Data-Ed: Unlock Business Value through Data Governance

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If your organization understands your function, they see you as an investment. If your organization does not understand what you do, they are likely to perceive you as a cost. The goal of this webinar is to provide you with concrete ideas for how to reinforce the first mindset at your organization. Success stories must be used to ensure continued organizational support. 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. For example: using specific common terms (and narratives) when referencing organizational mishaps, e.g. The Chocolate Story.

Learning Objectives:

Understanding contextually why data governance can be tricky for most organizations
Demonstrate a variety of “storytelling” techniques
How to use “worst practices” to your advantage
Understanding foundational data governance concepts based on the Data Management Body of Knowledge (DMBOK)
Taking away several novel but tangible examples of generating business value through data governance

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Data-Ed: Unlock Business Value through Data Governance

  1. 1. Unlock Business Value through Data Governance• If your organization understands your function, they see you as an investment. If your organization does not understand what you do, they are likely to perceive you as a cost. The goal of this webinar is to provide you with concrete ideas for how to reinforce the first mindset at your organization. Success stories must be used to ensure continued organizational support. 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. For example: using specific common terms (and narratives) when referencing organizational mishaps, e.g. The Chocolate Story. 1 Copyright 2013 by Data Blueprint
  2. 2. Unlock Business Value through Data GovernanceIf your organization understands your function, theysee you as an investment. If your organizationdoes not understand what you do, they are likely toperceive you as a cost. The goal of this webinar isto provide you with concrete ideas for how toreinforce the first mindset at your organization.Success stories must be used to ensure continuedorganizational support. When selling datagovernance to organizational management, it isuseful to concentrate on the specifics that motivatethe initiative. This means developing a specificvocabulary and set of narratives to facilitateunderstanding of your organizational businessconcepts. For example: using specific commonterms (and narratives) when referencingorganizational mishaps, e.g. The Chocolate Story.Date: April 9, 2013Time: 2:00 PM ETPresented by: Peter Aiken, PhD 2 Copyright 2013 by Data Blueprint
  3. 3. Commonly Asked Questions1) Will I get copies of the slides after the event?2) Is this being recorded so I can view it afterwards? 3 Copyright 2013 by Data Blueprint
  4. 4. Get Social With Us! Live Twitter Feed Like Us on Facebook Join the Group Join the conversation! www.facebook.com/ Data Management & Follow us: datablueprint Business Intelligence @datablueprint Post questions and Ask questions, gain insights comments and collaborate with fellow @paiken Find industry news, insightful data managementAsk questions and submit content professionalsyour comments: #dataed and event updates. 4 Copyright 2013 by Data Blueprint
  5. 5. Unlock Business Value throughData Governance
  6. 6. Meet Your Presenter: Peter Aiken, Ph.D. • Internationally recognized thought- leader in the data management field - 30 years of experience – Recipient of multiple international awards – Founder, Data Blueprint – 7 books and dozens of articles • Experienced w/ 500+ data management practices in 20 countries • Multi-year immersions with organizations as diverse as the US DoD, Deutsche Bank, Nokia, Wells Fargo, and the Commonwealth of Virginia 6 Copyright 2013 by Data Blueprint
  7. 7. Motivation • #nowthatcherisdead • #now thatcher is dead • #now that cher is dead • #now t hatcher is dead 7 Copyright 2013 by Data Blueprint
  8. 8. 8Copyright 2013 by Data Blueprint
