Strategies

  1. 1. Peter Aiken, Ph.D. Data Governance Strategies Copyright 2019 by Data Blueprint Slide # !1 • DAMA International President 2009-2013 / 2018 • DAMA International Achievement Award 2001 
 (with Dr. E. F. "Ted" Codd • DAMA International Community Award 2005 Peter Aiken, Ph.D. !2Copyright 2019 by Data Blueprint Slide # • I've been doing this a long time • My work is recognized as useful • Associate Professor of IS (vcu.edu) • Founder, Data Blueprint (datablueprint.com) • DAMA International (dama.org) • 10 books and dozens of articles • Experienced w/ 500+ data management practices worldwide • Multi-year immersions – US DoD (DISA/Army/Marines/DLA) – Nokia – Deutsche Bank – Wells Fargo – Walmart – … PETER AIKEN WITH JUANITA BILLINGS FOREWORD BY JOHN BOTTEGA MONETIZING DATA MANAGEMENT Unlocking the Value in Your Organization’s Most Important Asset.
  2. 2. Infogix Confidential Copyright 2019 About Infogix • Innovating data solutions since 1982 • Data Governance; Data Quality and Data Analytics • Large and mid-size customers world-wide: • Organizations rely on Infogix so they can trust their data • Average customer tenure > 18 years “ I n d u s t r i e s t h a t t h r i v e o n d a t a ”
  3. 3. Infogix Confidential Copyright 2019 • For many a “strategy” happens the second time around • Little grounding in the reality of business and related business proposition • A strategy perspective promotes new operational model around data • A long trail of woes… Governance Strategy – Some Observations
  4. 4. Infogix Confidential Copyright 2019 When you are starting, what does “good” look like? Industry Frameworks Data Frameworks Relatively few frameworks anchored in data best practices Governance Framework
  5. 5. Data Governance Strategies !3Copyright 2019 by Data Blueprint Slide # • Data's Confounding Characteristics – General low understanding leads to uneven application – Data is uniquely-valuable – Organizational data is largely comprised of ROT – The case for data governance • Strategy #1: Keep DG practically focused – Discipline is immature – "By the book" is not a good starting place – A more targeted approach to DG • Strategy #2: DG = HR at the programmatic level – DG is central to DM – Must be de-coupled from IT strategy – Directly supportive of organizational strategy • Strategy #3: Gradually add ingredients – Frameworks/Stewards – Checklists/Scorecards – Avoid worst practices • Data Governance in Action (Storytelling) • Take Aways/References/Q&A Confusion • IT thinks data is a business problem – "If they can connect to the server, then my job is done!" • The business thinks IT is managing data adequately – "Who else would be taking care of it?" !4Copyright 2019 by Data Blueprint Slide #
  6. 6. Complex & detailed • Outsiders do not want to hear about
 or discuss any aspects of 
 challenges/solutions • Most are unqualified re: architecture/ engineering Taught inconsistently • Focus is on technology • Business impact is 
 not addressed
 
 
 Not well understood • (Re)learned by every
 workgroup • Lack of standards/ poor literacy/
 unknown dependencies Wally Easton Playing Piano 
 https://www.youtube.com/watch?v=NNbPxSvII-Q As a topic, Data has confounding characteristics !5Copyright 2019 by Data Blueprint Slide # !6Copyright 2019 by Data Blueprint Slide #
  7. 7. !7Copyright 2019 by Data Blueprint Slide # Bad Data Decisions Spiral !8Copyright 2019 by Data Blueprint Slide # Bad data decisions Technical deci- sion makers are not data knowledgable Business decision makers are not data knowledgable Poor organizational outcomes Poor treatment of organizational data assets Poor
 quality
 data
  8. 8. Separating the Wheat from the Chaff !9Copyright 2019 by Data Blueprint Slide # Separating the Wheat from the Chaff • Better organized data increases in value • Poor data management practices are costing organizations much money/time/effort • Minimally 80% of organizational data is ROT – Redundant – Obsolete – Trivial • The question is – Which data to eliminate? !10Copyright 2019 by Data Blueprint Slide # Incomplete
  9. 9. • Reduce the amount of organizational data ROT – Redundant, obsolete, trivial • Recycle – Methods, best practices, successful approaches • Reuse the remainder – Fewer vocabulary items to resolve – Greater quality engineering leverage Reduce-Recycle-Reuse … Data? !11Copyright 2019 by Data Blueprint Slide # Data Assets Win! Data 
