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Data Governance: Keystone
of Information Management
Initiatives


Alan McSweeney
Objectives

•   To provide an overview of the importance and relevance
    of data governance as part of an information management
    initiative




    April 21, 2010                                            2
Agenda

•   Data Management Issues
•   Data Governance and Data Management Frameworks
•   Approach to Data Governance
•   State of Information and Data Governance




    April 21, 2010                                   3
Data Governance

•   Provides an operating discipline for managing data and information as a key
    enterprise asset
•   Includes organisation, processes and tools for establishing and exercising decision
    rights regarding valuation and management of data
•   Elements of data governance
      −    Decision making authority
      −    Compliance
      −    Policies and standards
      −    Data inventories
      −    Full lifecycle management
      −    Content management
      −    Records management,
      −    Preservation and disposal
      −    Data quality
      −    Data classification
      −    Data security and access
      −    Data risk management
      −    Data valuation


    April 21, 2010                                                                        4
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




    April 21, 2010                                            5
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
    April 21, 2010                                      6
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

    April 21, 2010                                                    7
Information Management Issues

•   52% of users don’t have confidence in their information
•   59% of managers miss information they should have used
•   42% of managers use wrong information at least once a
    week
•   75% of CIOs believe they can strengthen their competitive
    advantage by better using and managing enterprise data
•   78% of CIOs want to improve the way they use and
    manage their data
•   Only 15% of CIOs believe that their data is currently
    comprehensively well managed
    April 21, 2010                                              8
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




    April 21, 2010                                                9
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

 April 21, 2010                                                                                    10
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

    April 21, 2010                                                         11
Data, Information, Knowledge and Action


 Knowledge                                Action




         Information
                                          Data


 April 21, 2010                                    12
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
•   Information is a high-value asset of any enterprise
•   What do you do when you have something valuable
      − Retain it
      − Protect it
      − Manage it


    April 21, 2010                                                           13
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


    April 21, 2010                                       14
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


    April 21, 2010                                                                                                          15
Generalised Information Management Lifecycle

•   Need to implement management frameworks and
    associated solutions to automate the information lifecycle

                     Data Governance
                       Framework


                                       Data Architecture to
                                        Implement Data
                                           Governance


                                                         Data Infrastructure to
                                                           Implement Data
                                                             Architecture


                                                                                  Data Operations to
                                                                                    Manage Data
                                                                                    Infrastructure
    April 21, 2010                                                                                     16
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

    April 21, 2010                                                                                                                                      17
Objectives of Implementing Solutions to Deliver
Generalised Information Management Lifecycle
•   Establish effective policies for lifecycle enterprise information management to
    control data growth and lower information management costs
•   Meet service level goals to ensure the timely completion of key business
    processes for mission-critical applications
•   Support appropriate data retention compliance initiatives and mitigate risk for
    compliance, audits and legal discovery requests
•   Support appropriate data retention compliance requirements and mitigate risk for
    compliance, audits and legal discovery requests that keep historical transaction
    records accessible until legal retention periods expire
•   Implement scalable archiving strategies that easily adapt to ongoing business
    requirements
•   Improve application portfolio management to decommission redundant
    applications and simplify the IT infrastructure
•   Manage application information growth and its impact on service levels,
    operational costs and risks as well as storage requirements
•   Manage data quality, consistency, security, privacy and accuracy

    April 21, 2010                                                                     18
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




    April 21, 2010                                            19
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

 April 21, 2010                                                                           20
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



    April 21, 2010                                                            21
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




    April 21, 2010                                                      22
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




    April 21, 2010                                           23
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


    April 21, 2010                                              24
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
    April 21, 2010                                               25
Scope of Complete Data Management Function
                                     Metadata         Data
                         Data       Management     Governance
                     Warehousing
                     and Business
                     Intelligence                                     Data
                     Management                                   Development




                                                                          Data
                     Data
                                                                        Security
                    Quality
                                                                       Management
                  Management




                         Data
                                                                  Reference and
                       Operations
                                                                   Master Data
                      Management
                                                                  Management

                                        Data       Document and
                                    Architecture      Content
                                    Management     Management

 April 21, 2010                                                                     26
Data Governance

•   Capstone of
    Data
    Management
                                           Data Governance
    initiatives
                                       Database Architecture Management


                            Data Warehousing and Business Intelligence Management


                       Data Quality Management                 Metadata Management


                      Data Security Management                      Data Development


                     Data Operations         Reference and Master       Document and Content
                      Management              Data Management               Management


    April 21, 2010                                                                             27
Objectives of Data Governance

•   Guide information management decision-making
•   Ensure information is consistently defined and well
    understood
•   Increase the use and trust of data as an organisation asset
•   Improve consistency of projects across the organisation
•   Ensure regulatory compliance
•   Eliminate data risks




    April 21, 2010                                                28
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

    April 21, 2010                                             29
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
    April 21, 2010                                                    30
Data Governance and Data Management
Frameworks




 April 21, 2010                       31
Data Governance and Data Management
Frameworks
•   DMBOK - Data Management Book of Knowledge
•   TOGAF - The Open Group Architecture Framework
•   COBIT - Control Objectives for Information and related
    Technology




    April 21, 2010                                           32
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


 April 21, 2010                                                                              33
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




 April 21, 2010                                                                             34
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
    April 21, 2010                                           35
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



    April 21, 2010                                            36
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

    April 21, 2010                                                                        37
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



 April 21, 2010                                                                                  38
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




    April 21, 2010                                             39
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
 April 21, 2010                                                                                                                                        40
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


    April 21, 2010                                                                        41
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




    April 21, 2010                                                         42
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


    April 21, 2010                                                         43
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
    April 21, 2010                                                                                        44
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

 April 21, 2010                                                                                                                                                                      45
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


    April 21, 2010                                              46
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


    April 21, 2010                                                                 47
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




    April 21, 2010                                                                                                                           48
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

 April 21, 2010                                                                                               49
Approach to Data Governance




 April 21, 2010               50
Data Governance

•   Core function of Data Management
•   Interacts with and influences each of the surrounding ten data
    management functions
•   Data governance is the exercise of authority and control (planning,
    monitoring, and enforcement) over the management of data assets
•   Data governance function guides how all other data management
    functions are performed
•   High-level, executive data stewardship
•   Data governance is not the same thing as IT governance
•   Data governance is focused exclusively on the management of data
    assets

    April 21, 2010                                                        51
Data Governance

•   Shared decision making is the hallmark of data governance
•   Requires working across organisational and system boundaries
•   Some decisions are primarily business decisions made with input and guidance from IT
•   Other decisions are primarily technical decisions made with input and guidance from
    business data stewards at all levels
             Decisions Made                                            Decisions Made
               by Business                                                  by IT
              Management                                                Management



          Business Operating    Enterprise Information   Information Management   Database Architecture
                Model                   Model                    Strategy

