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Structured and
Comprehensive Approach to
Data Management and the
Data Management Book of
Knowledge (DMBOK)


Alan McSweeney
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
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
Preamble

•   Every good presentation should start with quotations from
    The Prince and Dilbert




    March 8, 2010                                               4
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
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
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
Data, Information, Knowledge and Action


 Knowledge                                Action




        Information
                                          Data


 March 8, 2010                                     8
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
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
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
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
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
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
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
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
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
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
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
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
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
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
Data Management Issues




 March 8, 2010           23
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
Other Data Management Frameworks




 March 8, 2010                     40
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
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
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
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
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
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
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
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
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
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
COBIT Structure
                                                                           COBIT


Plan and Organise (PO)                     Acquire and Implement (AI)                     Deliver and Support (DS)                    Monitor and Evaluate (ME)

                                                                                                             DS1 Define and manage service                ME1 Monitor and evaluate IT
                  PO1 Define a strategic IT plan              AI1 Identify automated solutions
                                                                                                                         levels                                 performance

                   PO2 Define the information                    AI2 Acquire and maintain                                                                  ME2 Monitor and evaluate
                                                                                                            DS2 Manage third-party services
                         architecture                              application software                                                                        internal control

                  PO3 Determine technological                    AI3 Acquire and maintain                    DS3 Manage performance and                      ME3 Ensure regulatory
                           direction                             technology infrastructure                            capacity                                    compliance

                   PO4 Define the IT processes,
                                                               AI4 Enable operation and use                  DS4 Ensure continuous service                 ME4 Provide IT governance
                  organisation and relationships

                  PO5 Manage the IT investment                    AI5 Procure IT resources                    DS5 Ensure systems security

                 PO6 Communicate management
                                                                    AI6 Manage changes                       DS6 Identify and allocate costs
                       aims and direction

                                                              AI7 Install and accredit solutions
                 PO7 Manage IT human resources                                                                DS7 Educate and train users
                                                                         and changes

                                                                                                              DS8 Manage service desk and
                          PO8 Manage quality
                                                                                                                      incidents

                 PO9 Assess and manage IT risks                                                              DS9 Manage the configuration


                         PO10 Manage projects                                                                   DS10 Manage problems


                                                                                                              DS11 Manage data
                                                                                                               DS12 Manage the physical
                                                                                                                    environment

                                                                                                                DS13 Manage operations

 March 8, 2010                                                                                                                                                                       51
COBIT and Data Management

•   COBIT objective DS11 Manage Data within the Deliver and
    Support (DS) domain
•   Effective data management requires identification of data
    requirements
•   Data management process includes establishing effective
    procedures to manage the media library, backup and
    recovery of data and proper disposal of media
•   Effective data management helps ensure the quality,
    timeliness and availability of business data


    March 8, 2010                                               52
COBIT and Data Management

•   Objective is the control over the IT process of managing data that
    meets the business requirement for IT of optimising the use of
    information and ensuring information is available as required
•   Focuses on maintaining the completeness, accuracy, availability and
    protection of data
•   Involves taking actions
      − Backing up data and testing restoration
      − Managing onsite and offsite storage of data
      − Securely disposing of data and equipment
•   Measured by
      − User satisfaction with availability of data
      − Percent of successful data restorations
      − Number of incidents where sensitive data were retrieved after media were
        disposed of


    March 8, 2010                                                                  53
COBIT Process DS11 Manage Data
•   DS11.1 Business Requirements for Data Management
      − Establish arrangements to ensure that source documents expected from the business are received, all data received from the
        business are processed, all output required by the business is prepared and delivered, and restart and reprocessing needs are
        supported
•   DS11.2 Storage and Retention Arrangements
      − Define and implement procedures for data storage and archival, so data remain accessible and usable
      − Procedures should consider retrieval requirements, cost-effectiveness, continued integrity and security requirements
      − Establish storage and retention arrangements to satisfy legal, regulatory and business requirements for documents, data, archives,
        programmes, reports and messages (incoming and outgoing) as well as the data (keys, certificates) used for their encryption and
        authentication
•   DS11.3 Media Library Management System
      − Define and implement procedures to maintain an inventory of onsite media and ensure their usability and integrity
      − Procedures should provide for timely review and follow-up on any discrepancies noted
•   DS11.4 Disposal
      − Define and implement procedures to prevent access to sensitive data and software from equipment or media when they are
        disposed of or transferred to another use
      − Procedures should ensure that data marked as deleted or to be disposed cannot be retrieved.
•   DS11.5 Backup and Restoration
      − Define and implement procedures for backup and restoration of systems, data and documentation in line with business
        requirements and the continuity plan
      − Verify compliance with the backup procedures, and verify the ability to and time required for successful and complete restoration
      − Test backup media and the restoration process
•   DS11.6 Security Requirements for Data Management
      − Establish arrangements to identify and apply security requirements applicable to the receipt, processing, physical storage and
        output of data and sensitive messages
      − Includes physical records, data transmissions and any data stored offsite




    March 8, 2010                                                                                                                            54
COBIT Data Management Goals and Metrics
        Activity Goals                       Process Goals                           Activity Goals