 Assets Financial 
 Assets Real
 Estate Assets Inventory Assets Non- depletable Available for subsequent use Can be 
 used up Can be 
 used up Non- degrading √ √ Can degrade
 over time Can degrade
 over time Durable Non-taxed √ √ Strategic Asset √ √ √ √ Data Assets Win! • Today, data is the most powerful, yet underutilized and poorly managed organizational asset • Data is your – Sole – Non-depletable – Non-degrading – Durable – Strategic • Asset – Data is the new oil! – Data is the new (s)oil! – Data is the new bacon! • As such, data deserves: – It's own strategy – Attention on par with similar organizational assets – Professional ministration to make up for past neglect !12Copyright 2019 by Data Blueprint Slide # Asset: A resource controlled by the organization as a result of past events or transactions and from which future economic benefits are expected to flow [Wikipedia]
  10. 10. Why is Data Governance important? • Cost organizations millions each year in – Productivity – Redundant and siloed efforts – Poorly thought out hardware and software purchases – Delayed decision making using inadequate information – Reactive instead of proactive initiatives – 20-40% of IT spending can be reduced through better data governance !13Copyright 2019 by Data Blueprint Slide # Data Governance Strategies !14Copyright 2019 by Data Blueprint Slide # • Data's Confounding Characteristics – General low understanding leads to uneven application – Data is uniquely-valuable – Organizational data is largely comprised of ROT – The case for data governance • Strategy #1: Keep DG practically focused – Discipline is immature – "By the book" is not a good starting place – A more targeted approach to DG • Strategy #2: DG = HR at the programmatic level – DG is central to DM – Must be de-coupled from IT strategy – Directly supportive of organizational strategy • Strategy #3: Gradually add ingredients – Frameworks/Stewards – Checklists/Scorecards – Avoid worst practices • Data Governance in Action (Storytelling) • Take Aways/References/Q&A
  11. 11. What is the world's oldest profession? !15Copyright 2019 by Data Blueprint Slide # Augusta Ada King
 Countess of Lovelace
 (1815-52) • 8,000+ years • formalize practices • GAAP It is appropriate that we (data professionals) acknowledge that we are currently not as mature a discipline as we would like to be but it is not okay for our discipline to remain in its current state of maturity 7 Data Governance Definitions • The formal orchestration of people, process, and technology to enable 
 an organization to leverage data as an enterprise asset. - The MDM Institute • A convergence of data quality, data management, business process management, and risk management surrounding the handling of data in an organization – Wikipedia • A system of decision rights and accountabilities for information-related processes, executed according to agreed-upon models which describe who can take what actions with what information, and when, under what circumstances, using what methods – Data Governance Institute • The execution and enforcement of authority over the management of data assets and the performance of data functions – KiK Consulting • A quality control discipline for assessing, managing, using, improving, monitoring, maintaining, and protecting organizational 
 information – IBM Data Governance Council • Data governance is the formulation of policy to optimize, secure, 
 and leverage information as an enterprise asset by aligning the 
 objectives of multiple functions – Sunil Soares • The exercise of authority and control over the 
 management of data assets – DM BoK !16Copyright 2019 by Data Blueprint Slide #
  12. 12. !17Copyright 2019 by Data Blueprint Slide # Managing Data with Guidance What is Data Governance? Ask anyone ... • Would you want your sole, non- depletable, non- degrading, durable asset managed without guidance? !18Copyright 2019 by Data Blueprint Slide #
  13. 13. !19Copyright 2019 by Data Blueprint Slide # Managing Data with Guidance What is Data Governance? !20Copyright 2019 by Data Blueprint Slide # Managing Data Decisions with Guidance What is Data Governance?
