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Data-Ed: Data Governance Strategies

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The data governance function exercises authority and control over the management of your mission critical assets and guides how all other data management functions are performed. When selling data governance to organizational management, it is useful to concentrate on the specifics that motivate the initiative. This means developing a specific vocabulary and set of narratives to facilitate understanding of your organizational business concepts. This webinar provides you with an understanding of what data governance functions are required and how they fit with other data management disciplines. Understanding these aspects is a necessary pre-requisite to eliminate the ambiguity that often surrounds initial discussions and implement effective data governance and stewardship programs that manage data in support of organizational strategy.

Find more of our Data-Ed webinars here: http://www.datablueprint.com/resource-center/webinar-schedule/

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Data-Ed: Data Governance Strategies

  1. 1. Data Governance Strategies • Date: April 14, 2015 • Time: 2:00 PM ET • Presented by: Peter Aiken, PhD • The data governance function exercises authority and control over the management of your mission critical assets and guides how all other data management functions are performed. When selling data governance to organizational management, it is useful to concentrate on the specifics that motivate the initiative. This means developing a specific vocabulary and set of narratives to facilitate understanding of your organizational business concepts. This webinar provides you with an understanding of what data governance functions are required and how they fit with other data management disciplines. Understanding these aspects is a necessary pre-requisite to eliminate the ambiguity that often surrounds initial discussions and implement effective data governance and stewardship programs that manage data in support of organizational strategy. • Learning Objectives – Understanding why data governance can be tricky for most organizations – Steps for improving data governance within your organization – Guiding principles & lessons learned – Understanding foundational data governance concepts based on the DAMA DMBOK 1 Copyright 2014 by Data Blueprint
  2. 2. Shannon Kempe Executive Editor at DATAVERSITY.net 2 Copyright 2015 by Data Blueprint
  3. 3. Get Social With Us! 3Copyright 2015 by Data Blueprint Like Us on Facebook www.facebook.com/ datablueprint Post questions and comments Find industry news, insightful content and event updates. Join the Group Data Management & Business Intelligence Ask questions, gain insights and collaborate with fellow data management professionals Live Twitter Feed Join the conversation! Follow us: @datablueprint @paiken Ask questions and submit your comments: #dataed
  4. 4. Peter Aiken, Ph.D. 4 Copyright 2015 by Data Blueprint • 30+ years in data management • Repeated international recognition • Founder, Data Blueprint (datablueprint.com) • Associate Professor of IS (vcu.edu) • DAMA International (dama.org) • 9 books and dozens of articles • Experienced w/ 500+ data management practices • Multi-year immersions:
 - US DoD
 - Nokia
 - Deutsche Bank
 - Wells Fargo
 - Walmart • DAMA International President 2009-2013 • DAMA International Achievement Award 2001 (with Dr. E. F. "Ted" Codd • DAMA International Community Award 2005 PETER AIKEN WITH JUANITA BILLINGS FOREWORD BY JOHN BOTTEGA MONETIZING DATA MANAGEMENT Unlocking the Value in Your Organization’s Most Important Asset. The Case for the Chief Data Officer Recasting the C-Suite to Leverage Your MostValuable Asset Peter Aiken and Michael Gorman
  5. 5. We believe ... 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 √ √ √ √ 5 Copyright 2015 by Data Blueprint • Today, data is the most powerful, yet underutilized and poorly managed organizational asset • Data is your – Sole – Non-depleteable – Non-degrading – Durable – Strategic • Asset – Data is the new oil! – Data is the new (s)oil! – Data is the new bacon! • Our mission is to unlock business value by – Strengthening your data management capabilities – Providing tailored solutions, and – Building lasting partnerships 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]
  6. 6. Presented By Peter Aiken, Ph.D. Data Governance Strategies “If you don't know where you are going, any road will get you there.” 
