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Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)

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Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)

  1. 1. Structured and Comprehensive Approach to Data Management and the Data Management Book of Knowledge (DMBOK) Alan McSweeney
  2. 2. Objectives • To provide an overview of a structured approach to developing and implementing a detailed data management policy including frameworks, standards, project, team and maturity March 8, 2010 2
  3. 3. Agenda • Introduction to Data Management • State of Information and Data Governance • Other Data Management Frameworks • Data Management and Data Management Book of Knowledge (DMBOK) • Conducting a Data Management Project • Creating a Data Management Team • Assessing Your Data Management Maturity March 8, 2010 3
  4. 4. Preamble • Every good presentation should start with quotations from The Prince and Dilbert March 8, 2010 4
  5. 5. Management Wisdom • There is nothing more difficult to take in hand, more perilous to conduct or more uncertain in its success than to take the lead in the introduction of a new order of things. − The Prince • Never be in the same room as a decision. I'll illustrate my point with a puppet show that I call "Journey to Blameville" starring "Suggestion Sam" and "Manager Meg.“ • You will often be asked to comment on things you don't understand. These handouts contain nonsense phrases that can be used in any situation so, let's dominate our industry with quality implementation of methodologies. • Our executives have started their annual strategic planning sessions. This involves sitting in a room with inadequate data until an illusion of knowledge is attained. Then we'll reorganise, because that's all we know how to do. − Dilbert March 8, 2010 5
  6. 6. Information • Information in all its forms – input, processed, outputs – is a Applications core component of any IT system • Applications exist to process data supplied by users and other applications Processes Information • Data breathes life into applications IT Systems • Data is stored and managed by infrastructure – hardware and software • Data is a key organisation asset with a substantial value People Infrastructure • Significant responsibilities are imposed on organisations in managing data March 8, 2010 6
  7. 7. Data, Information and Knowledge • Data is the representation of facts as text, numbers, graphics, images, sound or video • Data is the raw material used to create information • Facts are captured, stored, and expressed as data • Information is data in context • Without context, data is meaningless - we create meaningful information by interpreting the context around data • Knowledge is information in perspective, integrated into a viewpoint based on the recognition and interpretation of patterns, such as trends, formed with other information and experience • Knowledge is about understanding the significance of information • Knowledge enables effective action March 8, 2010 7
  8. 8. Data, Information, Knowledge and Action Knowledge Action Information Data March 8, 2010 8
  9. 9. Information is an Organisation Asset • Tangible organisation assets are seen as having a value and are managed and controlled using inventory and asset management systems and procedures • Data, because it is less tangible, is less widely perceived as a real asset, assigned a real value and managed as if it had a value • High quality, accurate and available information is a pre- requisite to effective operation of any organisation March 8, 2010 9
  10. 10. Data Management and Project Success • Data is fundamental to the effective and efficient operation of any solution − Right data − Right time − Right tools and facilities • Without data the solution has no purpose • Data is too often overlooked in projects • Project managers frequently do not appreciate the complexity of data issues March 8, 2010 10
  11. 11. Generalised Information Management Lifecycle Enter, Create, Acquire, • Generalised lifecycle that Derive, Update, Capture differs for specific information types Store, Manage, M an Replicate and Distribute ag e, Co nt ro la nd Ad Protect and Recover mi n is t er • Design, define and implement framework to manage Archive and Recall information through this lifecycle Delete/Remove March 8, 2010 11
  12. 12. Expanded Generalised Information Management Lifecycle Plan, Design and Specify De Implement sig Underlying n, Im Infrastructure ple m en Enter, Create, t, M Acquire, Derive, an ag Update, Capture e, Co nt Store, Manage, ro la Replicate and nd Distribute Ad mi ni ste r • Include phases for information Protect and Recover management lifecycle design and implementation of Archive and Recall appropriate hardware and software to actualise lifecycle Delete/Remove March 8, 2010 12
  13. 13. Data and Information Management • Data and information management is a business process consisting of the planning and execution of policies, practices, and projects that acquire, control, protect, deliver, and enhance the value of data and information assets March 8, 2010 13
  14. 14. Data and Information Management To manage and utilise information as a strategic asset To implement processes, policies, infrastructure and solutions to govern, protect, maintain and use information To make relevant and correct information available in all business processes and IT systems for the right people in the right context at the right time with the appropriate security and with the right quality To exploit information in business decisions, processes and relations March 8, 2010 14