  14. 14. The DAMA Guide to the Data Management Body of Knowledge • Published by DAMA International – The professional association for Data Managers (40 chapters worldwide) • DM BoK organized around – Primary data management functions focused around data delivery to the organization – Organized around several environmental elements !21Copyright 2019 by Data Blueprint Slide # Data 
 Management Functions By the Book !22Copyright 2019 by Data Blueprint Slide # Data Strategy Data Governance
 Strategy Metadata
 Strategy Data 
 Quality
 Strategy BI/ Warehouse 
 Strategy Data
 Architecture
 Strategy Master/
 Reference 
 Data
 Strategy Document/
 Content
 Strategy Database
 Strategy Data
 Acquisition
 Strategy X
  15. 15. Version 1 !23Copyright 2019 by Data Blueprint Slide # Data Strategy Data Governance
 Strategy Data 
 Quality
 Strategy Master/
 Reference 
 Data
 Strategy Perfecting operations in 3 data management practice areas Version 2 !24Copyright 2019 by Data Blueprint Slide # Data Strategy Data Governance
 Strategy BI Warehouse Strategy Data
 Operations
 Strategy Perfecting operations in 3 data management practice areas
  16. 16. Data Governance from the DMBOK !25Copyright 2019 by Data Blueprint Slide # from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International Corporate Governance • "Corporate governance - which can be defined narrowly as the relationship of a company to its shareholders or, more broadly, as its relationship to society….", 
 Financial Times, 1997. • "Corporate governance is about promoting corporate fairness, transparency and accountability" James Wolfensohn, World Bank, President Financial Times, June 1999. • “Corporate governance deals with the ways in which suppliers of finance to corporations assure themselves of getting a return on their investment”,
 The Journal of Finance, Shleifer and Vishny, 1997. !26Copyright 2019 by Data Blueprint Slide #
  17. 17. Definition of IT Governance IT Governance: • "putting structure around how organizations align IT strategy with business strategy, ensuring that companies stay on track to achieve their strategies and goals, and implementing good ways to measure IT’s performance. • It makes sure that all stakeholders’ interests 
 are taken into account and that processes
 provide measurable results. • An IT governance framework should 
 answer some key questions, such 
 as how the IT department is functioning 
 overall, what key metrics management 
 needs and what return IT is giving back 
 to the business from the investment it’s 
 making." CIO Magazine (May 2007) IT Governance Institute, five areas of focus: • Strategic Alignment • Value Delivery • Resource Management • Risk Management • Performance Measures !27Copyright 2019 by Data Blueprint Slide # Organizational Data Governance Purpose Statement • What does data governance mean to my organization? – Managing data with guidance – Getting some individuals (whose opinions matter) – To form a body (needs a formal purpose/authority) – Who will advocate/evangelize for (not dictate, enforce, rule) – Increasing scope and rigor of – Data-centric development practices !28Copyright 2019 by Data Blueprint Slide #
  18. 18. !29Copyright 2019 by Data Blueprint Slide # Organizational
 Strategy Data Strategy IT Projects Organizational Operations Data Governance Data Strategy and Data Governance in Context Data asset support for 
 organizational strategy What the data assets do to support strategy How well the data strategy is working Operational feedback How data is delivered by IT How IT supports strategy Other aspects of organizational strategy !30Copyright 2019 by Data Blueprint Slide # Data Strategy Data Governance Data Strategy & Data Governance What the data assets do to support strategy
 How well the data strategy is working
 (Business Goals) (Metadata)
  19. 19. What is the Difference Between DG and DM? • Data Governance – Policy level guidance – Setting general guidelines and direction – Example: All information not marked public should be considered confidential • Data Management – The business function of planning for, controlling and delivering data/information assets – Example: Delivering data 
 to solve business challenges !31Copyright 2019 by Data Blueprint Slide # DIP Implementation !32Copyright 2019 by Data Blueprint Slide # DataLeadership Feedback Feedback Data Governance Data Improvement DataStewards DataCommunityParticipants DataGenerators/DataUsers Data
 Things 
 Happen Organizational
 Things 
 Happen DIPs Data Improves Over
 Time Data Improves As A Result of Focus
  20. 20. Data Governance Strategies !33Copyright 2019 by Data Blueprint Slide # • Data's Confounding Characteristics – General low understanding leads to uneven application – Data is uniquely-valuable – Organizational data is largely comprised of ROT – The case for data governance • Strategy #1: Keep DG practically focused – Discipline is immature – "By the book" is not a good starting place – A more targeted approach to DG • Strategy #2: DG = HR at the programmatic level – DG is central to DM – Must be de-coupled from IT strategy – Directly supportive of organizational strategy • Strategy #3: Gradually add ingredients – Frameworks/Stewards – Checklists/Scorecards – Avoid worst practices • Data Governance in Action (Storytelling) • Take Aways/References/Q&A Differences between Programs and Projects • Programs are Ongoing, Projects End – Managing a program involves long term strategic planning and 
 continuous process improvement is not required of a project • Programs are Tied to the Financial Calendar – Program managers are often responsible for delivering 
 results tied to the organization's financial calendar • Program Management is Governance Intensive – Programs are governed by a senior board that provides direction, 
 oversight, and control while projects tend to be less governance-intensive • Programs Have Greater Scope of Financial Management – Projects typically have a straight-forward budget and project financial management is focused on spending to budget while program planning, management and control is significantly more complex • Program Change Management is an Executive Leadership Capability – Projects employ a formal change management process while at the program level, change management requires executive leadership skills and program change is driven more by an organization's strategy and is subject to market conditions and changing business goals !34Copyright 2019 by Data Blueprint Slide # Adapted from http://top.idownloadnew.com/program_vs_project/ and http://management.simplicable.com/management/new/program-management-vs-project-management Your data program must last at least as long as your HR program!