 - Lewis Carroll
  7. 7. Motivation Beth Jacobs abruptly 
 resigned in March 7 Copyright 2014 by Data Blueprint
  8. 8. Reported Home Depot data breach could exceed Target hack 8 Copyright 2014 by Data Blueprint
  9. 9. 9 Copyright 2015 by Data Blueprint • Strategy – Term of Recent Usage – Context: Organizational -> IT -> Data – Difficult Choices • Data Governance – What is it? – Why is it important? – Requirements for Effective Data Governance • Data Governance Components – Frameworks – Building Blocks – Checklists – Worst Practices • Data Governance (Storytelling) in Action • Take Aways/References/Q&A Data Governance Strategies Tweeting now: #dataed • Strategy – Term of Recent Usage – Context: Organizational -> IT -> Data – Difficult Choices • Data Governance – What is it? – Why is it important? – Requirements for Effective Data Governance • Data Governance Components – Frameworks – Building Blocks – Checklists – Worst Practices • Data Governance (Storytelling) in Action • Take Aways/References/Q&A
  10. 10. 10 Copyright 2015 by Data Blueprint • Strategy – Term of Recent Usage – Context: Organizational -> IT -> Data – Difficult Choices • Data Governance – What is it? – Why is it important? – Requirements for Effective Data Governance • Data Governance Components – Frameworks – Building Blocks – Checklists – Worst Practices • Data Governance (Storytelling) in Action • Take Aways/References/Q&A Data Governance Strategies Tweeting now: #dataed
  11. 11. What is Strategy? • Current use derived from military • "a pattern in a stream of decisions" [Henry Mintzberg] • "a system of finding, formulating, and developing a doctrine that will ensure long-term success if followed faithfully [Vladimir Kvint] 11 Copyright 2014 by Data Blueprint
  12. 12. Strategy in Action: Napoleon defeats a larger enemy • Question? – How to I defeat the competition when their forces are bigger than mine? • Answer: – Divide 
 and 
 conquer! – “a pattern 
 in a stream 
 of decisions” 12 Copyright 2014 by Data Blueprint – “a pattern 
 in a stream 
 of decisions”
  13. 13. Strategy in Action: Napoleon defeats a larger enemy Copyright 2014 by Data Blueprint 13
  14. 14. Wayne Gretzky’s Strategy He skates to where he 
 thinks the puck will be ... 14 Copyright 2014 by Data Blueprint
  15. 15. Data Strategy in Context • Organizational Strategy • IT Strategy • Data 
 Governance 
 Strategy 15 Copyright 2014 by Data Blueprint
  16. 16. 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. 16 Copyright 2014 by Data Blueprint
  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 17 Copyright 2014 by Data Blueprint
  18. 18. No clear connection exists between to business priorities and IT initiatives 18 Copyright 2014 by Data Blueprint Grow expenses slower than sales Grow operating income faster than sales Pass on savings Drive efficiency with technology Leverage scale globally Leverage expertise Deploy new formats Grow productivity of existing assets Attract new members Expand into new channels Enter new markets Make acquisitions Produce significant free cash flow Drive ROI performance Deliver greater shareholder value Customer Perspectiv e Open new stores Develop new, innovative formats Appeal to new demographics Integrate shopping experience Develop new, innovative formats Remain relevant to all customers Increase "Green" Image Internal Perspectiv e Create competitive advantages Improve use of information Strengthen supply chain Improve Associate productivity Making acquisitions Increase benefit from our global expertise Present consistent view and experience Integrate channels Match staffing to store needs Increase sell through Financial Perspectiv e Reduce expenses Inventory Management Human and Intell. Capital investment Manage new facilities Improve Sales and margin by facilities Increased member-base revenues Revenue growth Cash flow Return on Capital Walmart Strategy Map See more uniform brand and retail experience Leverage Growth Return Gross Margin Improvement CEOPerspective Attract more customers & have customer purchasing more Associate Productivity Customer Insights Human Capital Corp. Reputation Acquisition Strategic Planning Real estate CRM CRM Analytic and reporting processes Corporate Reputation - Risk Management, Compliance, Marketing, IT and Data Governance Corporate Processes Corporate Data Inventory Mgmt TransformationPortfolio Supply Chain Multi ChannelMerchant ToolsSupply Chain Strategic Initiatives AcctingSales Transactional Processing Logistics AssociateLocations and Codes Item CustomerSuppliers Retail Planning ( Alignment Gap ) Adapted from John Ladley
  19. 19. Strategy is Difficult to Perceive at the IT Project Level • If they exist ... • A singular organizational strategy and set of goals/objectives ... • Are not perceived as such at the project level and ... • What does exist is confused, inaccurate, and incomplete • IT projects do not well reflect organizational strategy 
 Organizational
 Strategy Set of 
 Organizational 
 Goals/Objectives Organizational IT
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 19 Copyright 2014 by Data Blueprint Division/Group/Project
  20. 20. 