  9. 9. Unlock Business Value through Data Governance•• Context: What is Data Management/ Context: What Management/ DAMA/DM BoK/CDMP? DAMA/DM BoK/CDMP?• What is Data Governance and why • What is Data Governance and why is it Important? is it Important? – – Organizational -> IT -> Data Organiza*onal  -­‐>  IT  -­‐>  Data – Requirements for Effective Data – Requirements  for  Effec*ve  Data  Governance Governance• Data Governance• Data Governance – – Frameworks Frameworks – – Checklists   Checklists – – Worst  Prac*ces Worst Practices – – Building  Blocks Building Blocks• Data Governance in Action:• Data Governance in Action: Tweeting now: – Securi*es  eexample – Securities xample #dataed – Retail  eexample – Retail xample• Take Aways/References/Q&A• Take Aways/References/Q&A 9 Copyright 2013 by Data Blueprint
  10. 10. Unlock Business Value through Data Governance• Context: What is Data Management/ DAMA/DM BoK/CDMP?• What is Data Governance and why is it Important? – Organizational -> IT -> Data – Requirements for Effective Data Governance• Data Governance – Frameworks – Checklists – Worst Practices – Building Blocks• Data Governance in Action: Tweeting now: – Securities example #dataed – Retail example• Take Aways/References/Q&A 10 Copyright 2013 by Data Blueprint
  11. 11. Data Management is an Integrated System of Five Practice Areas #dataed 11 Copyright 2013 by Data Blueprint
  12. 12. Five Integrated DM Practices Manage data coherently. Data Program Coordination Share data across boundaries. Organizational Data Integration Data Stewardship Data DevelopmentAssign responsibilities for data. Engineer data delivery systems. Data Support Operations Maintain data availability. #dataed 12 Copyright 2013 by Data Blueprint
  13. 13. Data Management Practices Hierarchy (after Maslow) • 5 Data Management Practices Areas / Data Management Basics• Are necessary but insufficient Advanced prerequisites to Data organizational data Practices leveraging • Cloud • MDM applications • Mining (that is Self Actualizing • Analytics Data or Advanced Data • Warehousing Practices) • Big Basic Data Management Practices – Data Program Management – Organizational Data Integration – Data Stewardship – Data Development – Data Support Operations http://3.bp.blogspot.com/-ptl-9mAieuQ/T-idBt1YFmI/AAAAAAAABgw/Ib-nVkMmMEQ/s1600/maslows_hierarchy_of_needs.pngby Data Blueprint Copyright 2013
  14. 14. Data  Management  Func-ons   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 #dataed 14 Copyright 2013 by Data Blueprint
  15. 15. Unlock Business Value through Data Governance• Context: What is Data Management/ DAMA/DM BoK/CDMP?• What is Data Governance and why is it Important? – Organizational -> IT -> Data – Requirements for Effective Data Governance• Data Governance – Frameworks – Checklists – Worst Practices – Building Blocks• Data Governance in Action: Tweeting now: – Securities example #dataed – Retail example• Take Aways/References/Q&A 15 Copyright 2013 by Data Blueprint
  16. 16. Unlock Business Value through Data Governance• Context: What is Data Management/ DAMA/DM BoK/CDMP?• What is Data Governance and why is it Important? – Organizational -> IT -> Data – Requirements for Effective Data Governance• Data Governance – Frameworks – Checklists – Worst Practices – Building Blocks• Data Governance in Action: Tweeting now: – Securities example #dataed – Retail example• Take Aways/References/Q&A 16 Copyright 2013 by Data Blueprint
  17. 17. Data Strategy in Context Organiza)onal IT  Strategy Data  Strategy Only  1  is  10  organiza/ons  has  a  board  approved  data   strategy! 17 Copyright 2013 by Data Blueprint
  18. 18. 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. 18 Copyright 2013 by Data Blueprint
  19. 19. 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)According to the IT Governance Institute, there are five areas of focus:• Strategic Alignment• Value Delivery• Resource Management• Risk Management• Performance Measures 19 Copyright 2013 by Data Blueprint