  21. 21. IT Project or Application-Centric Development Original articulation from Doug Bagley @ Walmart !35Copyright 2019 by Data Blueprint Slide # Data/ Information IT
 Projects 
 Strategy • In support of strategy, organizations implement IT projects • Data/information are typically considered within the scope of IT projects • Problems with this approach: – Ensures data is formed to the applications and not around the organizational-wide information requirements – Process are narrowly formed around applications – Very little data reuse is possible Data-Centric Development Original articulation from Doug Bagley @ Walmart !36Copyright 2019 by Data Blueprint Slide # IT
 Projects Data/
 Information 
 Strategy • In support of strategy, the organization develops specific, shared data-based goals/objectives • These organizational data goals/ objectives drive the development of specific IT projects with an eye to organization-wide usage • Advantages of this approach: – Data/information assets are developed from an organization-wide perspective – Systems support organizational data 
 needs and compliment organizational 
 process flows – Maximum data/information reuse
  22. 22. Data Strategy in Context !37Copyright 2019 by Data Blueprint Slide # Organizational
 Strategy IT Strategy Data Strategy Organizational
 Strategy IT Strategy Data Strategy This is wrong! !38Copyright 2019 by Data Blueprint Slide # Organizational
 Strategy IT Strategy Data Strategy
  23. 23. Organizational
 Strategy IT Strategy This is correct … !39Copyright 2019 by Data Blueprint Slide # Data Strategy 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! !40Copyright 2019 by Data Blueprint Slide #
  24. 24. Data programmes preceding software development !41Copyright 2019 by Data Blueprint Slide # Common Organizational Data 
 (and corresponding data needs requirements) New Organizational Capabilities Systems Development Activities Build Evolve Future State (Version +1) Data evolution is separate from, external to, and precedes system development life cycle activities! Data management and software development must be separated and sequenced Mismatched railroad tracks non aligned Copyright 2019 by Data Blueprint Slide # !42 Data programmes preceding software development
  25. 25. Data Governance Strategies !43Copyright 2019 by Data Blueprint Slide # • Data's Confounding Characteristics – General low understanding leads to uneven application – Data is uniquely-valuable – Organizational data is largely comprised of ROT – The case for data governance • Strategy #1: Keep DG practically focused – Discipline is immature – "By the book" is not a good starting place – A more targeted approach to DG • Strategy #2: DG = HR at the programmatic level – DG is central to DM – Must be de-coupled from IT strategy – Directly supportive of organizational strategy • Strategy #3: Gradually add ingredients – Frameworks/Stewards – Checklists/Scorecards – Avoid worst practices • Data Governance in Action (Storytelling) • Take Aways/References/Q&A Data Governance Strategies !44Copyright 2019 by Data Blueprint Slide # • Data's Confounding Characteristics – General low understanding leads to uneven application – Data is uniquely-valuable – Organizational data is largely comprised of ROT – The case for data governance • Strategy #1: Keep DG practically focused – Discipline is immature – "By the book" is not a good starting place – A more targeted approach to DG • Strategy #2: DG = HR at the programmatic level – DG is central to DM – Must be de-coupled from IT strategy – Directly supportive of organizational strategy • Strategy #3: Gradually add ingredients – Frameworks/Stewards – Checklists/Scorecards – Avoid worst practices • Data Governance in Action (Storytelling) • Take Aways/References/Q&A
  26. 26. !45Copyright 2019 by Data Blueprint Slide # • Before further construction could proceed • No IT equivalent Our barn had to pass a foundation inspection Data Governance Frameworks • A system of ideas for guiding analyses • A means of organizing 
 project data • Priorities for data decision making • A means of assessing progress – Don’t put up walls until foundation inspection is passed – Put the roof on ASAP • Make it all dependent upon continued funding !46Copyright 2019 by Data Blueprint Slide #