 Q1
 Keeping the doors open
 (little or no proactive 
 data management) Q2
 Increasing organizational efficiencies/effectiveness Q3
 Using data to create 
 strategic opportunities
 Q4
 Both Improve Operations Innovation Only 1 is 10 organizations has a board approved data strategy! Data Governance Strategy Choices 20 Copyright 2014 by Data Blueprint
  21. 21. Supplemental: CMMI Data Strategy Elements The data management strategy defines the overall framework of the program. A data management strategy typically includes: • A vision statement, which includes core operating principles; goals and objectives; priorities, based on a synthesis of factors important to the organization, such as business value, degree of support for strategic initiatives, level of effort, and dependencies • Program scope – including both key business areas (e.g. Customer Accounts); data management priorities (e.g. Data Quality); and key data sets (e.g. Customer Master Data) • Business benefits – The selected data management framework and how it will be used – High-level roles and responsibilities – Governance needs – Description of the approach used to develop the data management program – Compliance approach and measures – High-level sequence plan (roadmap). 21 Copyright 2014 by Data Blueprint
  22. 22. 22 Copyright 2015 by Data Blueprint • Strategy – Term of Recent Usage – Context: Organizational -> IT -> Data – Difficult Choices • Data Governance – What is it? – Why is it important? – Requirements for Effective Data Governance • Data Governance Components – Frameworks – Building Blocks – Checklists – Worst Practices • Data Governance (Storytelling) in Action • Take Aways/References/Q&A Data Governance Strategies Tweeting now: #dataed
  23. 23. 23 Copyright 2015 by Data Blueprint • Strategy – Term of Recent Usage – Context: Organizational -> IT -> Data – Difficult Choices • Data Governance – What is it? – Why is it important? – Requirements for Effective Data Governance • Data Governance Components – Frameworks – Building Blocks – Checklists – Worst Practices • Data Governance (Storytelling) in Action • Take Aways/References/Q&A Data Governance Strategies Tweeting now: #dataed
  24. 24. 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 24 Copyright 2014 by Data Blueprint
  25. 25. DAMA DM BoK & CDMP • Published by DAMA International – The professional association for Data Managers (40 chapters worldwide) – DMBoK organized around – Primary data management functions focused around data delivery to the organization (more at dama.org) – Organized around several environmental elements • CDMP – Certified Data Management Professional – DAMA International and ICCP – Membership in a distinct group made up of your fellow professionals – Recognition for your specialized knowledge in a choice of 17 specialty areas – Series of 3 exams – For more information, please visit: • http://www.dama.org/i4a/pages/index.cfm?pageid=3399 • http://iccp.org/certification/designations/cdmp 25 Copyright 2014 by Data Blueprint Data Management Functions
  26. 26. 5 Requirements for Effective DG Data governance is a set of well-defined policies and practices designed to ensure that data is: 1. Accessible – Can the people who need it access the data they need? – Does the data match the format the user requires? 2. Secure – Are authorized people the only ones who can access the data? – Are non-authorized users prevented from accessing it? 3. Consistent – When two users seek the "same" piece of data, is it actually the same data? – Have multiple versions been rationalized? 4. High Quality – Is the data accurate? – Has it been conformed to meet agreed standards 5. Auditable – Where did the data come from? – Is the lineage clear? – Does IT know who is using it and for what purpose? 26 Copyright 2014 by Data Blueprint Source: “5 Steps to Effective Data Governance” by Angela Guess; http://www.dataversity.net/archives/5160 • Integrity • Accountability • Transparency • Strategic alignment • Standardization • Organizational change management • Data architecture • Stewardship/Quality • Protection
  27. 27. 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 27 Copyright 2014 by Data Blueprint
  28. 28. • Getting access to data around here is like that Catherine Zeta Jones scene where she is having to get thru all those lasers … Use Their Language ... 28 Copyright 2014 by Data Blueprint
  29. 29. Practice Articulating How DG Solves Problems 29 Copyright 2014 by Data Blueprint Decision Making Needs Data Quality/Inventory Management Organizational Strategy Formulation/Implementation Operational Data Delivery Performance Data Security Planning/Implementation
  30. 30. 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 30 Copyright 2014 by Data Blueprint
  31. 31. DMM℠ Structure 31 Copyright 2014 by Data Blueprint
  32. 32. One concept for process improvement, others include: • Norton Stage Theory • TQM • TQdM • TDQM • ISO 9000
 and focus on understanding current processes and determining where to make improvements. DMM℠ Capability Maturity Model Levels Our DM practices are informal and ad hoc, dependent upon "heroes" and heroic efforts Performed (1) Managed (2) Our DM practices are defined and documented processes performed at the business unit level Our DM efforts remain aligned with business strategy using standardized and consistently implemented practices Defined (3) Measured (4) We manage our data as a asset using advantageous data governance practices/structures 
 Optimized (5)