               IT Leadership     Information Needs       Information Management     Data Integration
                                                                  Policies           Architecture

          Capital Investments        Information         Information Management    Data Warehousing
                                    Specifications              Standards            Architecture
           Research and         Quality Requirements     Information Management   Metadata Architecture
        Development Funding                                       Metrics

      Data Governance Model        Issue Resolution      Information Management    Technical Metadata
                                                                 Services
    April 21, 2010                                                                                        52
Data Governance

•   Data governance is accomplished most effectively as an
    on-going program and a continual improvement process
•   Every effective data governance program is unique, taking
    into account distinctive organisational and cultural issues,
    and the immediate data management challenges and
    opportunities
•   Data governance is not the same thing as IT governance




    April 21, 2010                                                 53
Data Governance and IT Governance

•   IT Governance makes decisions about       •   Data Governance is focused
      − IT investments                            exclusively on the management of
      − IT application portfolio                  data assets
      − IT project portfolio                  •   Data Governance is at the heart of
•   IT Governance aligns the IT strategies        managing data assets
    and investments with enterprise goals
    and strategies
•   COBIT (Control Objectives for
    Information and related Technology)
    provides standards for IT governance
      − Only a small portion of the COBIT
        framework addresses managing
        information
•   Some critical issues, such as Sarbanes-
    Oxley compliance, span the concerns
    of corporate governance, IT
    governance, and data governance

    April 21, 2010                                                                     54
Data Governance – Definition and Goals

•   Definition
      − The exercise of authority and control (planning, monitoring, and
        enforcement) over the management of data assets
•   Goals
      − To define, approve, and communicate data strategies, policies,
        standards, architecture, procedures, and metrics
      − To track and enforce regulatory compliance and conformance to
        data policies, standards, architecture, and procedures
      − To sponsor, track, and oversee the delivery of data management
        projects and services
      − To manage and resolve data related issues
      − To understand and promote the value of data assets

    April 21, 2010                                                         55
Data Governance - Overview
                    Inputs                                     Primary Deliverables

•Business Goals                                          •Data Policies
•Business Strategies                                     •Data Standards
•IT Objectives                                           •Resolved Issues
•IT Strategies                                           •Data Management Projects and
•Data Needs                                              Services
•Data Issues                                             •Quality Data and Information
•Regulatory Requirements                                 •Recognised Data Value


                   Suppliers    Data Governance                     Consumers


•Business Executives                                     •Data Producers
•IT Executives                                           •Knowledge Workers
•Data Stewards                                           •Managers and Executives
•Regulatory Bodies                                       •Data Professionals
                                                         •Customers



               Participants                 Tools                     Metrics

•Executive Data Stewards       •Intranet Website         •Data Value
•Coordinating Data Stewards    •E-Mail                   •Data Management Cost
•Business Data Stewards        •Metadata Tools           •Achievement of Objectives
•Data Professionals            •Metadata Repository      •# of Decisions Made
•DM Executive                  •Issue Management Tools   •Steward Representation / Coverage
•CIO                           •Data Governance KPI      •Data Professional Headcount
                               •Dashboard                •Data Management Process Maturity

  April 21, 2010                                                                              56
Data Governance Function, Activities and Sub-
Activities
                                        Data Governance


       Data Management Planning                                   Data Management Control

                            Understand Strategic Enterprise Data                    Supervise Data Professional Organisations
                                          Needs                                                     and Staff

                           Develop and Maintain the Data Strategy                     Coordinate Data Governance Activities

                            Establish Data Professional Roles and
                                                                                     Manage and Resolve Data Related Issues
                                        Organisations

                             Identify and Appoint Data Stewards                     Monitor and Ensure Regulatory Compliance

                                  Establish Data Governance and                       Monitor and Enforce Conformance with
                                    Stewardship Organisations                        Data Policies, Standards and Architecture

                             Develop and Approve Data Policies,                      Oversee Data Management Projects and
                                 Standards, and Procedures                                         Services

                                                                                     Communicate and Promote the Value of
                           Review and Approve Data Architecture
                                                                                                 Data Assets

                            Plan and Sponsor Data Management
                                   Projects and Services

                          Estimate Data Asset Value and Associated
                                           Costs
 April 21, 2010                                                                                                                  57
Data Governance

•   Data governance is accomplished most effectively as an
    on-going program and a continual improvement process
•   Every data governance programme is unique, taking into
    account distinctive organisational and cultural issues, and
    the immediate data management challenges and
    opportunities
•   Data governance is at the core of managing data assets




    April 21, 2010                                                58
Data Governance - Possible Organisation Structure

                                                   Data Governance Structure



                         Organisation Data Governance
                                                                                      CIO
                                    Council



     Data Governance Office                       Data Management Executive



                         Business Unit Data Governance
                                                                               Data Technologists
                                    Councils



                         Data Stewardship Committees



                              Data Stewardship Teams

 April 21, 2010                                                                                     59
Data Governance Shared Decision Making
      Business Decisions                 Shared Decision Making             IT Decisions

                                                              Enterprise
     Business Operating        Enterprise                    Information       Database
           Model           Information Model                 Management       Architecture
                                                               Strategy
                                                              Enterprise
                           Information Needs                 Information    Data Integration
          IT Leadership                                      Management      Architecture
                                                               Policies
                                                              Enterprise   Data Warehousing
                              Information                    Information     and Business
    Capital Investments      Specifications                  Management       Intelligence
                                                              Standards      Architecture

         Research and                                         Enterprise
                                Quality                      Information       Metadata
         Development         Requirements                    Management       Architecture
           Funding                                             Metrics

                                                              Enterprise
      Data Governance       Issue Resolution                 Information   Technical Metadata
           Model                                             Management
                                                               Services


 April 21, 2010                                                                                 60
Data Stewardship

•   Formal accountability for business responsibilities ensuring effective
    control and use of data assets
•   Data steward is a business leader and/or recognised subject matter
    expert designated as accountable for these responsibilities
•   Manage data assets on behalf of others and in the best interests of
    the organisation
•   Represent the data interests of all stakeholders, including but not
    limited to, the interests of their own functional departments and
    divisions
•   Protects, manages, and leverages the data resources
•   Must take an enterprise perspective to ensure the quality and
    effective use of enterprise data

    April 21, 2010                                                           61
Data Stewardship - Roles

•   Executive Data Stewards – provide data governance and
    make of high-level data stewardship decisions
•   Coordinating Data Stewards - lead and represent teams of
    business data stewards in discussions across teams and
    with executive data stewards
•   Business Data Stewards - subject matter experts work
    with data management professionals on an ongoing basis
    to define and control data