  27. 27. Data Governance from the DMBOK !47Copyright 2019 by Data Blueprint Slide # from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International Data Governance Institute • A system of ideas for guiding analyses • A means of organizing project data • Data integration priorities decision making framework • A means of assessing progress !48Copyright 2019 by Data Blueprint Slide # http://www.datagovernance.com/
  28. 28. KiK Consulting • A system of ideas for guiding analyses • A means of organizing project data • Data integration priorities decision making framework • A means of assessing progress !49Copyright 2019 by Data Blueprint Slide # http://www.kikconsulting.com/ IBM Data Governance Council • A system of ideas for guiding analyses • A means of organizing project data • Data integration priorities decision making framework • A means of assessing progress !50Copyright 2019 by Data Blueprint Slide # http://www-01.ibm.com/software/data/system-z/data-governance/workshops.html
  29. 29. Elements of Effective Data Governance !51Copyright 2019 by Data Blueprint Slide # See IBM Data Governance Council, http://www-01.ibm.com/software/tivoli/ governance/servicemanagement/ data-governance.html. Baseline Consulting (sas.com) !52Copyright 2019 by Data Blueprint Slide #
  30. 30. American College Personnel Association !53Copyright 2019 by Data Blueprint Slide # Making a Better 
 Data Governance Sandwich !54Copyright 2019 by Data Blueprint Slide #
  31. 31. Standard data Data supply Data literacy Making a Better Data Governance Sandwich !55Copyright 2019 by Data Blueprint Slide # Data literacy Standard data Data supply Making a Better Data Governance Sandwich !56Copyright 2019 by Data Blueprint Slide # Standard data Data supply Data literacy
  32. 32. Making a Better Data Sandwich !57Copyright 2019 by Data Blueprint Slide # Standard data Data supply Data literacy This cannot happen without engineering and architecture! Quality engineering/
 architecture work products 
 do not happen accidentally! Making a Better Data Sandwich !58Copyright 2019 by Data Blueprint Slide # Standard data Data supply Data literacy This cannot happen without data engineering and architecture! Quality data engineering/
 architecture work products 
 do not happen accidentally!
  33. 33. Getting Started !59Copyright 2019 by Data Blueprint Slide # Assess context Define DG roadmap Secure executive mandate Assign Data Stewards Execute plan Evaluate results Revise plan Apply change management (Occurs once) (Repeats) Primary Deliverables • Data Policies • Data Standards • Resolved Issues • Data Management Projects and Services • Quality Data and Information • Recognized Data Value Illustration from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International !60Copyright 2019 by Data Blueprint Slide #
  34. 34. Roles and Responsibilities • Suppliers: – Business Executives – IT Executives – Data Stewards – Regulatory Bodies • Consumers: – Data Producers – Knowledge Workers – Managers and Executives – Data Professionals – Customers • Participants: – Executive Data Stewards – Coordinating Data Stewards – Business Data Stewards – Data Professionals – DM Executive – CIO Illustration from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International !61Copyright 2019 by Data Blueprint Slide # Practices and Techniques !62Copyright 2019 by Data Blueprint Slide # Illustration from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International • Data Value • Data Management Cost • Achievement of Objectives • # of Decisions Made • Steward Representation/Coverage • Data Professional Headcount • Data Management Process Maturity
  35. 35. 4 four up Goals and Principles • To define, approve, and communicate data strategies, policies, standards, architecture, procedures, and metrics. • To track and enforce regulatory compliance and conformance to data policies, standards, architecture, and procedures. • To sponsor, track, and oversee the delivery of data management projects and services. • To manage and resolve data related issues. • To understand and promote the value of data assets. Illustration from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International 132Copyright 2017 by Data Blueprint Slide # Primary Deliverables • Data Policies • Data Standards • Resolved Issues • Data Management Projects and Services • Quality Data and Information • Recognized Data Value Illustration from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International 133Copyright 2017 by Data Blueprint Slide # Roles and Responsibilities • Suppliers: – Business Executives – IT Executives – Data Stewards – Regulatory Bodies • Consumers: – Data Producers – Knowledge