 DM is strategic organizational capability, most importantly we have a process for improving our DM capabilities 32 Copyright 2014 by Data Blueprint
  33. 33. 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 33 Copyright 2014 by Data Blueprint
  34. 34. Industry Focused Results 34 Copyright 2014 by Data Blueprint Data Management Strategy Data Governance Platform & Architecture Data Quality Data Operations Optimized(V)
 Measured(IV)
 Defined(III)
 Managed(II)
 Initial(I) • 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"
  35. 35. Data Management Strategy Data Governance Data Platform & Architecture Data Quality Data Operations 0 1 2 3 4 5 Client Industry Competition All Respondents Comparative Assessment Results Challenge Challenge Challenge 35 Copyright 2014 by Data Blueprint
  36. 36. 1 2 3 4 5 DataProgramCoordination OrganizationalDataIntegration DataStewardship DataDevelopment DataSupportOperations 2007 Maturity Levels 2012 Maturity Levels Comparison of DM Maturity 2007-2012 36 Copyright 2014 by Data Blueprint
  37. 37. 2012 London Summer Games • 60 GB of data/second • 200,000 hours of big data will be generated testing systems • 2,000 hours media coverage/daily • 845 million Facebook users averaging 15 TB/ day • 13,000 tweets/second • 4 billion watching • 8.5 billion devices connected 37 Copyright 2014 by Data Blueprint
  38. 38. Supplemental: Data Governance 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. 38 Copyright 2014 by Data Blueprint from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
  39. 39. Supplemental: Data Governance Activities • Understand Strategic 
 Enterprise Data Needs • Develop and Maintain 
 the Data Strategy • Establish Data Professional 
 Roles and Organizations • Identify and Appoint 
 Data Stewards • Establish Data Governance and Stewardship Organizations • Develop and Approve Data Policies, Standards, and Procedures • Review and Approve Data Architecture • Plan and Sponsor Data Management Projects and Services • Estimate Data Asset Value and Associated Costs 39 Copyright 2014 by Data Blueprint from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
  40. 40. Supplemental: Data Governance Primary Deliverables • Data Policies • Data Standards • Resolved Issues • Data Management Projects and Services • Quality Data and Information • Recognized Data Value 40 Copyright 2014 by Data Blueprint from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
  41. 41. Supplemental: Data Governance Roles and Responsibilities • Participants: – Executive Data Stewards – Coordinating Data Stewards – Business Data Stewards – Data Professionals – DM Executive – CIO • Suppliers: – Business Executives – IT Executives – Data Stewards – Regulatory Bodies • Consumers: – Data Producers – Knowledge Workers – Managers and Executives – Data Professionals – Customers 41 Copyright 2014 by Data Blueprint from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
  42. 42. Supplemental: Data Governance Technologies • Intranet Website • E-Mail • Metadata Tools • Metadata Repository • Issue Management Tools • Data Governance KPI Dashboard 42 Copyright 2014 by Data Blueprint from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
  43. 43. Supplemental: Data Governance Practices and Techniques • Data Value • Data Management 
 Cost • Achievement of 
 Objectives • # of Decisions Made • Steward Representation/Coverage • Data Professional Headcount • Data Management Process Maturity 43 Copyright 2014 by Data Blueprint from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
  44. 44. Why is Data Governance Important? Cost organizations millions each year in • Productivity • Redundant and siloed efforts • Poorly thought out hardware 
 and software purchases • Reactive instead of 
 proactive initiatives • Delayed decision making 
 using inadequate information • 20-40% of IT spending can 
 be reduced through better 
 data governance 44 Copyright 2014 by Data Blueprint
  45. 45. Largely Ineffective Investments • Approximately, 10% percent of organizations achieve parity and (potential positive returns) on their investments • Only 30% of investments achieve tangible returns at all • Seventy percent of organizations have very small or no tangible return on their investments 45 Copyright 2014 by Data Blueprint
  46. 46. Application-Centric Development Original articulation from Doug Bagley @ Walmart • In support of strategy, organizations develop specific goals/objectives • The goals/objectives drive the development of specific systems/applications • Development of systems/applications leads to network/infrastructure requirements • Data/information are typically considered after the systems/applications and network/ infrastructure have been articulated • 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/ Information Network/ Infrastructure Systems/ Applications Goals/ Objectives Strategy 46 Copyright 2014 by Data Blueprint
  47. 47. What does it mean to treat data as an organizational asset? • An asset is a resource controlled by the organization as a result of past events or transactions and from which future economic benefits are expected to flow to 
 the organization [Wikipedia] • Assets are economic resources – Must own or control – Must use to produce value – Value can be converted into cash • As assets: – Formalize the care and feeding of data – Put data to work in unique and significant ways 47 Copyright 2014 by Data Blueprint
  48. 48. Evolving Data is Different than Creating New Systems 48 Copyright 2014 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!