    April 21, 2010                                             62
Data Stewardship Roles Across Data Management
Functions - 1
                            All Data Stewards            Executive Data Stewards   Coordinating Data           Business Data Stewards
                                                                                   Stewards
Data Architecture           Review, validate, approve,   Review and approve the    Integrate specifications,   Define data requirements
Management                  maintain and refine data     enterprise data           resolving differences       specifications
                            architecture                 architecture
Data Development            Validate physical data                                                             Define data requirements
                            models and database                                                                and specifications
                            designs, participate in
                            database testing and
                            conversion
Data Operations                                                                                                Define requirements for
Management                                                                                                     data recovery, retention
                                                                                                               and performance
                                                                                                               Help identify, acquire, and
                                                                                                               control externally sourced
                                                                                                               data
Data Security Management                                                                                       Provide security, privacy
                                                                                                               and confidentiality
                                                                                                               requirements, identify and
                                                                                                               resolve data security
                                                                                                               issues, assist in data
                                                                                                               security audits, and classify
                                                                                                               information confidentiality
Reference and Master Data                                                                                      Control the creation,
Management                                                                                                     update, and retirement of
                                                                                                               code values and other
                                                                                                               reference data, define
                                                                                                               master data management
                                                                                                               requirements, identify and
                                                                                                               help resolve issues

    April 21, 2010                                                                                                                             63
Data Stewardship Roles Across Data Management
Functions - 2
                          All Data Stewards            Executive Data Stewards   Coordinating Data   Business Data Stewards
                                                                                 Stewards
Data Warehousing and                                                                                 Provide business
Business Intelligence                                                                                intelligence requirements
Management                                                                                           and management metrics,
                                                                                                     and they identify and help
                                                                                                     resolve business
                                                                                                     intelligence issues
Document and Content                                                                                 Define enterprise
Management                                                                                           taxonomies and resolve
                                                                                                     content management
                                                                                                     issues
Metadata Management       Create and maintain
                          business metadata (names,
                          meanings, business rules),
                          define metadata access
                          and integration needs and
                          use metadata to make
                          effective data stewardship
                          and governance decisions
Data Quality Management                                                                              Define data quality
                                                                                                     requirements and business
                                                                                                     rules, test application edits
                                                                                                     and validations, assist in
                                                                                                     the analysis, certification,
                                                                                                     and auditing of data
                                                                                                     quality, lead clean-up
                                                                                                     efforts, identify ways to
                                                                                                     solve causes of poor data
                                                                                                     quality, promote data
                                                                                                     quality awareness
   April 21, 2010                                                                                                                    64
Data Strategy

•   High-level course of action to achieve high-level goals
•   Data strategy is a data management program strategy a
    plan for maintaining and improving data quality, integrity,
    security and access
•   Address all data management functions relevant to the
    organisation




    April 21, 2010                                                65
Elements of Data Strategy

•   Vision for data management
•   Summary business case for data management
•   Guiding principles, values, and management perspectives
•   Mission and long-term directional goals of data management
•   Management measures of data management success
•   Short-term data management programme objectives
•   Descriptions of data management roles and business units along
    with a summary of their responsibilities and decision rights
•   Descriptions of data management programme components and
    initiatives
•   Outline of the data management implementation roadmap
•   Scope boundaries
    April 21, 2010                                                   66
Data Strategy




                                     Data Management
                                     Programme Charter
      Data Management                                                    Data Management
       Scope Statement               Overall vision, business case,
                                       goals, guiding principles,         Implementation
                                      measures of success, critical          Roadmap
     Goals and objectives for a     success factors, recognised risks
 defined planning horizon and the
                                                                         Identifying specific programs,
      roles, organisations, and
                                                                        projects, task assignments, and
  individual leaders accountable
                                                                              delivery milestones
   for achieving these objectives




 April 21, 2010                                                                                           67
Data Policies

•   Statements of intent and fundamental rules governing the
    creation, acquisition, integrity, security, quality, and use of
    data and information
•   More fundamental, global, and business critical than data
    standards
•   Describe what to do and what not to do
•   Should be few data policies stated briefly and directly




    April 21, 2010                                                    68
Data Policies

•   Possible topics for data policies
      − Data modeling and other data development activities
      − Development and use of data architecture
      − Data quality expectations, roles, and responsibilities
      − Data security, including confidentiality classification policies,
        intellectual property policies, personal data privacy policies,
        general data access and usage policies, and data access by
        external parties
      − Database recovery and data retention
      − Access and use of externally sourced data
      − Sharing data internally and externally
      − Data warehousing and business intelligence
      − Unstructured data - electronic files and physical records

    April 21, 2010                                                          69
Data Architecture

•   Enterprise data model and other aspects of data
    architecture sponsored at the data governance level
•   Need to pay particular attention to the alignment of the
    enterprise data model with key business strategies,
    processes, business units and systems
•   Includes
      − Data technology architecture
      − Data integration architecture
      − Data warehousing and business intelligence architecture
      − Metadata architecture


    April 21, 2010                                                70
Data Standards and Procedures

•   Include naming standards, requirement specification
    standards, data modeling standards, database design
    standards, architecture standards and procedural
    standards for each data management function
•   Must be effectively communicated, monitored, enforced
    and periodically re-evaluated
•   Data management procedures are the methods,
    techniques, and steps followed to accomplish a specific
    activity or task



    April 21, 2010                                            71
Data Standards and Procedures

•   Possible topics for data standards and procedures
      − Data modeling and architecture standards, including data naming conventions,
        definition standards, standard domains, and standard abbreviations
      − Standard business and technical metadata to be captured, maintained, and
        integrated
      − Data model management guidelines and procedures
      − Metadata integration and usage procedures
      − Standards for database recovery and business continuity, database
        performance, data retention, and external data acquisition
      − Data security standards and procedures
      − Reference data management control procedures
      − Match / merge and data cleansing standards and procedures
      − Business intelligence standards and procedures
      − Enterprise content management standards and procedures, including use of
        enterprise taxonomies, support for legal discovery and document and e-mail
        retention, electronic signatures, report formatting standards and report
        distribution approaches

    April 21, 2010                                                                     72
Regulatory Compliance

•   Most organisations are is impacted by government and
    industry regulations
•   Many of these regulations dictate how data and
    information is to be managed
•   Compliance is generally mandatory
•   Data governance guides the implementation of adequate
    controls to ensure, document, and monitor compliance
    with data-related regulations.