Workers – Managers and Executives – Data Professionals – Customers • Participants: – Executive Data Stewards – Coordinating Data Stewards – Business Data Stewards – Data Professionals – DM Executive – CIO Illustration from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International 134Copyright 2017 by Data Blueprint Slide # Practices and Techniques • Data Value • Data Management Cost • Achievement of Objectives • # of Decisions Made • Steward Representation/Coverage • Data Professional Headcount • Data Management Process Maturity 135Copyright 2017 by Data Blueprint Slide # Illustration from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International !63Copyright 2019 by Data Blueprint Slide # Encountergoverneddatamoredirectly←|→Encountergoverneddatalessdirectly Moretimeisdedicated←|→Lesstimeisdedicated Copyright 2019 by Data Blueprint Slide # Components comprising the data community IT/Systems Development Leadership Stewards Domain expertise is less ← | → Domain expertise is greater Roles more formally defined ← |→ Roles less formally defined Guidance Decisions 
 Data Community Participants (DCPs)/ Subject Matter Experts (SMEs) Generators/
 Users IT/SystemsDevelopment Ideas Data/feedback Action Changes Data/Feedback R esources
  36. 36. Data Steward • Business data steward – Manage from the perspective of business elements (i.e. business definitions 
 and data quality) • Technical data steward – Focus on the use of data by systems and models (i.e. code operation) • Project data steward – Gather definitions, quality rules and issues for referral to business/technical stewards • Domain data steward – Manage data/metadata required across multiple business areas (i.e. customer data) • Operational data steward – Directly input data or instruct those who do; aid business 
 stewards identifying root cause and addressing issues • Metadata Data Steward – Manage metadata as an asset • Legacy Data Steward – Manage legacy data as an asset • Data steward auditor – Ensures compliance with data guidance • Data steward manager – Planning, organizing, leading and controlling !65Copyright 2019 by Data Blueprint Slide # (list adapted from Plotkin, 2014) one who actively directs the use of 
 organizational data assets in support 
 of specific mission objectives • one who actively directs !66Copyright 2019 by Data Blueprint Slide # Steward, Data
  37. 37. Goals and Principles • To define, approve, and communicate data strategies, policies, standards, architecture, procedures, and metrics. • To track and enforce regulatory compliance and conformance to data policies, standards, architecture, and procedures. • To sponsor, track, and oversee the delivery of data management projects and services. • To manage and resolve data related issues. • To understand and promote the value of data assets. Illustration from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International !67Copyright 2019 by Data Blueprint Slide # Data Governance Checklist ✓ Decision-Making Authority ✓ Standard Policies and Procedures ✓ Data Inventories ✓ Data Content Management ✓ Data Records Management ✓ Data Quality ✓ Data Access ✓ Data Security and Risk Management !68Copyright 2019 by Data Blueprint Slide # Source: “Data Governance Checklist for Educators” by Angela Guess; http://www.dataversity.net/archives/5198
  38. 38. Scorecard: Data Governance Practices/Techniques • Data Value • Data Management 
 Cost • Achievement of 
 Objectives • # of Decisions Made • Steward Representation/Coverage • Data Professional Headcount • Data Management Process Maturity !69Copyright 2019 by Data Blueprint Slide # from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International What do I include in my Data Governance Program? • Security and Privacy of Data • Quality of Data • Life Cycle Management • Risk Management • Content Valuation • Standards (Data Design, Models and Tools) • Governance Tool Kits and Case Studies !70Copyright 2019 by Data Blueprint Slide #
  39. 39. In what order do I incorporate in these into my DG Program? 1. Risk Management 2. Security and Privacy of Data 3. Content Valuation 4. Quality of Data 5. Life Cycle Management 6. Standards (Data Design, Models and Tools) 7. Governance Tool Kits and Case Studies !71Copyright 2019 by Data Blueprint Slide # 10 DG Worst Practices 1. Buy-in but not Committing: Business vs. IT 2. Ready, Fire, Aim 3. Trying to Solve World Hunger or Boil the Ocean 4. The Goldilocks Syndrome 5. Committee Overload 6. Failure to Implement 7. Not Dealing with Change Management 8. Assuming that Technology Alone is the Answer 9. Not Building Sustainable and Ongoing Processes 10. Ignoring “Data Shadow Systems” !72Copyright 2019 by Data Blueprint Slide #