  49. 49. Data-Centric Development Original articulation from Doug Bagley @ Walmart • In support of strategy, the organization develops specific goals/objectives • The goals/objectives drive the development of specific data/information assets with an eye to organization-wide usage • Network/infrastructure components are developed to support organization-wide use of data • Development of systems/applications is derived from the data/network architecture • 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 Systems/ Applications Network/ Infrastructure Data/ Information Goals/ Objectives Strategy 49 Copyright 2014 by Data Blueprint
  50. 50. The special nature of DCD • An architectural focus • Practice extension • Personality/organizational challenges 
 unrecognized • Technical engineering requires different skills • Extra attention required to communication • Scarcity of 
 professionals • Need for a 
 specialist 
 discipline 50 Copyright 2014 by Data Blueprint PETER AIKEN WITH JUANITA BILLINGS FOREWORD BY JOHN BOTTEGA MONETIZING DATA MANAGEMENT Unlocking the Value in Your Organization’s Most Important Asset. When our organizations transform to a data-centric approach, we begin to measure success differently than we did before—same project, same process, but with different measures that include: • asking if our data is correct; • valuing data more than valuing "on time and within budget;" • valuing correct data more than correct process; and • auditing data rather than project documents. - Linda Bevolo
  51. 51. 51 Copyright 2015 by Data Blueprint • Strategy – Term of Recent Usage – Context: Organizational -> IT -> Data – Difficult Choices • Data Governance – What is it? – Why is it important? – Requirements for Effective Data Governance • Data Governance Components – Frameworks – Building Blocks – Checklists – Worst Practices • Data Governance (Storytelling) in Action • Take Aways/References/Q&A Data Governance Strategies Tweeting now: #dataed
  52. 52. 52 Copyright 2015 by Data Blueprint • Strategy – Term of Recent Usage – Context: Organizational -> IT -> Data – Difficult Choices • Data Governance – What is it? – Why is it important? – Requirements for Effective Data Governance • Data Governance Components – Frameworks – Building Blocks – Checklists – Worst Practices • Data Governance (Storytelling) in Action • Take Aways/References/Q&A Data Governance Strategies Tweeting now: #dataed
  53. 53. Getting Started 53 Copyright 2014 by Data Blueprint Assess context Define DG roadmap Secure executive mandate Assign Data Stewards Execute plan Evaluate results Revise plan Apply change management (Occurs once) (Repeats)
  54. 54. 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 54 Copyright 2014 by Data Blueprint
  55. 55. Data Governance from the DMBOK 55 Copyright 2014 by Data Blueprint from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
  56. 56. 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 56 Copyright 2014 by Data Blueprint http://www.datagovernance.com/
  57. 57. 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 57 Copyright 2014 by Data Blueprint http://www.kikconsulting.com/
  58. 58. 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 58 Copyright 2014 by Data Blueprint http://www-01.ibm.com/software/data/system-z/data-governance/workshops.html
  59. 59. Elements of Effective Data Governance 59 Copyright 2014 by Data Blueprint See IBM Data Governance Council, http://www-01.ibm.com/software/tivoli/ governance/servicemanagement/ data-governance.html.