•Backing up data and testing           •Maintain the completeness,             •Backing up data and testing
restoration                            accuracy, validity and                  restoration
•Managing onsite and offsite           accessibility of stored data            •Managing onsite and offsite
storage of data                        •Secure data during disposal            storage of data
•Securely disposing of data            of media                                •Securely disposing of data
and equipment                          •Effectively manage storage             and equipment
                                       media



      Are Measured                          Are Measured                            Are Measured
           By                  Drive             By                    Drive             By

      Key Performance                      Process Key Goal                      IT Key Goal Indicators
         Indicators                           Indicators
                                       •% of successful data                   •Occurrences of inability to
                                       restorations                            recover data critical to
•Frequency of testing of               •# of incidents where                   business process
backup media                           sensitive data were retrieved           •User satisfaction with
•Average time for data                 after media were disposed of            availability of data
restoration                            •# of down time or data                 •Incidents of noncompliance
                                       integrity incidents caused by           with laws due to storage
                                       insufficient storage capacity           management issues

 March 8, 2010                                                                                                55
Data Management Book of Knowledge (DMBOK)




 March 8, 2010                              56
Data Management Book of Knowledge (DMBOK)

•   DMBOK is a generalised and comprehensive framework for
    managing data across the entire lifecycle
•   Developed by DAMA (Data Management Association)
•   DMBOK provides a detailed framework to assist
    development and implementation of data management
    processes and procedures and ensures all requirements
    are addressed
•   Enables effective and appropriate data management
    across the organisation
•   Provides awareness and visibility of data management
    issues and requirements
    March 8, 2010                                            57
Data Management Book of Knowledge (DMBOK)

•   Not a solution to your data management needs
•   Framework and methodology for developing and
    implementing an appropriate solution
•   Generalised framework to be customised to meet specific
    needs
•   Provide a work breakdown structure for a data
    management project to allow the effort to be assessed
•   No magic bullet



    March 8, 2010                                             58
Scope and Structure of Data Management Book of
Knowledge (DMBOK)

                      Data Management
                    Environmental Elements

   Data
Management
 Functions




 March 8, 2010                                   59
DMBOK Data Management Functions
                                        Data Management
                                            Functions

                 Data Governance                      Data Architecture Management



                 Data Development                     Data Operations Management



             Data Security Management                     Data Quality Management



                                                      Data Warehousing and Business
 Reference and Master Data Management
                                                         Intelligence Management



     Document and Content Management                       Metadata Management

 March 8, 2010                                                                        60
DMBOK Data Management Functions

•   Data Governance - planning, supervision and control over data management and
    use
•   Data Architecture Management - defining the blueprint for managing data assets
•   Data Development - analysis, design, implementation, testing, deployment,
    maintenance
•   Data Operations Management - providing support from data acquisition to
    purging
•   Data Security Management - Ensuring privacy, confidentiality and appropriate
    access
•   Data Quality Management - defining, monitoring and improving data quality
•   Reference and Master Data Management - managing master versions and
    replicas
•   Data Warehousing and Business Intelligence Management - enabling reporting
    and analysis
•   Document and Content Management - managing data found outside of databases
•   Metadata Management - integrating, controlling and providing metadata


    March 8, 2010                                                                    61
DMBOK Data Management Environmental Elements
                                     Data Management
                                   Environmental Elements


                 Goals and Principles                         Activities



                 Primary Deliverables                 Roles and Responsibilities



           Practices and Techniques                          Technology



           Organisation and Culture

 March 8, 2010                                                                     62
DMBOK Data Management Environmental Elements

•   Goals and Principles - directional business goals of each function and the fundamental
    principles that guide performance of each function
•   Activities - each function is composed of lower level activities, sub-activities, tasks and
    steps
•   Primary Deliverables - information and physical databases and documents created as
    interim and final outputs of each function. Some deliverables are essential, some are
    generally recommended, and others are optional depending on circumstances
•   Roles and Responsibilities - business and IT roles involved in performing and supervising
    the function, and the specific responsibilities of each role in that function. Many roles will
    participate in multiple functions
•   Practices and Techniques - common and popular methods and procedures used to perform
    the processes and produce the deliverables and may also include common conventions,
    best practice recommendations, and alternative approaches without elaboration
•   Technology - categories of supporting technology such as software tools, standards and
    protocols, product selection criteria and learning curves
•   Organisation and Culture – this can include issues such as management metrics, critical
    success factors, reporting structures, budgeting, resource allocation issues, expectations
    and attitudes, style, cultural, approach to change management




    March 8, 2010                                                                                    63
DMBOK Data Management Functions and
Environmental Elements
                   Goals and    Activities   Primary        Roles and        Practices and   Technology   Organisation
                   Principles                Deliverables   Responsibilities Techniques                   and Culture
Data
Governance
Data
Architecture
Management
Data
Development
Data
Operations
Management
                                   Scope of Each Data Management Function
Data Security
Management
Data Quality
Management
Reference and
Master Data
Management
Data
Warehousing
and Business
Intelligence
Management
Document and
Content
Management
Metadata
Management
   March 8, 2010                                                                                                         64
Scope of Data Management Book of Knowledge
(DMBOK) Data Management Framework
•   Hierarchy
      − Function
             • Activity
                − Sub-Activity (not in all cases)
•   Each activity is classified as one (or more) of:
      − Planning Activities (P)
             • Activities that set the strategic and tactical course for other data management
               activities
             • May be performed on a recurring basis
      − Development Activities (D)
             • Activities undertaken within implementation projects and recognised as part of the
               systems development lifecycle (SDLC), creating data deliverables through analysis,
               design, building, testing, preparation, and deployment
      − Control Activities (C)
             • Supervisory activities performed on an on-going basis
      − Operational Activities (O)
             • Service and support activities performed on an on- going basis