  40. 40. Data Governance Strategies !73Copyright 2019 by Data Blueprint Slide # • Data's Confounding Characteristics – General low understanding leads to uneven application – Data is uniquely-valuable – Organizational data is largely comprised of ROT – The case for data governance • Strategy #1: Keep DG practically focused – Discipline is immature – "By the book" is not a good starting place – A more targeted approach to DG • Strategy #2: DG = HR at the programmatic level – DG is central to DM – Must be de-coupled from IT strategy – Directly supportive of organizational strategy • Strategy #3: Gradually add ingredients – Frameworks/Stewards – Checklists/Scorecards – Avoid worst practices • Data Governance in Action (Storytelling) • Take Aways/References/Q&A Use Their Language ... • Getting access to data around here is like that Catherine Zeta Jones scene where she is having to get thru all those lasers … !74Copyright 2019 by Data Blueprint Slide #
  41. 41. Toyota versus Detroit Engine Mounting (Circa 1994) • Detroit – 3 different bolts – 3 different wrenches – 3 different bolt inventories • Toyota – 1 bolt used 
 for all three assemblies – 1 bolt inventory – 1 type of wrench !75Copyright 2019 by Data Blueprint Slide # Toyota versus Detroit Engine Mounting (Circa 1994) • Detroit – many different bolts – many different wrenches – many different bolt inventories • Toyota – same bolts used for all three assemblies – same 1 bolt inventory – same 1 type of wrench !76Copyright 2019 by Data Blueprint Slide #
  42. 42. Formalizing the Role of U.S. Army Data Governance !77Copyright 2019 by Data Blueprint Slide # Suicide Mitigation !78Copyright 2019 by Data Blueprint Slide #
  43. 43. !79Copyright 2019 by Data Blueprint Slide # 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 !80Copyright 2019 by Data Blueprint Slide #
  44. 44. Senior Army Official • Room full of Colonels • A very heavy dose of management support • Advised the group of his opinion on the matter • Any questions as to future direction – "They should make an appointment to speak directly with me!" • Empower the team – The conversation turned from "can this be done?" to "how are we going to accomplish this?" – Mistakes along the way would be tolerated – Implement a workable solution in prototype form !81Copyright 2019 by Data Blueprint Slide # Communication Patterns • !82Copyright 2019 by Data Blueprint Slide # Source: The Challenge and the Promise: Strengthening the Force, Preventing Suicide and Saving Lives - The Final Report of the Department of Defense Task Force on the Prevention of Suicide by Members of the Armed Forces - August 2010
  45. 45. Vocabulary is Important-Tank, Tanks, Tankers, Tanked !83Copyright 2019 by Data Blueprint Slide # How one inventory item proliferates data throughout the chain !84Copyright 2019 by Data Blueprint Slide # 555 Subassemblies & subcomponents 17,659 Repair parts or Consumables System 1:
 18,214 Total items
 75 Attributes/ item
 1,366,050 Total attributes System 2
 47 Total items
 15+ Attributes/item
 720 Total attributes System 3 16,594 Total items 73 Attributes/item 1,211,362 Total attributes System 4
 8,535 Total items
 16 Attributes/item
 136,560 Total attributes System 5
 15,959 Total items
 22 Attributes/item
 351,098 Total attributes Total for the five systems show above:
 59,350 Items
 179 Unique attributes
 3,065,790 values
  46. 46. Business Implications • National Stock Number (NSN) 
 Discrepancies – If NSNs in LUAF, GABF, and RTLS are 
 not present in the MHIF, these records 
 cannot be updated in SASSY – Additional overhead is created to correct 
 data before performing the real 
 maintenance of records • Serial Number Duplication – If multiple items are assigned the same 
 serial number in RTLS, the traceability of 
 those items is severely impacted – Approximately $531 million of SAC 3 