  20. 20. No clear connection exists between to business priorities and IT initiatives Walmart Strategy Map CEO Perspective Leverage Growth Return Grow expenses Grow operating Grow Produce Deliver greater Pass on Drive efficiency Leverage scale Leverage Deploy new Attract new Expand into Enter new Make Drive ROI slower than income faster productivity of significant free shareholder savings with technology globally expertise formats members new channels markets acquisitions performance sales than sales existing assets cash flow value Perspectiv Customer Develop new, Integrate Develop new, Remain See more uniform brand and retail Open new Appeal to new Increase Attract more customers & have customer purchasing more innovative shopping innovative relevant to all e experience stores demographics "Green" Image formats experience formats customers Perspectiv Increase Present Internal Create Improve Improve use of Strengthen Making benefit from consistent Integrate Match staffing Increase sell competitive Associate e information supply chain acquisitions our global view and channels to store needs through advantages productivity expertise experience Perspectiv Improve Financial Human and Increased Reduce Inventory Manage new Sales and Revenue Return on Gross Margin Improvement Intell. Capital member-base Cash flow e expenses Management facilities margin by growth Capital investment revenues facilities ( Alignment Gap ) Strategic Initiatives Associate Customer Supply Chain Merchant Tools Multi Channel Productivity Insights Transformation Portfolio Corporate Processes Supply Chain Human Capital Corp. Reputation Acquisition Strategic Planning Inventory Mgmt Real estate CRM Sales CRM Accting Transactional Processing Retail Planning Analytic and reporting processes Corporate Reputation - Risk Management, Compliance, Marketing, IT and Data Governance Corporate Data Logistics Locations and Codes Associate Item Suppliers Customer Adapted  from  John  Ladley 20 Copyright 2013 by Data Blueprint
  21. 21. 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 21 Copyright 2013 by Data Blueprint
  22. 22. Organizational Data Governance Purpose Statement• What does data governance mean to my organization? – 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 22 Copyright 2013 by Data Blueprint
  23. 23. Data Governance from the DMBOK 23 Copyright 2013 by Data Blueprint
  24. 24. Data Governance from the DMBOK Organizational Strategy Formulation/Implementation Data Security Planning/Implementation Operational Data Delivery Performance Data Quality/Inventory Management Decision Making Needs 24 Copyright 2013 by Data Blueprint
  25. 25. 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 25 Copyright 2013 by Data Blueprint
  26. 26. 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 26 Copyright 2013 by Data Blueprint
  27. 27. 5 Requirements for Effective DGData governance is a set of well-defined policies andpractices designed to ensure that data is: • Integrity • Accountability1. Accessible • Transparency – Can the people who need it access the data they need? • Strategic alignment – Does the data match the format the user requires? • Standardization2. Secure • Organizational change management – Are authorized people the only ones who can access the data? • Data architecture – Are non-authorized users prevented from accessing it? • Stewardship/Quality3. Consistent • Protection – 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 standards5. Auditable – Where did the data come from? – Is the lineage clear? – Does IT know who is using it and for what purpose? Source: “5 Steps to Effective Data Governance” by Angela Guess; http://www.dataversity.net/archives/5160 27 Copyright 2013 by Data Blueprint
  28. 28. Unlock Business Value through Data Governance• Context: What is Data Management/ DAMA/DM BoK/CDMP?• What is Data Governance and why is it Important? – Organizational -> IT -> Data – Requirements for Effective Data Governance• Data Governance – Frameworks – Checklists – Worst Practices – Building Blocks• Data Governance in Action: Tweeting now: – Securities example #dataed – Retail example• Take Aways/References/Q&A 28 Copyright 2013 by Data Blueprint
  29. 29. Unlock Business Value through Data Governance• Context: What is Data Management/ DAMA/DM BoK/CDMP?• What is Data Governance and why is it Important? – Organizational -> IT -> Data – Requirements for Effective Data Governance• Data Governance – Frameworks – Checklists – Worst Practices – Building Blocks• Data Governance in Action: Tweeting now: – Securities example #dataed – Retail example• Take Aways/References/Q&A 29 Copyright 2013 by Data Blueprint
  30. 30. Getting Started Assess context Execute plan Define DG roadmap Evaluate results Secure executive mandate Revise plan Apply change management Assign Data Stewards(Occurs once) (Repeats) 30 Copyright 2013 by Data Blueprint