    March 8, 2010                                                                                   65
Activity Groups Within Functions

                                                •   Activity groups are
                                                    classifications of data
                                                    management
                   Planning      Development
                                                    activities
                   Activities      Activities   •   Use the activity
                                                    groupings to define
                                                    the scope of data
                                                    management sub-
                                                    projects and identify
                                                    the appropriate tasks:
                  Control       Operational
                 Activities                         − Analysis and design
                                 Activities
                                                    − Implementation
                                                    − Operational
                                                      improvement
                                                    − Management and
                                                      administration

 March 8, 2010                                                                66
DMBOK Function and Activity Structure
                                                                                                             Data
                                                                                                          Management

                                                                                                                                                  Reference and                                   Document and
                     Data Architecture                                Data Operations          Data Security             Data Quality                                       DW and BI                                     Metadata
Data Governance                              Data Development                                                                                      Master Data                                       Content
                      Management                                       Management              Management                Management                                        Management                                    Management
                                                                                                                                                  Management                                      Management

                                                                                                          Understand Data
                                                         Data Modeling,                                                          Develop and Promote      Understand Reference    Understand Business
           Data Management     Understand Enterprise                                                     Security Needs and                                                                               Documents / Records   Understand Metadata
                                                       Analysis, and Solution    Database Support                                    Data Quality           and Master Data            Intelligence
               Planning         Information Needs                                                            Regulatory                                                                                      Management            Requirements
                                                              Design                                                                  Awareness            Integration Needs       Information Needs
                                                                                                           Requirements

                                                                                                                                                           Identify Master and
                                Develop and Maintain                                                                                                                              Define and Maintain
           Data Management                                                        Data Technology       Define Data Security      Define Data Quality        Reference Data                                                     Define the Metadata
                                 the Enterprise Data   Detailed Data Design                                                                                                           the DW / BI         Content Management
                Control                                                            Management                  Policy                Requirement               Sources and                                                          Architecture
                                       Model                                                                                                                                          Architecture
                                                                                                                                                              Contributors

                                 Analyse and Align       Data Model and                                                                                    Define and Maintain      Implement Data
                                                                                                        Define Data Security      Profile, Analyse, and                                                                         Develop and Maintain
                                With Other Business      Design Quality                                                                                    the Data Integration   Warehouses and Data
                                                                                                             Standards            Assess Data Quality                                                                            Metadata Standards
                                      Models              Management                                                                                           Architecture              Marts

                                                                                                                                                           Implement Reference
                                Define and Maintain                                                     Define Data Security                                                                                                    Implement a Managed
                                                                                                                                  Define Data Quality        and Master Data       Implement BI Tools
                                   the Database        Data Implementation                                  Controls and                                                                                                             Metadata
                                                                                                                                        Metrics               Management           and User Interfaces
                                    Architecture                                                             Procedures                                                                                                             Environment
                                                                                                                                                                Solutions

                                Define and Maintain                                                        Manage Users,
                                                                                                                                  Define Data Quality      Define and Maintain      Process Data for                            Create and Maintain
                                the Data Integration                                                    Passwords, and Group
                                                                                                                                    Business Rules             Match Rules        Business Intelligence                              Metadata
                                    Architecture                                                            Membership


                                Define and Maintain                                                                                                                                Monitor and Tune
                                                                                                        Manage Data Access       Test and Validate Data     Establish “Golden”
                                    the DW / BI                                                                                                                                    Data Warehousing                              Integrate Metadata
                                                                                                       Views and Permissions     Quality Requirements            Records
                                    Architecture                                                                                                                                       Processes


                                Define and Maintain                                                         Monitor User                                   Define and Maintain    Monitor and Tune BI
                                                                                                                                 Set and Evaluate Data                                                                           Manage Metadata
                               Enterprise Taxonomies                                                     Authentication and                                  Hierarchies and         Activity and
                                                                                                                                 Quality Service Levels                                                                            Repositories
                                  and Namespaces                                                          Access Behaviour                                      Affiliations        Performance


                                Define and Maintain                                                                              Continuously Measure      Plan and Implement
                                                                                                         Classify Information                                                                                                   Distribute and Deliver
                                   the Metadata                                                                                    and Monitor Data         Integration of New
                                                                                                            Confidentiality                                                                                                            Metadata
                                    Architecture                                                                                        Quality                Data Sources


                                                                                                                                                               Replicate and
                                                                                                                                 Manage Data Quality                                                                             Query, Report, and
                                                                                                         Audit Data Security                               Distribute Reference
                                                                                                                                       Issues                                                                                    Analyse Metadata
                                                                                                                                                             and Master Data


                                                                                                                                 Clean and Correct Data     Manage Changes to
                                                                                                                                     Quality Defects       Reference and Master
                                                                                                                                                                   Data