 items have duplicated serial numbers • On-Hand Quantity Discrepancies – If the LUAF O/H QTY and number of items serialized in RTLS conflict, there can be no clear answer as to how many items a unit actually has on-hand – Approximately $5 billion of equipment does not tie out between the LUAF and RTLS !85Copyright 2019 by Data Blueprint Slide # Barclays Excel Spreadsheet Horror • Barclays preparing to buy Lehman’s Brothers assets. • 179 dodgy Lehman’s contracts were almost accidentally purchased by Barclays because of an Excel spreadsheet reformatting error • A first-year associate reformatted an Excel contracts spreadsheet – Predictably, this work was done long after normal business hours, just after 11:30 p.m... • The Lehman/Barclays sale closed on September 22nd • the 179 contracts were marked as “hidden” in Excel, and those entries became “un-hidden” when when globally reformatting the document … • … and the sale closed … !86Copyright 2019 by Data Blueprint Slide #
  47. 47. Mizuho Securities !87Copyright 2019 by Data Blueprint Slide # 
 
 CLUMSY typing cost a Japanese bank at least £128 million and staff their Christmas bonuses yesterday, after a trader mistakenly sold 600,000 more shares than he should have. The trader at Mizuho Securities, who has not been named, fell foul of what is known in financial circles as “fat finger syndrome” where a dealer types incorrect details into his computer. He wanted to sell one share in a new telecoms company called J Com, for 600,000 yen (about £3,000). Possibly the Worst Data Governance Example Mizuho Securities • Wanted to sell 1 share for 600,000 yen • Sold 600,000 shares for 1 yen • $347 million loss • In-house system did not have limit checking • Tokyo stock exchange system did not have limit checking ... • … and doesn't allow order cancellations !88Copyright 2019 by Data Blueprint Slide #
  48. 48. Data Governance Strategies !89Copyright 2019 by Data Blueprint Slide # • Data's Confounding Characteristics – General low understanding leads to uneven application – Data is uniquely-valuable – Organizational data is largely comprised of ROT – The case for data governance • Strategy #1: Keep DG practically focused – Discipline is immature – "By the book" is not a good starting place – A more targeted approach to DG • Strategy #2: DG = HR at the programmatic level – DG is central to DM – Must be de-coupled from IT strategy – Directly supportive of organizational strategy • Strategy #3: Gradually add ingredients – Frameworks/Stewards – Checklists/Scorecards – Avoid worst practices • Data Governance in Action (Storytelling) • Take Aways/References/Q&A Data Governance Strategies !90Copyright 2019 by Data Blueprint Slide # • Data's Confounding Characteristics – General low understanding leads to uneven application – Data is uniquely-valuable – Organizational data is largely comprised of ROT – The case for data governance • Strategy #1: Keep DG practically focused – Discipline is immature – "By the book" is not a good starting place – A more targeted approach to DG • Strategy #2: DG = HR at the programmatic level – DG is central to DM – Must be de-coupled from IT strategy – Directly supportive of organizational strategy • Strategy #3: Gradually add ingredients – Frameworks/Stewards – Checklists/Scorecards – Avoid worst practices • Data Governance in Action (Storytelling) • Take Aways/References/Q&A
  49. 49. Take Aways • Need for DG is increasing – Increase in data volume – Lack of practice improvement • DG is a new discipline – Must conform to constraints – No one best way • DG must be driven by a data strategy complimenting organizational strategy • DG Strategy #1: Keep it practically focused • DG Strategy #2: Implement DG (and data) as a program not a project • DG Strategy #3: Gradually add ingredients !91Copyright 2019 by Data Blueprint Slide # IT Business Data Perceived State of Data !92Copyright 2019 by Data Blueprint Slide #
  50. 50. Data Desired To Be State of Data !93Copyright 2019 by Data Blueprint Slide # IT Business The Real State of Data !94Copyright 2019 by Data Blueprint Slide # Data IT Business
  51. 51. References Websites • Data Governance Book Data Governance Book Compliance Book !95Copyright 2019 by Data Blueprint Slide # IT Governance Books !96Copyright 2019 by Data Blueprint Slide #
  52. 52. + = Questions? !97Copyright 2019 by Data Blueprint Slide # It’s your turn! 
 Use the chat feature or Twitter (#dataed) to submit your questions now! Upcoming Events July Webinar
 Data Modeling Fundamentals: 
 July 9, 2019 @ 2:00 PM ET August Webinar
 Data Management versus
 Data Strategy 
 August 13, 2019 @ 2:00 PM ET 
 Sign up for webinars at: 
 www.datablueprint.com/webinar-schedule !98Copyright 2019 by Data Blueprint Slide # Brought to you by:
  53. 53. 10124 W. Broad Street, Suite C Glen Allen, Virginia 23060 804.521.4056 Copyright 2019 by Data Blueprint Slide # !99

×