    April 21, 2010                                          73
Regulatory Compliance

•   Data governance needs to work the business to find the best
    answers to the following regulatory compliance questions
      −    How relevant is a regulation?
      −    Why is it important for us?
      −    How do we interpret it?
      −    What policies and procedures does it require?
      −    Do we comply now?
      −    How do we comply now?
      −    How should we comply in the future?
      −    What will it take?
      −    When will we comply?
      −    How do we demonstrate and prove compliance?
      −    How do we monitor compliance?
      −    How often do we review compliance?
      −    How do we identify and report non-compliance?
      −    How do we manage and rectify non-compliance?
    April 21, 2010                                                74
Issue Management

•   Data governance assists in identifying, managing, and resolving data
    related issues
      −    Data quality issues
      −    Data naming and definition conflicts
      −    Business rule conflicts and clarifications
      −    Data security, privacy, and confidentiality issues
      −    Regulatory non-compliance issues
      −    Non-conformance issues (policies, standards, architecture, and procedures)
      −    Conflicting policies, standards, architecture, and procedures
      −    Conflicting stakeholder interests in data and information
      −    Organisational and cultural change management issues
      −    Issues regarding data governance procedures and decision rights
      −    Negotiation and review of data sharing agreements


    April 21, 2010                                                                      75
Issue Management, Control and Escalation

•   Data governance implements issue controls and
    procedures
      − Identifying, capturing, logging and updating issues
      − Tracking the status of issues
      − Documenting stakeholder viewpoints and resolution alternatives
      − Objective, neutral discussions where all viewpoints are heard
      − Escalating issues to higher levels of authority
      − Determining, documenting and communicating issue resolutions.




    April 21, 2010                                                       76
Data Management Projects

•   Data management roadmap sets out a course of action for
    initiating and/or improving data management functions
•   Consists of an assessment of current functions, definition
    of a target environment and target objectives and a
    transition plan outlining the steps required to reach these
    targets including an approach to organisational change
    management
•   Every data management project should follow the project
    management standards of the organisation



    April 21, 2010                                                77
Data Asset Valuation

•   Data and information are truly assets because they have
    business value, tangible or intangible
•   Different approaches to estimating the value of data assets
•   Identify the direct and indirect business benefits derived
    from use of the data
•   Identify the cost of data loss, identifying the impacts of not
    having the current amount and quality level of data




    April 21, 2010                                                   78
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)
•   Ponemon Institute 2009 Annual Study Cost of a Data
    Breach




    April 21, 2010                                                           79
Terms Used by Organisations to Describe the
Activities Associated with Governing Data
                    Data Management                                                           62.7%


                      Data Governance                                                 55.4%


                     Data Stewardship                                         46.6%


          Information Management                                            43.6%


            Information Governance                      17.2%

                        Data Resource
                                                10.8%
                        Management

           Information Stew ardship             10.3%

                  Information Resource
                                                10.3%
                      Management

                                Other               13.7%


                                         0%   10%       20%     30%   40%      50%      60%       70%

 April 21, 2010                                                                                         80
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%



 April 21, 2010                                                                          81
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%


 April 21, 2010                                                                                             82
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%



 April 21, 2010                                                                                        83
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%



 April 21, 2010                                                                                    84
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%

 April 21, 2010                                                                                                                85
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%
 April 21, 2010                                                                                 86
Focus of Information / Data Governance Efforts
                               Customers                                                                  70.2%

                                Financials                                                      57.6%

                  Products and Production                                               46.6%

                                 Services                                            41.9%

                                    Sales                                     35.6%

                               Employees                                  31.4%

      Supply Chain, Vendors, Suppliers                             25.1%

                        Items / Materials                       20.4%

                  Equipment and Facilities                16.2%

                             Maintenance                13.1%

       Environment, Health and Safety               10.5%

                                    Other          9.5%

                                             0%   10%       20%         30%    40%      50%     60%     70%       80%

 April 21, 2010                                                                                                         87
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
                                                                                                                 %
 April 21, 2010                                                                                                         88
Primary Activities of Organisation's Information /
Data Governance Efforts
                                             Standardise Data Definitions Across The Organisation                                                                          70.5%

       Provide Common Information Strategies, Processes, Policies, And Standards On Behalf Of The
                                            Organisation
                                                                                                                                                                 61.6%

                                     Support Data Warehouse And Business Intelligence Initiatives                                                          58.4%

                           Define And Standardise Common Business Rules Across The Organisation                                                       53.7%

                                    Select And Charter Specific Data Quality Improvement Projects                                                  49.5%

  Provide Oversight And Enforcement Of Data Standards On Every Project That Involves Information
                                    Systems And Technology
                                                                                                                                              47.9%

      Establish A Common Vocabulary And Culture Around The Deployment Of Data That Ensures Its
                                                                                                                                             46.8%
                                 Privacy, Compliance, And Security
      Support The Access And Use Of Common Corporate Data Through A Focus On Architecture And
                                            Integration
                                                                                                                                             45.8%

                                   Support The Development Of An Enterprise Logical Data Model                                             43.7%

                                             Guide The Management Of Master Or Reference Data                                             42.6%

   Support Information Management Problem-Solving And Decision-Making And Providing Processes
                                    For Strategic Alignment.
                                                                                                                                     40.0%

                                                                   Manage Information Products                              27.9%

                                                          Measure The Costs Of Low Quality Data                          25.3%

                                                          Measure The Value Of High Quality Data                        23.2%

                                              Implement Internal Information Chain Management                  13.2%

                                                  Implement External Data Supplier Management             10.0%

                                                    Implement Information Product Management              10.0%

                                                                                           Other          10.0%

                                                                                                    0%   10%      20%      30%      40%       50%          60%           70%       80%

 April 21, 2010                                                                                                                                                                          89
Primary Drivers for Organisation's Information /
Data Governance Efforts
            General Desire To Improve The Quality Of Our Data                                                                                      65.6%


                       Data Warehousing / Business Intelligence                                                                            57.7%


                                               Compliance / Risk                                                                   46.6%


                                         Enterprise Architecture                                                     33.3%


                                   Information Security / Privacy                                                   32.3%


                       Master Data Management (MDM) Project                                                        31.2%


                              Applications / Systems Integration                                                 30.2%


                         Customer Data Integration (CDI) Project                                         25.9%


        Suffered Major Negative Impact From Bad Data Quality                                         22.2%


                     Service-Oriented Architecture (SOA) Project                               18.0%


                      Enterprise Resource Planning (ERP) Project                           16.4%


          Merger And Acquisition Planning Or Implementation                            12.7%


                  Product Information Management (PIM) Project                    10.1%


                               Reaction To Competitors' Activity         3.7%


                                                           Other                8.5%


                                                                    0%          10%            20%           30%             40%    50%    60%       70%   80%

 April 21, 2010                                                                                                                                                  90
Category of Tools Currently Used in Organisation
          Data Quality Analysis, Assessment Or
                                                                                                                                 66.3%
                        Profiling
        Extract-Transform-Load (ETL) And Other
                                                                                                                         57.2%
                 Data Integration Tools
     Data Modeling (Computer-Aided Software
                                                                                                                 48.7%
                  Engineering)
       Data Matching And Reconciliation (Data
                                                                                                                 48.7%
                  De-Duplication)

                      Data Quality Monitoring                                                             45.5%


                         Metadata Repository                                                             44.4%


            Data Remediation / Cleansing Tools                                                   39.0%


    Data Relationship Discovery And Mappings                                             28.9%