                                                                                                                                 Design and Implement
                                                                                                                                   Operational DQM
                                                                                                                                      Procedures


                                                                                                                                 Monitor Operational
                                                                                                                                 DQM Procedures and
                                                                                                                                    Performance
       March 8, 2010                                                                                                                                                                                                                            67
DMBOK Function and Activity - Planning Activities
                                                                                                        Data
                                                                                                     Management

                                                                                                                                          Reference and                                  Document and
                    Data Architecture                              Data Operations         Data Security           Data Quality                                    DW and BI                                     Metadata
Data Governance                            Data Development                                                                                Master Data                                      Content
                     Management                                     Management             Management              Management                                     Management                                    Management
                                                                                                                                          Management                                     Management
                                                                                                     Understand Data                                   Understand
                                   Understand           Data Modeling,                                                     Develop and Promote                            Understand Business                               Understand
          Data Management                                                                           Security Needs and                                Reference and                               Documents / Records
                                    Enterprise           Analysis, and        Database Support                                 Data Quality                                    Intelligence                                  Metadata
               Planning                                                                                 Regulatory                                     Master Data                                   Management
                               Information Needs        Solution Design                                                         Awareness                                  Information Needs                               Requirements
                                                                                                      Requirements                                  Integration Needs
                                  Develop and                                                                                                      Identify Master and
                                                                                                                                                                          Define and Maintain
          Data Management         Maintain the                                Data Technology       Define Data Security   Define Data Quality       Reference Data                                    Content          Define the Metadata
                                                      Detailed Data Design                                                                                                    the DW / BI
               Control           Enterprise Data                               Management                  Policy             Requirement              Sources and                                   Management             Architecture
                                                                                                                                                                              Architecture
                                     Model                                                                                                            Contributors

                                Analyse and Align       Data Model and                                                                             Define and Maintain      Implement Data                                Develop and
                                                                                                    Define Data Security   Profile, Analyse, and
                               With Other Business       Design Quality                                                                            the Data Integration     Warehouses and                              Maintain Metadata
                                                                                                         Standards         Assess Data Quality
                                     Models              Management                                                                                    Architecture           Data Marts                                    Standards

                                                                                                                                                   Implement Reference
                               Define and Maintain                                                  Define Data Security                                                                                                  Implement a
                                                                                                                           Define Data Quality       and Master Data      Implement BI Tools
                                  the Database        Data Implementation                               Controls and                                                                                                    Managed Metadata
                                                                                                                                 Metrics              Management          and User Interfaces
                                   Architecture                                                          Procedures                                                                                                       Environment
                                                                                                                                                        Solutions

                               Define and Maintain                                                    Manage Users,
                                                                                                                           Define Data Quality     Define and Maintain      Process Data for                            Create and Maintain
                               the Data Integration                                                   Passwords, and
                                                                                                                             Business Rules            Match Rules        Business Intelligence                              Metadata
                                   Architecture                                                     Group Membership


                               Define and Maintain                                                  Manage Data Access      Test and Validate                              Monitor and Tune
                                                                                                                                                    Establish “Golden”
                                   the DW / BI                                                          Views and             Data Quality                                 Data Warehousing                             Integrate Metadata
                                                                                                                                                         Records
                                   Architecture                                                        Permissions           Requirements                                      Processes

                               Define and Maintain
                                                                                                       Monitor User         Set and Evaluate       Define and Maintain    Monitor and Tune BI
                                    Enterprise                                                                                                                                                                           Manage Metadata
                                                                                                    Authentication and     Data Quality Service      Hierarchies and         Activity and
                                Taxonomies and                                                                                                                                                                             Repositories
                                                                                                     Access Behaviour            Levels                 Affiliations        Performance
                                   Namespaces

                               Define and Maintain                                                                            Continuously         Plan and Implement
                                                                                                    Classify Information                                                                                                  Distribute and
                                  the Metadata                                                                             Measure and Monitor     Integration of New
                                                                                                       Confidentiality                                                                                                   Deliver Metadata
                                   Architecture                                                                               Data Quality            Data Sources


                                                                                                                                                      Replicate and
                                                                                                                           Manage Data Quality                                                                          Query, Report, and
                                                                                                    Audit Data Security                            Distribute Reference
                                                                                                                                 Issues                                                                                 Analyse Metadata
                                                                                                                                                     and Master Data


                                                                                                                            Clean and Correct       Manage Changes to
                                                                                                                           Data Quality Defects      Reference and
                                                                                                                                                      Master Data

                                                                                                                              Design and
                                                                                                                              Implement
                                                                                                                            Operational DQM
                                                                                                                              Procedures

                                                                                                                           Monitor Operational
                                                                                                                           DQM Procedures and
                                                                                                                              Performance



     March 8, 2010                                                                                                                                                                                                                            68
DMBOK Function and Activity - Control Activities
                                                                                                             Data
                                                                                                          Management

                                                                                                                                                  Reference and                                   Document and
                     Data Architecture                                Data Operations          Data Security             Data Quality                                       DW and BI                                     Metadata
Data Governance                              Data Development                                                                                      Master Data                                       Content
                      Management                                       Management              Management                Management                                        Management                                    Management
                                                                                                                                                  Management                                      Management