  60. 60. Baseline Consulting (sas.com) 60 Copyright 2014 by Data Blueprint
  61. 61. American College Personnel Association 61 Copyright 2014 by Data Blueprint
  62. 62. Supplemental: NASCIO DG Implementation Process 62 Copyright 2014 by Data Blueprint
  63. 63. Supplemental: 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 63 Copyright 2014 by Data Blueprint Source: “Data Governance Checklist for Educators” by Angela Guess; http://www.dataversity.net/archives/5198
  64. 64. Supplemental: Data Governance Checklist • The Privacy Technical Assistance Center has published a new checklist “to assist stakeholder organizations, such as state and local education agencies, with establishing and maintaining a successful data governance program to help ensure the individual privacy and confidentiality of education records.” • The five page paper offers a number of suggestions for implementing a successful data governance program that can be applied to a variety of business models beyond education. • For more information, please visit the Privacy Technical Assistance Center: http://ed.gov/ptac 64 Copyright 2014 by Data Blueprint Source: “Data Governance Checklist for Educators” by Angela Guess; http://www.dataversity.net/archives/5198
  65. 65. Supplemental: NASCIO Scorecard 65 Copyright 2014 by Data Blueprint
  66. 66. Supplemental: 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” 66 Copyright 2014 by Data Blueprint
  67. 67. 67 Copyright 2015 by Data Blueprint • Strategy – Term of Recent Usage – Context: Organizational -> IT -> Data – Difficult Choices • Data Governance – What is it? – Why is it important? – Requirements for Effective Data Governance • Data Governance Components – Frameworks – Building Blocks – Checklists – Worst Practices • Data Governance (Storytelling) in Action • Take Aways/References/Q&A Data Governance Strategies Tweeting now: #dataed
  68. 68. 68 Copyright 2015 by Data Blueprint • Strategy – Term of Recent Usage – Context: Organizational -> IT -> Data – Difficult Choices • Data Governance – What is it? – Why is it important? – Requirements for Effective Data Governance • Data Governance Components – Frameworks – Building Blocks – Checklists – Worst Practices • Data Governance (Storytelling) in Action • Take Aways/References/Q&A Data Governance Strategies Tweeting now: #dataed
  69. 69. Simon Sinek: 
 How great leaders inspire action 69 Copyright 2014 by Data Blueprint http://www.ted.com/talks/simon_sinek_how_great_leaders_inspire_action.html 
 
 
 
 
 
 
 
 
 What 
 
 
 
 
 
 How Why
  70. 70. Attaching Stuff to the Engine • Detroit – 10 different bolts – 10 different wrenches – 10 different bolt inventories • Toyota – Same bolts used for all assemblies – 1 bolt inventory – 1 type of wrench 70 Copyright 2014 by Data Blueprint
  71. 71. 71 Copyright 2014 by Data Blueprint
  72. 72. healthcare.gov • 55 Contractors! • 6 weeks from launch and requirements not finalized • "Anyone who has written a line of code or built a system from the ground-up cannot be surprised or even mildly concerned that Healthcare.gov did not work out of the gate," 
 
 Standish Group International Chairman Jim Johnson said in a recent podcast. 
 • "The real news would have been if it actually did work. The very fact that most of it did work at all is a success in itself." 72 Copyright 2014 by Data Blueprint • "It was pretty obvious from the first look that the system hadn't been designed to work right," says Marty Abbott. "Any single thing that slowed down would slow everything down." • Software programmed to 
 access data using 
 traditional technologies • Data components incorporated 
 "big data technologies"
 http://www.slate.com/articles/technology/bitwise/2013/10/ problems_with_healthcare_gov_cronyism_bad_management_and_too_ many_cooks.html
  73. 73. Formalizing the Role of U.S. Army IT Governance/ Compliance 73 Copyright 2014 by Data Blueprint
  74. 74. Suicide Mitigation 74 Copyright 2014 by Data Blueprint
  75. 75. Data Mapping 12 Mental illness Deploy ments Work History Soldier Legal Issues Abuse Suicide Analysis FAPDMSS G1 DMDC CID Data objects complete? All sources identified? Best source for each object? How reconcile differences between sources? MDR 75 Copyright 2014 by Data Blueprint
  76. 76. Senior Army Official • A very heavy dose of 
 management support • Any questions as to future 
 data ownership, "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 76 Copyright 2014 by Data Blueprint
  77. 77. Communication Patterns 77 Copyright 2014 by Data Blueprint 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
  78. 78. Vocabulary is Important-Tank, Tanks, Tankers, Tanked 78 Copyright 2014 by Data Blueprint
  79. 79. How one inventory item proliferates data throughout the chain 79 Copyright 2014 by Data Blueprint 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
  80. 80. 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 80 Copyright 2014 by Data Blueprint
  81. 81. Spreadsheet Interpretation 81 Copyright 2014 by Data Blueprint
  82. 82. 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. 82 Copyright 2014 by Data Blueprint
  83. 83. Example of Poor Data Governance Mizuho Securities Example • 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 83 Copyright 2014 by Data Blueprint 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).