                               Workflow Tools                                        25.7%


                       Business Rules Engines                                20.3%


       Master Data Management (MDM) Tools                                  18.7%


         Customer Data Integration (CDI) Tools                     13.4%

       Product Information Management (PIM)
                                                        5.9%
                        Tools

                         Rules Discovery Tools        4.3%


                                        Other           5.9%


                                                 0%          10%           20%          30%      40%             50%      60%      70%   80%

 April 21, 2010                                                                                                                                91
Functional Area to Which the Leader of the Organisation's
 Information / Data Governance Effort Reports

              Information Technology                                                                                43.1%


Senior / Executive Management Team                                                              31.0%


                               Finance                                     17.2%


                      Compliance / Risk                       8.6%


         Operations / Manufacturing                           8.6%


                            Marketing                  5.2%


                            Purchasing         1.7%


                                  Legal        1.7%


                                 Other                        8.6%


                                          0%          5%      10%    15%      20%   25%   30%           35%   40%    45%    50%




     April 21, 2010                                                                                                         92
Number of Levels Between the Organisation's Most Senior
Leader and the Person Most Directly in Charge of the
Information / Data Governance Effort

           5 Levels or More                             12.3%



                    4 Levels                                    14.0%



                    3 Levels                                                            26.3%



                    2 Levels                                                  22.8%



                     1 Level                                    14.0%



They are the Same Person            3.5%



                  Don't Know               7.0%


                               0%     5%          10%           15%     20%       25%           30%

 April 21, 2010                                                                                 93
Membership of Senior Information / Data
Governance Body within an Organisation
      The Senior / Executive Management Team is the Top
                                                                                   21.4%
             Information / Data Governance Body

                                   C-Level non-IT Executives                           26.8%


                                       C-Level IT Executives                           26.8%


                              Middle-Level non-IT Managers                                                      51.8%


                                  Middle-Level IT Managers                                       33.9%


                   Junior-Level non-IT Supervisors/Managers         7.1%


                      Junior-Level IT Supervisors / Managers               14.3%

My Organisation Does Not Have any Governance Body for
                                                                    7.1%
             Information and Data Assets

                                                               0%   10%      20%           30%      40%   50%           60%
  April 21, 2010                                                                                                        94
Relationship Between Information / Data
Governance and Data Quality Leadership
Information Governance and Data Quality Are Led by the Same
                                                                                              36.8%
                         Person



Information Governance and Data Quality Are Led by Different
                                                                               17.5%
          People Who Report to the Same Manager



Information Governance and Data Quality Are Led by Different
                                                                                19.3%
         People Who Report to Different Managers



  There is No Specific Individual in Charge of Our Data Quality
                                                                               17.5%
                             Program



                                                        Other           8.8%



                                                                  0%   10%     20%      30%   40%     50%   60%

   April 21, 2010                                                                                             95
Data Governance: Keystone of Information Management Initiatives
Data Governance: Keystone of Information Management Initiatives
Data Governance: Keystone of Information Management Initiatives
Data Governance: Keystone of Information Management Initiatives
Data Governance: Keystone of Information Management Initiatives
Data Governance: Keystone of Information Management Initiatives
Data Governance: Keystone of Information Management Initiatives

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Data Governance: Keystone of Information Management Initiatives