                                                                                                          Understand Data
                                                         Data Modeling,                                                          Develop and Promote      Understand Reference    Understand Business
           Data Management     Understand Enterprise                                                     Security Needs and                                                                               Documents / Records   Understand Metadata
                                                       Analysis, and Solution    Database Support                                    Data Quality           and Master Data            Intelligence
               Planning         Information Needs                                                            Regulatory                                                                                      Management            Requirements
                                                              Design                                                                  Awareness            Integration Needs       Information Needs
                                                                                                           Requirements

                                                                                                                                                           Identify Master and
                                Develop and Maintain                                                                                                                              Define and Maintain
           Data Management                                                        Data Technology       Define Data Security      Define Data Quality        Reference Data                                                     Define the Metadata
                                 the Enterprise Data   Detailed Data Design                                                                                                           the DW / BI         Content Management
                Control                                                            Management                  Policy                Requirement               Sources and                                                          Architecture
                                       Model                                                                                                                                          Architecture
                                                                                                                                                              Contributors

                                 Analyse and Align       Data Model and                                                                                    Define and Maintain      Implement Data
                                                                                                        Define Data Security      Profile, Analyse, and                                                                         Develop and Maintain
                                With Other Business      Design Quality                                                                                    the Data Integration   Warehouses and Data
                                                                                                             Standards            Assess Data Quality                                                                            Metadata Standards
                                      Models              Management                                                                                           Architecture              Marts

                                                                                                                                                           Implement Reference
                                Define and Maintain                                                     Define Data Security                                                                                                    Implement a Managed
                                                                                                                                  Define Data Quality        and Master Data       Implement BI Tools
                                   the Database        Data Implementation                                  Controls and                                                                                                             Metadata
                                                                                                                                        Metrics               Management           and User Interfaces
                                    Architecture                                                             Procedures                                                                                                             Environment
                                                                                                                                                                Solutions

                                Define and Maintain                                                        Manage Users,
                                                                                                                                  Define Data Quality      Define and Maintain      Process Data for                            Create and Maintain
                                the Data Integration                                                    Passwords, and Group
                                                                                                                                    Business Rules             Match Rules        Business Intelligence                              Metadata
                                    Architecture                                                            Membership


                                Define and Maintain                                                                                                                                Monitor and Tune
                                                                                                        Manage Data Access       Test and Validate Data     Establish “Golden”
                                    the DW / BI                                                                                                                                    Data Warehousing                              Integrate Metadata
                                                                                                       Views and Permissions     Quality Requirements            Records
                                    Architecture                                                                                                                                       Processes


                                Define and Maintain                                                         Monitor User                                   Define and Maintain    Monitor and Tune BI
                                                                                                                                 Set and Evaluate Data                                                                           Manage Metadata
                               Enterprise Taxonomies                                                     Authentication and                                  Hierarchies and         Activity and
                                                                                                                                 Quality Service Levels                                                                            Repositories
                                  and Namespaces                                                          Access Behaviour                                      Affiliations        Performance


                                Define and Maintain                                                                              Continuously Measure      Plan and Implement
                                                                                                         Classify Information                                                                                                   Distribute and Deliver
                                   the Metadata                                                                                    and Monitor Data         Integration of New
                                                                                                            Confidentiality                                                                                                            Metadata
                                    Architecture                                                                                        Quality                Data Sources


                                                                                                                                                               Replicate and
                                                                                                                                 Manage Data Quality                                                                             Query, Report, and
                                                                                                         Audit Data Security                               Distribute Reference
                                                                                                                                       Issues                                                                                    Analyse Metadata
                                                                                                                                                             and Master Data


                                                                                                                                 Clean and Correct Data     Manage Changes to
                                                                                                                                     Quality Defects       Reference and Master
                                                                                                                                                                   Data


                                                                                                                                 Design and Implement
                                                                                                                                   Operational DQM
                                                                                                                                      Procedures