  15. 15. Data Management Goals • Primary goals − To understand the information needs of the enterprise and all its stakeholders − To capture, store, protect, and ensure the integrity of data assets − To continually improve the quality of data and information, including accuracy, integrity, integration, relevance and usefulness of data − To ensure privacy and confidentiality, and to prevent unauthorised inappropriate use of data and information − To maximise the effective use and value of data and information assets March 8, 2010 15
  16. 16. Data Management Goals • Secondary goals − To control the cost of data management − To promote a wider and deeper understanding of the value of data assets − To manage information consistently across the enterprise − To align data management efforts and technology with business needs March 8, 2010 16
  17. 17. Triggers for Data Management Initiative • When an enterprise is about to undertake architectural transformation, data management issues need to be understood and addressed • Structured and comprehensive approach to data management enables the effective use of data to take advantage of its competitive advantages March 8, 2010 17
  18. 18. Data Management Principles • Data and information are valuable enterprise assets • Manage data and information carefully, like any other asset, by ensuring adequate quality, security, integrity, protection, availability, understanding and effective use • Share responsibility for data management between business data owners and IT data management professionals • Data management is a business function and a set of related disciplines March 8, 2010 18
  19. 19. Organisation Data Management Function • Business function of planning for, controlling and delivering data and information assets • Development, execution, and supervision of plans, policies, programs, projects, processes, practices and procedures that control, protect, deliver, and enhance the value of data and information assets • Scope of the data management function and the scale of its implementation vary widely with the size, means, and experience of organisations • Role of data management remains the same across organisations even though implementation differs widely March 8, 2010 19
  20. 20. Scope of Complete Data Management Function Data Management Data Governance Data Architecture Management Data Development Data Operations Management Data Security Management Data Quality Management Reference and Master Data Data Warehousing and Business Management Intelligence Management Document and Content Management Metadata Management March 8, 2010 20
  21. 21. Shared Role Between Business and IT • Data management is a shared responsibility between data management professionals within IT and the business data owners representing the interests of data producers and information consumers • Business data ownership is the concerned with accountability for business responsibilities in data management • Business data owners are data subject matter experts • Represent the data interests of the business and take responsibility for the quality and use of data March 8, 2010 21
  22. 22. Why Develop and Implement a Data Management Framework? • Improve organisation data management efficiency • Deliver better service to business • Improve cost-effectiveness of data management • Match the requirements of the business to the management of the data • Embed handling of compliance and regulatory rules into data management framework • Achieve consistency in data management across systems and applications • Enable growth and change more easily • Reduce data management and administration effort and cost • Assist in the selection and implementation of appropriate data management solutions • Implement a technology-independent data architecture March 8, 2010 22
  23. 23. Data Management Issues March 8, 2010 23
  24. 24. Data Management Issues • Discovery - cannot find the right information • Integration - cannot manipulate and combine information • Insight - cannot extract value and knowledge from information • Dissemination - cannot consume information • Management – cannot manage and control information volumes and growth March 8, 2010 24
  25. 25. Data Management Problems – User View • Managing Storage Equipment • Application Recoveries / Backup Retention • Vendor Management • Power Management • Regulatory Compliance • Lack of Integrated Tools • Dealing with Performance Problems • Data Mobility • Archiving and Archive Management • Storage Provisioning • Managing Complexity • Managing Costs • Backup Administration and Management • Proper Capacity Forecasting and Storage Reporting • Managing Storage Growth March 8, 2010 25
  26. 26. Information Management Challenges • Explosive Data Growth − Value and volume of data is overwhelming − More data is see as critical − Annual rate of 50+% percent • Compliance Requirements − Compliance with stringent regulatory requirements and audit procedures • Fragmented Storage Environment − Lack of enterprise-wide hardware and software data storage strategy and discipline • Budgets − Frozen or being cut March 8, 2010 26
  27. 27. Data Quality • Poor data quality costs real money • Process efficiency is negatively impacted by poor data quality • Full potential benefits of new systems not be realised because of poor data quality • Decision making is negatively affected by poor data quality March 8, 2010 27