  • 1. Data Governance: Keystone of Information Management Initiatives Alan McSweeney
  • 2. Objectives • To provide an overview of the importance and relevance of data governance as part of an information management initiative April 21, 2010 2
  • 3. Agenda • Data Management Issues • Data Governance and Data Management Frameworks • Approach to Data Governance • State of Information and Data Governance April 21, 2010 3
  • 4. Data Governance • Provides an operating discipline for managing data and information as a key enterprise asset • Includes organisation, processes and tools for establishing and exercising decision rights regarding valuation and management of data • Elements of data governance − Decision making authority − Compliance − Policies and standards − Data inventories − Full lifecycle management − Content management − Records management, − Preservation and disposal − Data quality − Data classification − Data security and access − Data risk management − Data valuation April 21, 2010 4
  • 5. 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 April 21, 2010 5
  • 6. 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 April 21, 2010 6
  • 7. 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 April 21, 2010 7
  • 8. Information Management Issues • 52% of users don’t have confidence in their information • 59% of managers miss information they should have used • 42% of managers use wrong information at least once a week • 75% of CIOs believe they can strengthen their competitive advantage by better using and managing enterprise data • 78% of CIOs want to improve the way they use and manage their data • Only 15% of CIOs believe that their data is currently comprehensively well managed April 21, 2010 8
  • 9. 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 April 21, 2010 9
  • 10. 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 April 21, 2010 10
  • 11. 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 April 21, 2010 11
  • 12. Data, Information, Knowledge and Action Knowledge Action Information Data April 21, 2010 12
  • 13. 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 • Information is a high-value asset of any enterprise • What do you do when you have something valuable − Retain it − Protect it − Manage it April 21, 2010 13
  • 14. 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 April 21, 2010 14
  • 15. 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 April 21, 2010 15
  • 16. Generalised Information Management Lifecycle • Need to implement management frameworks and associated solutions to automate the information lifecycle Data Governance Framework Data Architecture to Implement Data Governance Data Infrastructure to Implement Data Architecture Data Operations to Manage Data Infrastructure April 21, 2010 16
  • 17. 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 April 21, 2010 17
  • 18. Objectives of Implementing Solutions to Deliver Generalised Information Management Lifecycle • Establish effective policies for lifecycle enterprise information management to control data growth and lower information management costs • Meet service level goals to ensure the timely completion of key business processes for mission-critical applications • Support appropriate data retention compliance initiatives and mitigate risk for compliance, audits and legal discovery requests • Support appropriate data retention compliance requirements and mitigate risk for compliance, audits and legal discovery requests that keep historical transaction records accessible until legal retention periods expire • Implement scalable archiving strategies that easily adapt to ongoing business requirements • Improve application portfolio management to decommission redundant applications and simplify the IT infrastructure • Manage application information growth and its impact on service levels, operational costs and risks as well as storage requirements • Manage data quality, consistency, security, privacy and accuracy April 21, 2010 18
  • 19. 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 April 21, 2010 19
  • 20. 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 April 21, 2010 20
  • 21. 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 April 21, 2010 21
  • 22. 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 April 21, 2010 22
  • 23. 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 April 21, 2010 23
  • 24. 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 April 21, 2010 24
  • 25. 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 April 21, 2010 25
  • 26. Scope of Complete Data Management Function Metadata Data Data Management Governance Warehousing and Business Intelligence Data Management Development Data Data Security Quality Management Management Data Reference and Operations Master Data Management Management Data Document and Architecture Content Management Management April 21, 2010 26
  • 27. Data Governance • Capstone of Data Management Data Governance initiatives Database Architecture Management Data Warehousing and Business Intelligence Management Data Quality Management Metadata Management Data Security Management Data Development Data Operations Reference and Master Document and Content Management Data Management Management April 21, 2010 27
  • 28. Objectives of Data Governance • Guide information management decision-making • Ensure information is consistently defined and well understood • Increase the use and trust of data as an organisation asset • Improve consistency of projects across the organisation • Ensure regulatory compliance • Eliminate data risks April 21, 2010 28
  • 29. 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 April 21, 2010 29
  • 30. 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 April 21, 2010 30
  • 31. Data Governance and Data Management Frameworks April 21, 2010 31
  • 32. Data Governance and Data Management Frameworks • DMBOK - Data Management Book of Knowledge • TOGAF - The Open Group Architecture Framework • COBIT - Control Objectives for Information and related Technology April 21, 2010 32
  • 33. 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 April 21, 2010 33
  • 34. 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 April 21, 2010 34
  • 35. 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 April 21, 2010 35
  • 36. 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 April 21, 2010 36
  • 37. 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 April 21, 2010 37
  • 38. 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 April 21, 2010 38
  • 39. 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 April 21, 2010 39
  • 40. 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 April 21, 2010 40
  • 41. 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 April 21, 2010 41
  • 42. 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 April 21, 2010 42
  • 43. 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 April 21, 2010 43
  • 44. 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 April 21, 2010 44
  • 45. 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 April 21, 2010 45
  • 46. 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 April 21, 2010 46
  • 47. 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 April 21, 2010 47
  • 48. 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 April 21, 2010 48
  • 49. 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 April 21, 2010 49
  • 50. Approach to Data Governance April 21, 2010 50
  • 51. Data Governance • Core function of Data Management • Interacts with and influences each of the surrounding ten data management functions • Data governance is the exercise of authority and control (planning, monitoring, and enforcement) over the management of data assets • Data governance function guides how all other data management functions are performed • High-level, executive data stewardship • Data governance is not the same thing as IT governance • Data governance is focused exclusively on the management of data assets April 21, 2010 51
  • 52. Data Governance • Shared decision making is the hallmark of data governance • Requires working across organisational and system boundaries • Some decisions are primarily business decisions made with input and guidance from IT • Other decisions are primarily technical decisions made with input and guidance from business data stewards at all levels Decisions Made Decisions Made by Business by IT Management Management Business Operating Enterprise Information Information Management Database Architecture Model Model Strategy IT Leadership Information Needs Information Management Data Integration Policies Architecture Capital Investments Information Information Management Data Warehousing Specifications Standards Architecture Research and Quality Requirements Information Management Metadata Architecture Development Funding Metrics Data Governance Model Issue Resolution Information Management Technical Metadata Services April 21, 2010 52
  • 53. Data Governance • Data governance is accomplished most effectively as an on-going program and a continual improvement process • Every effective data governance program is unique, taking into account distinctive organisational and cultural issues, and the immediate data management challenges and opportunities • Data governance is not the same thing as IT governance April 21, 2010 53
  • 54. Data Governance and IT Governance • IT Governance makes decisions about • Data Governance is focused − IT investments exclusively on the management of − IT application portfolio data assets − IT project portfolio • Data Governance is at the heart of • IT Governance aligns the IT strategies managing data assets and investments with enterprise goals and strategies • COBIT (Control Objectives for Information and related Technology) provides standards for IT governance − Only a small portion of the COBIT framework addresses managing information • Some critical issues, such as Sarbanes- Oxley compliance, span the concerns of corporate governance, IT governance, and data governance April 21, 2010 54