                                                                                                                                 Monitor Operational
                                                                                                                                 DQM Procedures and
                                                                                                                                    Performance
       March 8, 2010                                                                                                                                                                                                                            69
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)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
Data, Information And Knowledge Management Framework And The Data Management Book Of Knowledge (Dmbok)
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. Structured and Comprehensive Approach to Data Management and the Data Management Book of Knowledge (DMBOK) Alan McSweeney
  • 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. 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. Preamble • Every good presentation should start with quotations from The Prince and Dilbert March 8, 2010 4
  • 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. 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. 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. Data, Information, Knowledge and Action Knowledge Action Information Data March 8, 2010 8
  • 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. Data Management Issues March 8, 2010 23
  • 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. Other Data Management Frameworks March 8, 2010 40
  • 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. COBIT Structure COBIT Plan and Organise (PO) Acquire and Implement (AI) Deliver and Support (DS) Monitor and Evaluate (ME) DS1 Define and manage service ME1 Monitor and evaluate IT PO1 Define a strategic IT plan AI1 Identify automated solutions levels performance PO2 Define the information AI2 Acquire and maintain ME2 Monitor and evaluate DS2 Manage third-party services architecture application software internal control PO3 Determine technological AI3 Acquire and maintain DS3 Manage performance and ME3 Ensure regulatory direction technology infrastructure capacity compliance PO4 Define the IT processes, AI4 Enable operation and use DS4 Ensure continuous service ME4 Provide IT governance organisation and relationships PO5 Manage the IT investment AI5 Procure IT resources DS5 Ensure systems security PO6 Communicate management AI6 Manage changes DS6 Identify and allocate costs aims and direction AI7 Install and accredit solutions PO7 Manage IT human resources DS7 Educate and train users and changes DS8 Manage service desk and PO8 Manage quality incidents PO9 Assess and manage IT risks DS9 Manage the configuration PO10 Manage projects DS10 Manage problems DS11 Manage data DS12 Manage the physical environment DS13 Manage operations March 8, 2010 51
  • 52. COBIT and Data Management • COBIT objective DS11 Manage Data within the Deliver and Support (DS) domain • Effective data management requires identification of data requirements • Data management process includes establishing effective procedures to manage the media library, backup and recovery of data and proper disposal of media • Effective data management helps ensure the quality, timeliness and availability of business data March 8, 2010 52
  • 53. COBIT and Data Management • Objective is the control over the IT process of managing data that meets the business requirement for IT of optimising the use of information and ensuring information is available as required • Focuses on maintaining the completeness, accuracy, availability and protection of data • Involves taking actions − Backing up data and testing restoration − Managing onsite and offsite storage of data − Securely disposing of data and equipment • Measured by − User satisfaction with availability of data − Percent of successful data restorations − Number of incidents where sensitive data were retrieved after media were disposed of March 8, 2010 53
  • 54. COBIT Process DS11 Manage Data • DS11.1 Business Requirements for Data Management − Establish arrangements to ensure that source documents expected from the business are received, all data received from the business are processed, all output required by the business is prepared and delivered, and restart and reprocessing needs are supported • DS11.2 Storage and Retention Arrangements − Define and implement procedures for data storage and archival, so data remain accessible and usable − Procedures should consider retrieval requirements, cost-effectiveness, continued integrity and security requirements − Establish storage and retention arrangements to satisfy legal, regulatory and business requirements for documents, data, archives, programmes, reports and messages (incoming and outgoing) as well as the data (keys, certificates) used for their encryption and authentication • DS11.3 Media Library Management System − Define and implement procedures to maintain an inventory of onsite media and ensure their usability and integrity − Procedures should provide for timely review and follow-up on any discrepancies noted • DS11.4 Disposal − Define and implement procedures to prevent access to sensitive data and software from equipment or media when they are disposed of or transferred to another use − Procedures should ensure that data marked as deleted or to be disposed cannot be retrieved. • DS11.5 Backup and Restoration − Define and implement procedures for backup and restoration of systems, data and documentation in line with business requirements and the continuity plan − Verify compliance with the backup procedures, and verify the ability to and time required for successful and complete restoration − Test backup media and the restoration process • DS11.6 Security Requirements for Data Management − Establish arrangements to identify and apply security requirements applicable to the receipt, processing, physical storage and output of data and sensitive messages − Includes physical records, data transmissions and any data stored offsite March 8, 2010 54
  • 55. COBIT Data Management Goals and Metrics Activity Goals Process Goals Activity Goals •Backing up data and testing •Maintain the completeness, •Backing up data and testing restoration accuracy, validity and restoration •Managing onsite and offsite accessibility of stored data •Managing onsite and offsite storage of data •Secure data during disposal storage of data •Securely disposing of data of media •Securely disposing of data and equipment •Effectively manage storage and equipment media Are Measured Are Measured Are Measured By Drive By Drive By Key Performance Process Key Goal IT Key Goal Indicators Indicators Indicators •% of successful data •Occurrences of inability to restorations recover data critical to •Frequency of testing of •# of incidents where business process backup media sensitive data were retrieved •User satisfaction with •Average time for data after media were disposed of availability of data restoration •# of down time or data •Incidents of noncompliance integrity incidents caused by with laws due to storage insufficient storage capacity management issues March 8, 2010 55