  84. 84. 84 Copyright 2014 by Data Blueprint
  85. 85. Seven Sisters (from British Telecom) http://www.datablueprint.com/thought-leaders/peter-aiken/book-monetizing-data-management/ [Thanks to Dave Evans] Copyright 2013 by Data Blueprint 85
  86. 86. 86 Copyright 2015 by Data Blueprint • Strategy – Term of Recent Usage – Context: Organizational -> IT -> Data – Difficult Choices • Data Governance – What is it? – Why is it important? – Requirements for Effective Data Governance • Data Governance Components – Frameworks – Building Blocks – Checklists – Worst Practices • Data Governance (Storytelling) in Action • Take Aways/References/Q&A Data Governance Strategies Tweeting now: #dataed
  87. 87. 87 Copyright 2015 by Data Blueprint • Strategy – Term of Recent Usage – Context: Organizational -> IT -> Data – Difficult Choices • Data Governance – What is it? – Why is it important? – Requirements for Effective Data Governance • Data Governance Components – Frameworks – Building Blocks – Checklists – Worst Practices • Data Governance (Storytelling) in Action • Take Aways/References/Q&A Data Governance Strategies Tweeting now: #dataed
  88. 88. Maslow's Hierarchy of Needs 88 Copyright 2014 by Data Blueprint
  89. 89. 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) Data Management Practices Hierarchy Advanced 
 Data 
 Practices • MDM • Mining • Big Data • Analytics • Warehousing • SOA Foundational Data Management Practices 89 Copy right 2015by Data Blueprint Data Platform/Architecture Data Governance Data Quality Data Operations Data Management Strategy Technologies Capabilities
  90. 90. 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 • Comparing DG frameworks can be useful • DG directs data management efforts • The language of DG is metadata • Process improvement can improve DG practices 90 Copyright 2014 by Data Blueprint
  91. 91. The File Naming Convention Committee's Output 91 Copyright 2014 by Data Blueprint
  92. 92. Data Governance Council Hotel 92 Copyright 2014 by Data Blueprint
  93. 93. 93 Copyright 2014 by Data Blueprint PETER AIKEN WITH JUANITA BILLINGS FOREWORD BY JOHN BOTTEGA MONETIZING DATA MANAGEMENT Unlocking the Value in Your Organization’s Most Important Asset.
  94. 94. Supplemental: Data Governance Checklist • Decision-Making Authority – Assign appropriate levels of authority to data stewards – Proactively define scope and limitations of that authority • Standard Policies and Procedures – Adopt and enforce clear policies and procedures in a written data stewardship plan to ensure that everyone understands the importance of data quality and security – Helps to motivate and empower staff to implement DG • Data Inventories – Conduct inventory of all data that require protection – Maintain up-to-date inventory of all sensitive records and data systems – Classify data by sensitivity to identify focus areas for security efforts • Data Content Management – Closely manage data content to justify the collection of sensitive data, optimize data management processes and ensure compliance with federal, state, and local regulations 94 Copyright 2014 by Data Blueprint Source: “Data Governance Checklist for Educators” by Angela Guess; http://www.dataversity.net/archives/5198
  95. 95. Supplemental: Data Governance Checklist, cont’d • Data Records Management – Specify appropriate managerial and user activities related to handling data to provide data stewards and users with appropriate tools for complying with an organization’s security policies • Data Quality – Ensure that data are accurate, relevant, timely, and complete for their intended purposes – Key to maintaining high quality data is a proactive approach to DG that requires establishing and regularly updating strategies for preventing, detecting, and correcting errors and misuses of data • Data Access – Define and assign differentiated levels of data access to individuals based on their roles and responsibilities – This is critical to prevent unauthorized access and minimize risk of data breaches • Data Security and Risk Management – Ensure the security of sensitive and personally identifiable data and mitigate the risks of unauthorized disclosure of these data – Top priority for effective data governance plan 95 Copyright 2014 by Data Blueprint Source: “Data Governance Checklist for Educators” by Angela Guess; http://www.dataversity.net/archives/5198
  96. 96. Supplemental: 10 DG Worst Practices in Detail 1. Buy-in but not Committing: 
 Business vs. IT – Business needs to do more – Data governance tasks need 
 to recognized as priority – Without a real business-resource commitment, data governance takes a backseat and will never be implemented effectively 2. Ready, Fire, Aim – Good: Create governance steering committee 
 (business representatives from across enterprise) 