  • 55. Data Governance – Definition and Goals • Definition − The exercise of authority and control (planning, monitoring, and enforcement) over the management of data assets • Goals − To define, approve, and communicate data strategies, policies, standards, architecture, procedures, and metrics − To track and enforce regulatory compliance and conformance to data policies, standards, architecture, and procedures − To sponsor, track, and oversee the delivery of data management projects and services − To manage and resolve data related issues − To understand and promote the value of data assets April 21, 2010 55
  • 56. Data Governance - Overview Inputs Primary Deliverables •Business Goals •Data Policies •Business Strategies •Data Standards •IT Objectives •Resolved Issues •IT Strategies •Data Management Projects and •Data Needs Services •Data Issues •Quality Data and Information •Regulatory Requirements •Recognised Data Value Suppliers Data Governance Consumers •Business Executives •Data Producers •IT Executives •Knowledge Workers •Data Stewards •Managers and Executives •Regulatory Bodies •Data Professionals •Customers Participants Tools Metrics •Executive Data Stewards •Intranet Website •Data Value •Coordinating Data Stewards •E-Mail •Data Management Cost •Business Data Stewards •Metadata Tools •Achievement of Objectives •Data Professionals •Metadata Repository •# of Decisions Made •DM Executive •Issue Management Tools •Steward Representation / Coverage •CIO •Data Governance KPI •Data Professional Headcount •Dashboard •Data Management Process Maturity April 21, 2010 56
  • 57. Data Governance Function, Activities and Sub- Activities Data Governance Data Management Planning Data Management Control Understand Strategic Enterprise Data Supervise Data Professional Organisations Needs and Staff Develop and Maintain the Data Strategy Coordinate Data Governance Activities Establish Data Professional Roles and Manage and Resolve Data Related Issues Organisations Identify and Appoint Data Stewards Monitor and Ensure Regulatory Compliance Establish Data Governance and Monitor and Enforce Conformance with Stewardship Organisations Data Policies, Standards and Architecture Develop and Approve Data Policies, Oversee Data Management Projects and Standards, and Procedures Services Communicate and Promote the Value of Review and Approve Data Architecture Data Assets Plan and Sponsor Data Management Projects and Services Estimate Data Asset Value and Associated Costs April 21, 2010 57
  • 58. Data Governance • Data governance is accomplished most effectively as an on-going program and a continual improvement process • Every data governance programme is unique, taking into account distinctive organisational and cultural issues, and the immediate data management challenges and opportunities • Data governance is at the core of managing data assets April 21, 2010 58
  • 59. Data Governance - Possible Organisation Structure Data Governance Structure Organisation Data Governance CIO Council Data Governance Office Data Management Executive Business Unit Data Governance Data Technologists Councils Data Stewardship Committees Data Stewardship Teams April 21, 2010 59
  • 60. Data Governance Shared Decision Making Business Decisions Shared Decision Making IT Decisions Enterprise Business Operating Enterprise Information Database Model Information Model Management Architecture Strategy Enterprise Information Needs Information Data Integration IT Leadership Management Architecture Policies Enterprise Data Warehousing Information Information and Business Capital Investments Specifications Management Intelligence Standards Architecture Research and Enterprise Quality Information Metadata Development Requirements Management Architecture Funding Metrics Enterprise Data Governance Issue Resolution Information Technical Metadata Model Management Services April 21, 2010 60
  • 61. Data Stewardship • Formal accountability for business responsibilities ensuring effective control and use of data assets • Data steward is a business leader and/or recognised subject matter expert designated as accountable for these responsibilities • Manage data assets on behalf of others and in the best interests of the organisation • Represent the data interests of all stakeholders, including but not limited to, the interests of their own functional departments and divisions • Protects, manages, and leverages the data resources • Must take an enterprise perspective to ensure the quality and effective use of enterprise data April 21, 2010 61
  • 62. Data Stewardship - Roles • Executive Data Stewards – provide data governance and make of high-level data stewardship decisions • Coordinating Data Stewards - lead and represent teams of business data stewards in discussions across teams and with executive data stewards • Business Data Stewards - subject matter experts work with data management professionals on an ongoing basis to define and control data April 21, 2010 62
  • 63. Data Stewardship Roles Across Data Management Functions - 1 All Data Stewards Executive Data Stewards Coordinating Data Business Data Stewards Stewards Data Architecture Review, validate, approve, Review and approve the Integrate specifications, Define data requirements Management maintain and refine data enterprise data resolving differences specifications architecture architecture Data Development Validate physical data Define data requirements models and database and specifications designs, participate in database testing and conversion Data Operations Define requirements for Management data recovery, retention and performance Help identify, acquire, and control externally sourced data Data Security Management Provide security, privacy and confidentiality requirements, identify and resolve data security issues, assist in data security audits, and classify information confidentiality Reference and Master Data Control the creation, Management update, and retirement of code values and other reference data, define master data management requirements, identify and help resolve issues April 21, 2010 63
  • 64. Data Stewardship Roles Across Data Management Functions - 2 All Data Stewards Executive Data Stewards Coordinating Data Business Data Stewards Stewards Data Warehousing and Provide business Business Intelligence intelligence requirements Management and management metrics, and they identify and help resolve business intelligence issues Document and Content Define enterprise Management taxonomies and resolve content management issues Metadata Management Create and maintain business metadata (names, meanings, business rules), define metadata access and integration needs and use metadata to make effective data stewardship and governance decisions Data Quality Management Define data quality requirements and business rules, test application edits and validations, assist in the analysis, certification, and auditing of data quality, lead clean-up efforts, identify ways to solve causes of poor data quality, promote data quality awareness April 21, 2010 64
  • 65. Data Strategy • High-level course of action to achieve high-level goals • Data strategy is a data management program strategy a plan for maintaining and improving data quality, integrity, security and access • Address all data management functions relevant to the organisation April 21, 2010 65
  • 66. Elements of Data Strategy • Vision for data management • Summary business case for data management • Guiding principles, values, and management perspectives • Mission and long-term directional goals of data management • Management measures of data management success • Short-term data management programme objectives • Descriptions of data management roles and business units along with a summary of their responsibilities and decision rights • Descriptions of data management programme components and initiatives • Outline of the data management implementation roadmap • Scope boundaries April 21, 2010 66
  • 67. Data Strategy Data Management Programme Charter Data Management Data Management Scope Statement Overall vision, business case, goals, guiding principles, Implementation measures of success, critical Roadmap Goals and objectives for a success factors, recognised risks defined planning horizon and the Identifying specific programs, roles, organisations, and projects, task assignments, and individual leaders accountable delivery milestones for achieving these objectives April 21, 2010 67
  • 68. Data Policies • Statements of intent and fundamental rules governing the creation, acquisition, integrity, security, quality, and use of data and information • More fundamental, global, and business critical than data standards • Describe what to do and what not to do • Should be few data policies stated briefly and directly April 21, 2010 68
  • 69. Data Policies • Possible topics for data policies − Data modeling and other data development activities − Development and use of data architecture − Data quality expectations, roles, and responsibilities − Data security, including confidentiality classification policies, intellectual property policies, personal data privacy policies, general data access and usage policies, and data access by external parties − Database recovery and data retention − Access and use of externally sourced data − Sharing data internally and externally − Data warehousing and business intelligence − Unstructured data - electronic files and physical records April 21, 2010 69
  • 70. Data Architecture • Enterprise data model and other aspects of data architecture sponsored at the data governance level • Need to pay particular attention to the alignment of the enterprise data model with key business strategies, processes, business units and systems • Includes − Data technology architecture − Data integration architecture − Data warehousing and business intelligence architecture − Metadata architecture April 21, 2010 70
  • 71. Data Standards and Procedures • Include naming standards, requirement specification standards, data modeling standards, database design standards, architecture standards and procedural standards for each data management function • Must be effectively communicated, monitored, enforced and periodically re-evaluated • Data management procedures are the methods, techniques, and steps followed to accomplish a specific activity or task April 21, 2010 71