  31. 31. Data Governance Frameworks• A system of ideas for guiding analyses• A means of organizing project data ™• Data integration Classification Audience Executive Names Perspectives A l i g What C o m p o s i t e Inventory Identification Products I n t e g r a t i o n s How Process Identification Forecast Sales Where Distribution Identification Material Supply Ntwk A l i g n m e n t Who Responsibility Identification General Mgmt When Timing Identification Product Cycle C o m p o s i t e Why Motivation Identification New Markets Version 3.0 I n t e g r a t i o n s A l i g Classification Names Scope Model Names priorities decision Contexts Product Types Plan Production Product Dist. Ntwk Product Mgmt Market Cycle Revenue Growth Perspective Sell Products Voice Comm. Ntwk Engineering Design Planning Cycle Expns Reduction n Parts Bins Customers Take Orders Train Employees Data Comm. Ntwk Manu. Process Ntwk Manu. Engineering Accounting Order Cycle Employee Cycle Cust Convenience Customer Satis. n m Territories Assign Territories Finance Maint. Cycle Regulatory Comp. m Orders Employees Develop Markets Maintain Facilities Parts Dist. Ntwk Personnel Dist. Ntwk Transportation Distribution Production Cycle Sales Cycle New Capital Social Contribution e e.g. Vehicles e.g. Repair Products e.g. etc., etc. e.g. Marketing e.g. Economic Cycle e.g. Increased Yield e (Business Context n Accounts Record Transctns Sales Accounting Cycle Increased Quality n (Scope Identification t t Lists) Planners) T List: Inventory Types List: Process Types List: Distribution Types List: Responsibility Types List: Timing Types List: Motivation Types T r r a a n Inventory Definition Process Definition Distribution Definition Responsibility Definition Timing Definition Motivation Definition n s s Business Mgmt f o e.g.: primitive model: e.g.: composite model: f o Business e.g. e.g. e.g. e.g. e.g. e.g. Perspective r m r m Concepts a a (Business Concept t t (Business Definition i Business Entity Business Transform Business Location Business Role Business Interval Business End i Owners) o o Models) n Business Relationship Business Input/Output Business Connection Business Work Product Business Moment Business Means n making framework s s Inventory Representation Process Representation Distribution Representation Responsibility Representation Timing Representation Motivation Representation Architect e.g. e.g. e.g. e.g. e.g. e.g. System Perspective Logic (Business Logic (System System Entity System Transform System Location System Role System Interval System End Representation Models) Designers) System Relationship System Input /Output System Connection System Work Product System Moment System Means Inventory Specification Process Specification Distribution Specification Responsibility Specification Timing Specification Motivation Specification Engineer e.g. e.g. e.g. e.g. e.g. e.g. Technology Perspective Physics• A means of (Business Physics (Technology Technology Entity Technology Transform Technology Location Technology Role Technology Interval Technology End Specification Models) Builders) Technology Relationship Technology Input /Output Technology Connection Technology Work Product Technology Moment Technology Means A Inventory Configuration Process Configuration Distribution Configuration Responsibility Configuration Timing Configuration Motivation Configuration A l l Technician i g e.g. e.g. e.g. e.g. e.g. e.g. i g Tool Perspective n m n m e Components e n (Business Component n t t (Tool Configuration Implementers) Tool Entity Tool Transform Tool Location Tool Role Tool Interval Tool End Models) T Tool Relationship Tool Input /Output Tool Connection Tool Work Product Tool Moment Tool Means T r r a a n n assessing progress Enterprise s f Inventory Process Distribution Responsibility Timing Motivation s f Operations Perspective o r Instantiations Instantiations Instantiations Instantiations Instantiations Instantiations o r Instances m m (Users) a a (Implementations) t t The i o Operations Entities Operations Transforms Operations Locations Operations Roles Operations Intervals Operations Ends i o The Enterprise n s Operations Relationships Operations In/Outputs Operations Connections Operations Work