 and separate governance working group (data stewards) – Problem: Often get the timing wrong: Panels are formed and people are assigned BEFORE they really understand the scope of the data governance and participants’ roles and responsibilities – Prematurely organize management framework and realize you need a do-over = Guaranteed way to stall DG initiative 96 Copyright 2014 by Data Blueprint Source: “Data Governance Worst Practices” by Angela Guess; http://www.dataversity.net/archives/4895
  97. 97. Supplemental: 10 DG Worst Practices in Detail 3. Trying to Solve World Hunger or Boil the Ocean • Trap 1: Trying to solve all organizational data 
 problems in initial project phase • Trap 2: Starting with biggest data problems (highly political issues) • Almost impossible to establish a DG program while tacking data problems that have taken years to build up • Instead: “Think globally and act locally”: break data problems down into incremental deliverables • “Too big too fast” = Recipe for disaster 4. The Goldilocks Syndrome • Encountering things that are either one 
 extreme or another • Either the program is too high-level and 
 substantive issues are never dealt with or it 
 attempts to create definitions and rules for every field and table • Need to find happy compromise that enables DG initiatives to create real business value 97 Copyright 2014 by Data Blueprint Source: “Data Governance Worst Practices” by Angela Guess; http://www.dataversity.net/archives/4895 Source: “Data Governance Worst Practices” by Angela Guess; http://www.dataversity.net/archives/4895
  98. 98. Supplemental: 10 DG Worst Practices in Detail 5. Committee Overload • Good: People of various business units and 
 departments get involved in the governance process • Bad: more people -> more politics -> more watered down governance responsibilities • To be successful, limit committee sizes to 6-12 people and ensure that members have decision-making authority 6. Failure to Implement • DG efforts won’t produce any business value if 
 data definitions, business rules and KPIs are 
 created but not used in any processes • Governance process needs to be a complete feedback loop in which data is defined, monitored, acted upon, and changed when appropriate • Also important: Establish ongoing communication about governance to prevent business users going back to old habits 98 Copyright 2014 by Data Blueprint Source: “Data Governance Worst Practices” by Angela Guess; http://www.dataversity.net/archives/4895Source: “Data Governance Worst Practices” by Angela Guess; http://www.dataversity.net/archives/4895
  99. 99. Supplemental: 10 DG Worst Practices in Detail 7.Not Dealing with Change Management • Business and IT processes need to be 
 changed for enterprise DG to be successful • Need for change management is seldom addressed • Challenges: people/process issues and internal politics 8.Assuming that Technology Alone is the Answer • Purchasing MDM, data integration or data quality software to support DG programs is not the solution • Combination of vendor hype and high 
 price tags set high expectations • Internal interactions are what make 
 or break data governance efforts 99 Copyright 2014 by Data Blueprint Source: “Data Governance Worst Practices” by Angela Guess; http://www.dataversity.net/archives/4895
  100. 100. Supplemental: 10 DG Worst Practices in Detail 9.Not Building Sustainable and Ongoing 
 Processes • Initial investment in time, money 
 and people may be accurate • Many organizations don’t establish a budget, resource commitments or design DG processes with an eye toward sustaining the governance effort for the long term 10.Ignoring “Data Shadow Systems” • Common mistake: focus on “systems 
 of record” and BI systems, assuming 
 that all important data can be found there • Often, key information is located in “data shadow systems” scattered through organization • Don’t ignore such additional deposits of information 100 Copyright 2014 by Data Blueprint Source: “Data Governance Worst Practices” by Angela Guess; http://www.dataversity.net/archives/4895
  101. 101. References Websites • Data Governance Book Data Governance Book Compliance Book 101 Copyright 2014 by Data Blueprint
  102. 102. IT Governance Books 102 Copyright 2014 by Data Blueprint
  103. 103. Interdependencies 103 Data Governance Master DataData Quality makes the case and is responsible for is a necessary but insufficient prerequisite to success MD capabilities constrain governance effectiveness
  104. 104. Upcoming Events May Webinar: Monetizing Data Management May 12, 2015 @ 2:00 PM ET June Webinar: Go Small before going Big (Data) Subtitle: A Framework for Implementing NoSQL, Hadoop June 9, 2015 @ 2:00 PM ET Sign up here: • www.datablueprint.com/webinar-schedule • www.Dataversity.net 104 Copyright 2015 by Data Blueprint
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