  28. 28. State of Information and Data Governance • Information and Data Governance Report, April 2008 − International Association for Information and Data Quality (IAIDQ) − University of Arkansas at Little Rock, Information Quality Program (UALR-IQ) March 8, 2010 28
  29. 29. Your Organisation Recognises and Values Information as a Strategic Asset and Manages it Accordingly Strongly Disagree 3.4% Disagree 21.5% Neutral 17.1% Agree 39.5% Strongly Agree 18.5% 0% 10% 20% 30% 40% 50% March 8, 2010 29
  30. 30. Direction of Change in the Results and Effectiveness of the Organisation's Formal or Informal Information/Data Governance Processes Over the Past Two Years Results and Effectiveness Have Significantly 8.8% Improved Results and Effectiveness Have Improved 50.0% Results and Effectiveness Have Remained 31.9% Essentially the Same Results and Effectiveness Have Worsened 3.9% Results and Effectiveness Have Significantly 0.0% Worsened Don’t Know 5.4% 0% 10% 20% 30% 40% 50% 60% 70% March 8, 2010 30
  31. 31. Perceived Effectiveness of the Organisation's Current Formal or Informal Information/Data Governance Processes Excellent (All Goals are 2.5% Met) Good (Most Goals are 21.1% Met) OK (Some Goals are Met) 51.5% Poor (Few Goals are Met) 19.1% Very Poor (No Goals are 3.9% Met) Don’t Know 2.0% 0% 10% 20% 30% 40% 50% 60% 70% March 8, 2010 31
  32. 32. Actual Information/Data Governance Effectiveness vs. Organisation's Perception It is Better Than Most 20.1% People Think It is the Same as Most 32.4% People Think It is Worse Than Most 35.8% People Think Don’t Know 11.8% 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% March 8, 2010 32
  33. 33. Current Status of Organisation's Information/Data Governance Initiatives Started an Information/Data Governance Initiative, but 1.5% Discontinued the Effort Considered a Focused Information/Data Governance 0.5% Effort but Abandoned the Idea None Being Considered - Keeping the Status Quo 7.4% Exploring, Still Seeking to Learn More 20.1% Evaluating Alternative Frameworks and Information 23.0% Governance Structures Now Planning an Implementation 13.2% First Iteration Implemented the Past 2 Years 19.1% First Interation"in Place for More Than 2 Years 8.8% Don’t Know 6.4% 0% 5% 10% 15% 20% 25% 30% March 8, 2010 33
  34. 34. Expected Changes in Organisation's Information/Data Governance Efforts Over the Next Two Years Will Increase Significantly 46.6% Will Increase Somewhat 39.2% Will Remain the Same 10.8% Will Decrease Somewhat 1.0% Will Decrease Significantly 0.5% Don’t Know 2.0% 0% 10% 20% 30% 40% 50% 60% March 8, 2010 34
  35. 35. Overall Objectives of Information / Data Governance Efforts Improve Data Quality 80.2% Establish Clear Decision Rules and Decisionmaking 65.6% Processes for Shared Data Increase the Value of Data Assets 59.4% Provide Mechanism to Resolve Data Issues 56.8% Involve Non-IT Personnel in Data Decisions IT Should 55.7% not Make by Itself Promote Interdependencies and Synergies Between 49.6% Departments or Business Units Enable Joint Accountability for Shared Data 45.3% Involve IT in Data Decisions non-IT Personnel Should 35.4% not Make by Themselves Other 5.2% None Applicable 1.0% Don't Know 2.6% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100 % March 8, 2010 35
  36. 36. Change In Organisation's Information / Data Quality Over the Past Two Years Information / Data Quality 10.5% Has Significantly Improved Information / Data Quality 68.4% Has Improved Information / Data Quality Has Remained Essentially 15.8% the Same Information / Data Quality 3.5% Has Worsened Information / Data Quality 0.0% Has Significantly Worsened Don’t Know 1.8% 0% 10% 20% 30% 40% 50% 60% 70% 80% March 8, 2010 36
  37. 37. Maturity Of Information / Data Governance Goal Setting And Measurement In Your Organisation 5 - Optimised 3.7% 4 - Managed 11.8% 3 - Defined 26.7% 2 - Repeatable 28.9% 1 - Ad-hoc 28.9% 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% March 8, 2010 37
  38. 38. Maturity Of Information / Data Governance Processes And Policies In Your Organisation 5 - Optimised 1.6% 4 - Managed 4.8% 3 - Defined 24.5% 2 - Repeatable 46.3% 1 - Ad-hoc 22.9% 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% March 8, 2010 38
  39. 39. Maturity Of Responsibility And Accountability For Information / Data Governance Among Employees In Your Organisation 5 - Optimised 6.9% 4 - Managed 3.2% 3 - Defined 31.7% 2 - Repeatable 25.4% 1 - Ad-hoc 32.8% 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% March 8, 2010 39
  40. 40. Other Data Management Frameworks March 8, 2010 40
  41. 41. Other Data Management-Related Frameworks • TOGAF (and other enterprise architecture standards) define a process for arriving an at enterprise architecture definition, including data • TOGAF has a phase relating to data architecture • TOGAF deals with high level • DMBOK translates high level into specific details • COBIT is concerned with IT governance and controls: − IT must implement internal controls around how it operates − The systems IT delivers to the business and the underlying business processes these systems actualise must be controlled – these are controls external to IT − To govern IT effectively, COBIT defines the activities and risks within IT that need to be managed • COBIT has a process relating to data management • Neither TOGAF nor COBIT are concerned with detailed data management design and implementation March 8, 2010 41