  • 56. Data Management Book of Knowledge (DMBOK) March 8, 2010 56
  • 57. Data Management Book of Knowledge (DMBOK) • DMBOK is a generalised and comprehensive framework for managing data across the entire lifecycle • Developed by DAMA (Data Management Association) • DMBOK provides a detailed framework to assist development and implementation of data management processes and procedures and ensures all requirements are addressed • Enables effective and appropriate data management across the organisation • Provides awareness and visibility of data management issues and requirements March 8, 2010 57
  • 58. Data Management Book of Knowledge (DMBOK) • Not a solution to your data management needs • Framework and methodology for developing and implementing an appropriate solution • Generalised framework to be customised to meet specific needs • Provide a work breakdown structure for a data management project to allow the effort to be assessed • No magic bullet March 8, 2010 58
  • 59. Scope and Structure of Data Management Book of Knowledge (DMBOK) Data Management Environmental Elements Data Management Functions March 8, 2010 59
  • 60. DMBOK Data Management Functions Data Management Functions Data Governance Data Architecture Management Data Development Data Operations Management Data Security Management Data Quality Management Data Warehousing and Business Reference and Master Data Management Intelligence Management Document and Content Management Metadata Management March 8, 2010 60
  • 61. DMBOK Data Management Functions • Data Governance - planning, supervision and control over data management and use • Data Architecture Management - defining the blueprint for managing data assets • Data Development - analysis, design, implementation, testing, deployment, maintenance • Data Operations Management - providing support from data acquisition to purging • Data Security Management - Ensuring privacy, confidentiality and appropriate access • Data Quality Management - defining, monitoring and improving data quality • Reference and Master Data Management - managing master versions and replicas • Data Warehousing and Business Intelligence Management - enabling reporting and analysis • Document and Content Management - managing data found outside of databases • Metadata Management - integrating, controlling and providing metadata March 8, 2010 61
  • 62. DMBOK Data Management Environmental Elements Data Management Environmental Elements Goals and Principles Activities Primary Deliverables Roles and Responsibilities Practices and Techniques Technology Organisation and Culture March 8, 2010 62
  • 63. DMBOK Data Management Environmental Elements • Goals and Principles - directional business goals of each function and the fundamental principles that guide performance of each function • Activities - each function is composed of lower level activities, sub-activities, tasks and steps • Primary Deliverables - information and physical databases and documents created as interim and final outputs of each function. Some deliverables are essential, some are generally recommended, and others are optional depending on circumstances • Roles and Responsibilities - business and IT roles involved in performing and supervising the function, and the specific responsibilities of each role in that function. Many roles will participate in multiple functions • Practices and Techniques - common and popular methods and procedures used to perform the processes and produce the deliverables and may also include common conventions, best practice recommendations, and alternative approaches without elaboration • Technology - categories of supporting technology such as software tools, standards and protocols, product selection criteria and learning curves • Organisation and Culture – this can include issues such as management metrics, critical success factors, reporting structures, budgeting, resource allocation issues, expectations and attitudes, style, cultural, approach to change management March 8, 2010 63
  • 64. DMBOK Data Management Functions and Environmental Elements Goals and Activities Primary Roles and Practices and Technology Organisation Principles Deliverables Responsibilities Techniques and Culture Data Governance Data Architecture Management Data Development Data Operations Management Scope of Each Data Management Function Data Security Management Data Quality Management Reference and Master Data Management Data Warehousing and Business Intelligence Management Document and Content Management Metadata Management March 8, 2010 64
  • 65. Scope of Data Management Book of Knowledge (DMBOK) Data Management Framework • Hierarchy − Function • Activity − Sub-Activity (not in all cases) • Each activity is classified as one (or more) of: − Planning Activities (P) • Activities that set the strategic and tactical course for other data management activities • May be performed on a recurring basis − Development Activities (D) • Activities undertaken within implementation projects and recognised as part of the systems development lifecycle (SDLC), creating data deliverables through analysis, design, building, testing, preparation, and deployment − Control Activities (C) • Supervisory activities performed on an on-going basis − Operational Activities (O) • Service and support activities performed on an on- going basis March 8, 2010 65
  • 66. Activity Groups Within Functions • Activity groups are classifications of data management Planning Development activities Activities Activities • Use the activity groupings to define the scope of data management sub- projects and identify the appropriate tasks: Control Operational Activities − Analysis and design Activities − Implementation − Operational improvement − Management and administration March 8, 2010 66
  • 67. DMBOK Function and Activity Structure Data Management Reference and Document and Data Architecture Data Operations Data Security Data Quality DW and BI Metadata Data Governance Data Development Master Data Content Management Management Management Management Management Management Management Management Understand Data Data Modeling, Develop and Promote Understand Reference Understand Business Data Management Understand Enterprise Security Needs and Documents / Records Understand Metadata Analysis, and Solution Database Support Data Quality and Master Data Intelligence Planning Information Needs Regulatory Management Requirements Design Awareness Integration Needs Information Needs Requirements Identify Master and Develop and Maintain Define and Maintain Data Management Data Technology Define Data Security Define Data Quality Reference Data Define the Metadata the Enterprise Data Detailed Data Design the DW / BI Content Management Control Management Policy Requirement Sources and Architecture Model Architecture Contributors Analyse and Align Data Model and Define and Maintain Implement Data Define Data Security Profile, Analyse, and Develop and Maintain With Other Business Design Quality the Data Integration Warehouses and Data Standards Assess Data Quality Metadata Standards Models Management Architecture Marts Implement Reference Define and Maintain