Products Operations Moments Operations Means n s Enterprise C o m p o s i t e I n t e g r a t i o n s A l i g n m e n t C o m p o s i t e I n t e g r a t i o n s Audience *Horizontal integration lines are shown for example purposes Perspectives Inventory Process Distribution Responsibility Timing Motivation only and are not a complete set. Composite, integrative rela- Enterprise Sets Flows Networks Assignments Cycles Intentions tionships connecting every cell Names horizontally potentially exist. © 1987-2011 John A. Zachman, all rights reserved. Zachman® and Zachman International® are registered trademarks of John A. Zachman 31 Copyright 2013 by Data Blueprint
  32. 32. Data Governance Institute Copyright 2013 by Data Blueprint8  -­‐    datablueprint.com 1/26/2010 http://www.datagovernance.com/  -­‐  all  rights  reserved! http://www.datagovernance.com/ ©            Copyright  this  and  previous  years  by  Data  Blueprint  
  33. 33. KiK Consulting http://www.kikconsulting.com/ Copyright 2013 by Data Blueprint 88
  34. 34. IBM Data Governance Council Copyright 2013 by Data Blueprint http://www-01.ibm.com/software/data/system-z/data-governance/workshops.html 88
  35. 35. Elements of Effective Data Governance See IBM Data Governance Council, http://www-01.ibm.com/software/tivoli/ governance/servicemanagement/by Data Blueprint Copyright 2013 data-governance.html. 88
  36. 36. American College Personnel Association 36 Copyright 2013 by Data Blueprint
  37. 37. Data Governance from the DM BoK Illustration from The DAMA Guide to the Data Management Body of Knowledge p.Copyright 2013 byData Blueprint 37 © 2009 by DAMA International 1313
  38. 38. NASCIO DG Implementation Process 38 Copyright 2013 by Data Blueprint
  39. 39. NASCIO Scorecard 39 Copyright 2013 by Data Blueprint
  40. 40. 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 40 Copyright 2013 by Data Blueprint Source: “Data Governance Checklist for Educators” by Angela Guess; http://www.dataversity.net/archives/5198
  41. 41. 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 41 Copyright 2013 by Data Blueprint Source: “Data Governance Checklist for Educators” by Angela Guess; http://www.dataversity.net/archives/5198
  42. 42. 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 42 Source: “Data Governance Checklist for Educators” by Angela Guess; http://www.dataversity.net/archives/5198 Copyright 2013 by Data Blueprint
  43. 43. Largely Ineffective DG Investments • Approximately, 10% percent of organizations achieve parity and (potential positive returns) on their DM investments. • Only 30% of DM investments achieve tangible returns at all. • Seventy percent of organizations have very small or no tangible return on their DM investments. 43 Copyright 2013 by Data Blueprint
  44. 44. 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. 44 Copyright 2013 by Data Blueprint from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
  45. 45. 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 45 Copyright 2013 by Data Blueprint from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
  46. 46. Data Governance Primary Deliverables• Data Policies• Data Standards• Resolved Issues• Data Management Projects and Services• Quality Data and Information• Recognized Data Value 46 Copyright 2013 by Data Blueprint from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
  47. 47. Data Governance Roles and ResponsibilitiesParticipants: Consumers:• Executive Data Stewards • Data Producers• Coordinating Data Stewards • Knowledge Workers• Business Data Stewards • Managers and Executives• Data Professionals • Data Professionals• DM Executive • Customers• CIOSuppliers:• Business Executives• IT Executives• Data Stewards• Regulatory Bodies 47 Copyright 2013 by Data Blueprint from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
  48. 48. Data Governance Technologies• Intranet Website• E-Mail• Metadata Tools• Metadata Repository• Issue Management Tools• Data Governance KPI Dashboard 48 Copyright 2013 by Data Blueprint from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
  49. 49. 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 49 Copyright 2013 by Data Blueprint from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