  42. 42. DMBOK, TOGAF and COBIT Can be a DMBOK Is a Specific and Precursor to Comprehensive Data Implementing Oriented Framework Data Management DMBOK Provides Detailed for Definition, Implementation and TOGAF Defines the Process Operation of Data for Creating a Data Management and Utilisation Architecture as Part of an Overall Enterprise Architecture Can Provide a Maturity Model for Assessing Data Management COBIT Provides Data Governance as Part of Overall IT Governance March 8, 2010 42
  43. 43. DMBOK, TOGAF and COBIT – Scope and Overlap DMBOK Data Development Data Operations Management Reference and Master Data Management Data Warehousing and Business Intelligence Management TOGAF Document and Content Management Metadata Management Data Quality Management Data Architecture Management Data Management Data Migration Data Governance Data Security COBIT Management March 8, 2010 43
  44. 44. TOGAF and Data Management • Phase C1 (subset of Phase C) relates to Phase A: Architecture defining a data Vision Phase H: Phase B: architecture Architecture Business Change Architecture Management Phase C1: Data Architecture Phase G: Phase C: Requirements Information Implementation Management Systems Governance Architecture Phase C2: Solutions and Application Phase F: Phase D: Architecture Migration Technology Planning Architecture Phase E: Opportunities and Solutions March 8, 2010 44
  45. 45. TOGAF Phase C1: Information Systems Architectures - Data Architecture - Objectives • Purpose is to define the major types and sources of data necessary to support the business, in a way that is: − Understandable by stakeholders − Complete and consistent − Stable • Define the data entities relevant to the enterprise • Not concerned with design of logical or physical storage systems or databases March 8, 2010 45
  46. 46. TOGAF Phase C1: Information Systems Architectures - Data Architecture - Overview Phase C1: Information Systems Architectures - Data Architecture Approach Elements Inputs Steps Outputs Key Considerations for Data Reference Materials External to the Select Reference Models, Architecture Enterprise Viewpoints, and Tools Develop Baseline Data Architecture Architecture Repository Non-Architectural Inputs Description Develop Target Data Architecture Architectural Inputs Description Perform Gap Analysis Define Roadmap Components Resolve Impacts Across the Architecture Landscape Conduct Formal Stakeholder Review Finalise the Data Architecture Create Architecture Definition Document March 8, 2010 46
  47. 47. TOGAF Phase C1: Information Systems Architectures - Data Architecture - Approach - Key Considerations for Data Architecture • Data Management − Important to understand and address data management issues − Structured and comprehensive approach to data management enables the effective use of data to capitalise on its competitive advantages − Clear definition of which application components in the landscape will serve as the system of record or reference for enterprise master data − Will there be an enterprise-wide standard that all application components, including software packages, need to adopt − Understand how data entities are utilised by business functions, processes, and services − Understand how and where enterprise data entities are created, stored, transported, and reported − Level and complexity of data transformations required to support the information exchange needs between applications − Requirement for software in supporting data integration with external organisations March 8, 2010 47
  48. 48. TOGAF Phase C1: Information Systems Architectures - Data Architecture - Approach - Key Considerations for Data Architecture • Data Migration − Identify data migration requirements and also provide indicators as to the level of transformation for new/changed applications − Ensure target application has quality data when it is populated − Ensure enterprise-wide common data definition is established to support the transformation March 8, 2010 48
  49. 49. TOGAF Phase C1: Information Systems Architectures - Data Architecture - Approach - Key Considerations for Data Architecture • Data Governance − Ensures that the organisation has the necessary dimensions in place to enable the data transformation − Structure – ensures the organisation has the necessary structure and the standards bodies to manage data entity aspects of the transformation − Management System - ensures the organisation has the necessary management system and data-related programs to manage the governance aspects of data entities throughout its lifecycle − People - addresses what data-related skills and roles the organisation requires for the transformation March 8, 2010 49
  50. 50. TOGAF Phase C1: Information Systems Architectures - Data Architecture - Outputs • Refined and updated versions of the Architecture Vision phase deliverables − Statement of Architecture Work − Validated data principles, business goals, and business drivers • Draft Architecture Definition Document − Baseline Data Architecture − Target Data Architecture • Business data model • Logical data model • Data management process models • Data Entity/Business Function matrix • Views corresponding to the selected viewpoints addressing key stakeholder concerns − Draft Architecture Requirements Specification • Gap analysis results • Data interoperability requirements • Relevant technical requirements • Constraints on the Technology Architecture about to be designed • Updated business requirements • Updated application requirements − Data Architecture components of an Architecture Roadmap March 8, 2010 50
  51. 51. COBIT Structure COBIT Plan and Organise (PO) Acquire and Implement (AI) Del