  • 72. Data Standards and Procedures • Possible topics for data standards and procedures − Data modeling and architecture standards, including data naming conventions, definition standards, standard domains, and standard abbreviations − Standard business and technical metadata to be captured, maintained, and integrated − Data model management guidelines and procedures − Metadata integration and usage procedures − Standards for database recovery and business continuity, database performance, data retention, and external data acquisition − Data security standards and procedures − Reference data management control procedures − Match / merge and data cleansing standards and procedures − Business intelligence standards and procedures − Enterprise content management standards and procedures, including use of enterprise taxonomies, support for legal discovery and document and e-mail retention, electronic signatures, report formatting standards and report distribution approaches April 21, 2010 72
  • 73. Regulatory Compliance • Most organisations are is impacted by government and industry regulations • Many of these regulations dictate how data and information is to be managed • Compliance is generally mandatory • Data governance guides the implementation of adequate controls to ensure, document, and monitor compliance with data-related regulations. April 21, 2010 73
  • 74. Regulatory Compliance • Data governance needs to work the business to find the best answers to the following regulatory compliance questions − How relevant is a regulation? − Why is it important for us? − How do we interpret it? − What policies and procedures does it require? − Do we comply now? − How do we comply now? − How should we comply in the future? − What will it take? − When will we comply? − How do we demonstrate and prove compliance? − How do we monitor compliance? − How often do we review compliance? − How do we identify and report non-compliance? − How do we manage and rectify non-compliance? April 21, 2010 74
  • 75. Issue Management • Data governance assists in identifying, managing, and resolving data related issues − Data quality issues − Data naming and definition conflicts − Business rule conflicts and clarifications − Data security, privacy, and confidentiality issues − Regulatory non-compliance issues − Non-conformance issues (policies, standards, architecture, and procedures) − Conflicting policies, standards, architecture, and procedures − Conflicting stakeholder interests in data and information − Organisational and cultural change management issues − Issues regarding data governance procedures and decision rights − Negotiation and review of data sharing agreements April 21, 2010 75
  • 76. Issue Management, Control and Escalation • Data governance implements issue controls and procedures − Identifying, capturing, logging and updating issues − Tracking the status of issues − Documenting stakeholder viewpoints and resolution alternatives − Objective, neutral discussions where all viewpoints are heard − Escalating issues to higher levels of authority − Determining, documenting and communicating issue resolutions. April 21, 2010 76
  • 77. Data Management Projects • Data management roadmap sets out a course of action for initiating and/or improving data management functions • Consists of an assessment of current functions, definition of a target environment and target objectives and a transition plan outlining the steps required to reach these targets including an approach to organisational change management • Every data management project should follow the project management standards of the organisation April 21, 2010 77
  • 78. Data Asset Valuation • Data and information are truly assets because they have business value, tangible or intangible • Different approaches to estimating the value of data assets • Identify the direct and indirect business benefits derived from use of the data • Identify the cost of data loss, identifying the impacts of not having the current amount and quality level of data April 21, 2010 78
  • 79. 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) • Ponemon Institute 2009 Annual Study Cost of a Data Breach April 21, 2010 79
  • 80. Terms Used by Organisations to Describe the Activities Associated with Governing Data Data Management 62.7% Data Governance 55.4% Data Stewardship 46.6% Information Management 43.6% Information Governance 17.2% Data Resource 10.8% Management Information Stew ardship 10.3% Information Resource 10.3% Management Other 13.7% 0% 10% 20% 30% 40% 50% 60% 70% April 21, 2010 80
  • 81. 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% April 21, 2010 81
  • 82. 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% April 21, 2010 82
  • 83. 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% April 21, 2010 83
  • 84. 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% April 21, 2010 84
  • 85. 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% April 21, 2010 85
  • 86. 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% April 21, 2010 86
  • 87. Focus of Information / Data Governance Efforts Customers 70.2% Financials 57.6% Products and Production 46.6% Services 41.9% Sales 35.6% Employees 31.4% Supply Chain, Vendors, Suppliers 25.1% Items / Materials 20.4% Equipment and Facilities 16.2% Maintenance 13.1% Environment, Health and Safety 10.5% Other 9.5% 0% 10% 20% 30% 40% 50% 60% 70% 80% April 21, 2010 87
  • 88. 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 % April 21, 2010 88
  • 89. Primary Activities of Organisation's Information / Data Governance Efforts Standardise Data Definitions Across The Organisation 70.5% Provide Common Information Strategies, Processes, Policies, And Standards On Behalf Of The Organisation 61.6% Support Data Warehouse And Business Intelligence Initiatives 58.4% Define And Standardise Common Business Rules Across The Organisation 53.7% Select And Charter Specific Data Quality Improvement Projects 49.5% Provide Oversight And Enforcement Of Data Standards On Every Project That Involves Information Systems And Technology 47.9% Establish A Common Vocabulary And Culture Around The Deployment Of Data That Ensures Its 46.8% Privacy, Compliance, And Security Support The Access And Use Of Common Corporate Data Through A Focus On Architecture And Integration 45.8% Support The Development Of An Enterprise Logical Data Model 43.7% Guide The Management Of Master Or Reference Data 42.6% Support Information Management Problem-Solving And Decision-Making And Providing Processes For Strategic Alignment. 40.0% Manage Information Products 27.9% Measure The Costs Of Low Quality Data 25.3% Measure The Value Of High Quality Data 23.2% Implement Internal Information Chain Management 13.2% Implement External Data Supplier Management 10.0% Implement Information Product Management 10.0% Other 10.0% 0% 10% 20% 30% 40% 50% 60% 70% 80% April 21, 2010 89
  • 90. Primary Drivers for Organisation's Information / Data Governance Efforts General Desire To Improve The Quality Of Our Data 65.6% Data Warehousing / Business Intelligence 57.7% Compliance / Risk 46.6% Enterprise Architecture 33.3% Information Security / Privacy 32.3% Master Data Management (MDM) Project 31.2% Applications / Systems Integration 30.2% Customer Data Integration (CDI) Project 25.9% Suffered Major Negative Impact From Bad Data Quality 22.2% Service-Oriented Architecture (SOA) Project 18.0% Enterprise Resource Planning (ERP) Project 16.4% Merger And Acquisition Planning Or Implementation 12.7% Product Information Management (PIM) Project 10.1% Reaction To Competitors' Activity 3.7% Other 8.5% 0% 10% 20% 30% 40% 50% 60% 70% 80% April 21, 2010 90
  • 91. Category of Tools Currently Used in Organisation Data Quality Analysis, Assessment Or 66.3% Profiling Extract-Transform-Load (ETL) And Other 57.2% Data Integration Tools Data Modeling (Computer-Aided Software 48.7% Engineering) Data Matching And Reconciliation (Data 48.7% De-Duplication) Data Quality Monitoring 45.5% Metadata Repository 44.4% Data Remediation / Cleansing Tools 39.0% Data Relationship Discovery And Mappings 28.9% Workflow Tools 25.7% Business Rules Engines 20.3% Master Data Management (MDM) Tools 18.7% Customer Data Integration (CDI) Tools 13.4% Product Information Management (PIM) 5.9% Tools Rules Discovery Tools 4.3% Other 5.9% 0% 10% 20% 30% 40% 50% 60% 70% 80% April 21, 2010 91
  • 92. Functional Area to Which the Leader of the Organisation's Information / Data Governance Effort Reports Information Technology 43.1% Senior / Executive Management Team 31.0% Finance 17.2% Compliance / Risk 8.6% Operations / Manufacturing 8.6% Marketing 5.2% Purchasing 1.7% Legal 1.7% Other 8.6% 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% April 21, 2010 92
  • 93. Number of Levels Between the Organisation's Most Senior Leader and the Person Most Directly in Charge of the Information / Data Governance Effort 5 Levels or More 12.3% 4 Levels 14.0% 3 Levels 26.3% 2 Levels 22.8% 1 Level 14.0% They are the Same Person 3.5% Don't Know 7.0% 0% 5% 10% 15% 20% 25% 30% April 21, 2010 93
  • 94. Membership of Senior Information / Data Governance Body within an Organisation The Senior / Executive Management Team is the Top 21.4% Information / Data Governance Body C-Level non-IT Executives 26.8% C-Level IT Executives 26.8% Middle-Level non-IT Managers 51.8% Middle-Level IT Managers 33.9% Junior-Level non-IT Supervisors/Managers 7.1% Junior-Level IT Supervisors / Managers 14.3% My Organisation Does Not Have any Governance Body for 7.1% Information and Data Assets 0% 10% 20% 30% 40% 50% 60% April 21, 2010 94
  • 95. Relationship Between Information / Data Governance and Data Quality Leadership Information Governance and Data Quality Are Led by the Same 36.8% Person Information Governance and Data Quality Are Led by Different 17.5% People Who Report to the Same Manager Information Governance and Data Quality Are Led by Different 19.3% People Who Report to Different Managers There is No Specific Individual in Charge of Our Data Quality 17.5% Program Other 8.8% 0% 10% 20% 30% 40% 50% 60% April 21, 2010 95