  50. 50. Unlock Business Value through Data Governance• Context: What is Data Management/ DAMA/DM BoK/CDMP?• What is Data Governance and why is it Important? – Organizational -> IT -> Data – Requirements for Effective Data Governance• Data Governance – Frameworks – Checklists – Worst Practices – Building Blocks• Data Governance in Action: Tweeting now: – Securities example #dataed – Retail example• Take Aways/References/Q&A 50 Copyright 2013 by Data Blueprint
  51. 51. Unlock Business Value through Data Governance• Context: What is Data Management/ DAMA/DM BoK/CDMP?• What is Data Governance and why is it Important? – Organizational -> IT -> Data – Requirements for Effective Data Governance• Data Governance – Frameworks – Checklists – Worst Practices – Building Blocks• Data Governance in Action: Tweeting now: – Securities example #dataed – Retail example• Take Aways/References/Q&A 51 Copyright 2013 by Data Blueprint
  52. 52. Data Governance Examples, cont’dFormalizing the Role of U.S. Army IT Governance/Compliance 52 Copyright 2013 by Data Blueprint
  53. 53. Suicide Mitigation 53 Copyright 2013 by Data Blueprint
  54. 54. Suicide Mitigation Mapping Data Deploy Work ments History Abuse Soldier Legal Mental illness Issues Suicide AnalysisDMSS G1 DMDC FAP CID MDRData objects All sources Best source for How reconcilecomplete? identified? each object? differences between sources? 12 54 Copyright 2013 by Data Blueprint
  55. 55. 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 55 Copyright 2013 by Data Blueprint
  56. 56. Communication Patterns 56 Source: The Challenge and the Promise: Strengthening the Force, Preventing Suicide and Saving Lives - The Final Report of Copyright 2013 by Data Blueprint the Department of Defense Task Force on the Prevention of Suicide by Members of the Armed Forces - August 2010
  57. 57. Example of Poor Data GovernanceMizuho Securities Example• Wanted to sell 1 share for 600,000 yen• Sold 600,000 shares for 1 CLUMSY typing cost a Japanese bank yen at least £128 million and staff their Christmas bonuses yesterday, after a• $347 million loss trader mistakenly sold 600,000 more• In-house system did not have shares than he should have. The limit checking trader at Mizuho Securities, who has not been named, fell foul of what is• Tokyo stock exchange known in financial circles as “fat finger system did not have limit syndrome” where a dealer types incorrect details into his computer. He checking wanted to sell one share in a new• And doesnt allow order telecoms company called J Com, for cancellations 600,000 yen (about £3,000). 57 Copyright 2013 by Data Blueprint
  58. 58. Diaper Story Old New Shipping Semi Best Terms 2/10 net 30 ? Turns 5 50 Risks same JIT 58 Copyright 2013 by Data Blueprint
  59. 59. Unlock Business Value through Data Governance• Context: What is Data Management/ DAMA/DM BoK/CDMP?• What is Data Governance and why is it Important? – Organizational -> IT -> Data – Requirements for Effective Data Governance• Data Governance – Frameworks – Checklists – Worst Practices – Building Blocks• Data Governance in Action: Tweeting now: – Securities example #dataed – Retail example• Take Aways/References/Q&A 59 Copyright 2013 by Data Blueprint
  60. 60. Unlock Business Value through Data Governance• Context: What is Data Management/ DAMA/DM BoK/CDMP?• What is Data Governance and why is it Important? – Organizational -> IT -> Data – Requirements for Effective Data Governance• Data Governance – Frameworks – Checklists – Worst Practices – Building Blocks• Data Governance in Action: Tweeting now: – Securities example #dataed – Retail example• Take Aways/References/Q&A 60 Copyright 2013 by Data Blueprint
  61. 61. Take Aways• Need for DG is increasing• DG is a new discipline – Must conform to constraints – No one best way• Comparing DG frameworks can be useful• DG directs data management efforts• DG interacts directly and indirectly with the organization• Process improvement can improve DG practices 61 Copyright 2013 by Data Blueprint
  62. 62. 10 DG Worst Practices in Detail1. 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 effectively2. 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 62 Source: “Data Governance Worst Practices” by Angela Guess; http://www.dataversity.net/archives/4895 Copyright 2013 by Data Blueprint

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