Define Data Security Implement a Managed Define Data Quality and Master Data Implement BI Tools the Database Data Implementation Controls and Metadata Metrics Management and User Interfaces Architecture Procedures Environment Solutions Define and Maintain Manage Users, Define Data Quality Define and Maintain Process Data for Create and Maintain the Data Integration Passwords, and Group Business Rules Match Rules Business Intelligence Metadata Architecture Membership Define and Maintain Monitor and Tune Manage Data Access Test and Validate Data Establish “Golden” the DW / BI Data Warehousing Integrate Metadata Views and Permissions Quality Requirements Records Architecture Processes Define and Maintain Monitor User Define and Maintain Monitor and Tune BI Set and Evaluate Data Manage Metadata Enterprise Taxonomies Authentication and Hierarchies and Activity and Quality Service Levels Repositories and Namespaces Access Behaviour Affiliations Performance Define and Maintain Continuously Measure Plan and Implement Classify Information Distribute and Deliver the Metadata and Monitor Data Integration of New Confidentiality Metadata Architecture Quality Data Sources Replicate and Manage Data Quality Query, Report, and Audit Data Security Distribute Reference Issues Analyse Metadata and Master Data Clean and Correct Data Manage Changes to Quality Defects Reference and Master Data Design and Implement Operational DQM Procedures Monitor Operational DQM Procedures and Performance March 8, 2010 67
  • 68. DMBOK Function and Activity - Planning Activities Data Management Reference and Document and Data Architecture Data Operations Data Security Data Quality DW and BI Metadata Data Governance Data Development Master Data Content Management Management Management Management Management Management Management Management Understand Data Understand Understand Data Modeling, Develop and Promote Understand Business Understand Data Management Security Needs and Reference and Documents / Records Enterprise Analysis, and Database Support Data Quality Intelligence Metadata Planning Regulatory Master Data Management Information Needs Solution Design Awareness Information Needs Requirements Requirements Integration Needs Develop and Identify Master and Define and Maintain Data Management Maintain the Data Technology Define Data Security Define Data Quality Reference Data Content Define the Metadata Detailed Data Design the DW / BI Control Enterprise Data Management Policy Requirement Sources and Management Architecture Architecture Model Contributors Analyse and Align Data Model and Define and Maintain Implement Data Develop and Define Data Security Profile, Analyse, and With Other Business Design Quality the Data Integration Warehouses and Maintain Metadata Standards Assess Data Quality Models Management Architecture Data Marts Standards Implement Reference Define and Maintain Define Data Security Implement a Define Data Quality and Master Data Implement BI Tools the Database Data Implementation Controls and Managed Metadata Metrics Management and User Interfaces Architecture Procedures Environment Solutions Define and Maintain Manage Users, Define Data Quality Define and Maintain Process Data for Create and Maintain the Data Integration Passwords, and Business Rules Match Rules Business Intelligence Metadata Architecture Group Membership Define and Maintain Manage Data Access Test and Validate Monitor and Tune Establish “Golden” the DW / BI Views and Data Quality Data Warehousing Integrate Metadata Records Architecture Permissions Requirements Processes Define and Maintain Monitor User Set and Evaluate Define and Maintain Monitor and Tune BI Enterprise Manage Metadata Authentication and Data Quality Service Hierarchies and Activity and Taxonomies and Repositories Access Behaviour Levels Affiliations Performance Namespaces Define and Maintain Continuously Plan and Implement Classify Information Distribute and the Metadata Measure and Monitor Integration of New Confidentiality Deliver Metadata Architecture Data Quality Data Sources Replicate and Manage Data Quality Query, Report, and Audit Data Security Distribute Reference Issues Analyse Metadata and Master Data Clean and Correct Manage Changes to Data Quality Defects Reference and Master Data Design and Implement Operational DQM Procedures Monitor Operational DQM Procedures and Performance March 8, 2010 68
  • 69. DMBOK Function and Activity - Control Activities Data Management Reference and Document and Data Architecture Data Operations Data Security Data Quality DW and BI Metadata Data Governance Data Development Master Data Content Management Management Management Management Management Management Management Management Understand Data Data Modeling, Develop and Promote Understand Reference Understand Business Data Management Understand Enterprise Security Needs and Documents / Records Understand Metadata Analysis, and Solution Database Support Data Quality and Master Data Intelligence Planning Information Needs Regulatory Management Requirements Design Awareness Integration Needs Information Needs Requirements Identify Master and Develop and Maintain Define and Maintain Data Management Data Technology Define Data Security Define Data Quality Reference Data Define the Metadata the Enterprise Data Detailed Data Design the DW / BI Content Management Control Management Policy Requirement Sources and Architecture Model Architecture Contributors Analyse and Align Data Model and Define and Maintain Implement Data Define Data Security Profile, Analyse, and Develop and Maintain With Other Business Design Quality the Data Integration Warehouses and Data Standards Assess Data Quality Metadata Standards Models Management Architecture Marts Implement Reference Define and Maintain Define Data Security Implement a Managed Define Data Quality and Master Data Implement BI Tools the Database Data Implementation Controls and Metadata Metrics Management and User Interfaces Architecture Procedures Environment Solutions Define and Maintain Manage Users, Define Data Quality Define and Maintain Process Data for Create and Maintain the Data Integration Passwords, and Group Business Rules Match Rules Business Intelligence Metadata Architecture Membership Define and Maintain Monitor and Tune Manage Data Access Test and Validate Data Establish “Golden” the DW / BI Data Warehousing Integrate Metadata Views and Permissions Quality Requirements Records Architecture Processes Define and Maintain Monitor User Define and Maintain Monitor and Tune BI Set and Evaluate Data Manage Metadata Enterprise Taxonomies Authentication and Hierarchies and Activity and Quality Service Levels Repositories and Namespaces Access Behaviour Affiliations Performance Define and Maintain Continuously Measure Plan and Implement Classify Information Distribute and Deliver the Metadata and Monitor Data Integration of New Confidentiality Metadata Architecture Quality Data Sources Replicate and Manage Data Quality Query, Report, and Audit Data Security Distribute Reference Issues Analyse Metadata and Master Data Clean and Correct Data Manage Changes to Quality Defects Reference and Master Data Design and Implement Operational DQM Procedures Monitor Operational DQM Procedures and Performance March 8, 2010 69