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  • 07/05/2023 8:30 AM | Anonymous member (Administrator)

    The Knowledge Area (KA) Context Diagrams describe the detail of the KAs, including detail related to people processes & technology. They are based on the concept of a SIPOC diagram used for product management (Suppliers, Inputs, Processes, Outputs, & Consumers). Context Diagrams put activities at the center, since they produce the deliverables that meet the requirements of stakeholders.

    Each context diagram begins with the KA's definition and goals. activities that drive the goals (Center) are classified into four phases: Plan (P), Develop (D), Operate (O), & Control (C). On the left side (flowing into activities) are the Inputs & Suppliers. On the right side (flowing out of the activities) are Deliverables & Consumers. Participants are listed below the Activities. On the bottom are Tools, Techniques, & Metrics that influence aspects of the KA.

    Lists in the context diagram are illustrative, not exhaustive. Items will apply differently to different organizations. The high-level role lists include only the most important roles. Each organization can adapt this pattern to address its own needs.

    The component pieces of the context diagram:

    1. Definition - concisely define the KA
    2. Goals - purpose of KA & fundamental principles that guide performance of activities within KA
    3. Activities - actions/tasks required to meet goals of KA
    a. (P) Planning Activities - set strategic/tactical course for meeting data management goals
    b. (D) Development Activities - organized around Software Development Life Cycle (SDLC (analysis, design, build, test, prepare, deploy)
    c. (C) Control Activities - ensure ongoing quality of data & the integrity, reliability, & security of systems where data is accessed & used
    d. (O) Operational Activities - support use, maintenance & enhancement of systems/processes where data is accessed/used
    4. Inputs - tangible things that each KA requires to initiate its activities
    5. Deliverables - outputs of KA activities, tangible things each function is responsible for producing
    6. Roles & Responsibilities - describe how individuals/teams contribute to activities within KA
    7. Suppliers - people responsible to provide/enable access to activity inputs
    8. Consumers - those that benefit directly from primary deliverables by the data management activities
    9. Participants - people that perform, manage the performance of, or approve the KA activities
    10. Tools - applications/other technologies that enable KA goals
    11. Techniques - methods/procedures used to perform activities & produce deliverables in the KA
    12. Metrics - measurement or performance, progress, quality, or efficiency evaluation standards

  • 06/28/2023 8:30 AM | Anonymous member (Administrator)

    The Environmental Factors Hexagon shows the relationship between people, process, and technology and provides a key for reading the DMBoK context diagrams. It puts goals and principles at the center, since these provide guidance for how people should execute activities and effectively use the tools required for successful data management.

  • 06/21/2023 8:30 AM | Anonymous member (Administrator)

    The Amsterdam Information Model (AIM), like the Strategic Alignment Model (SAM), takes a strategic perspective on business and IT alignment. Known as the 9-cell, it recognizes a middle layer that focuses on structure and tactics, including planning and architecture. More ever, it recognizes the necessity of information communication (expressed as the information governance and data quality pillar in the AIM Model).

    The creators of both the SAM and the AIM frameworks describe in detail the relation between the components, from both the horizontal (Business/IT strategy) and vertical (Business Strategy / Business Operations) perspective.

  • 06/14/2023 12:00 PM | Anonymous member (Administrator)

    The Strategic Alignment Model (SAM) abstracts the fundamental drivers for any approach to #datamanagement. At its center is the relationship between data and information. Information is most often associated with #businessstrategy and the operational use of data. Data is associated with information technology and processes which support physical management of systems that make data accessible for use. Surrounding this concept are the four fundamental domains of strategic choice: business strategy, information technology strategy, organizational infrastructure and processes, and information technology infrastructure and processes.

    The fully articulated SAM is more complex than is illustrated. Each of the corner hexagons has its own underlying dimensions. For example, within Business and IT strategy, there is a need to account for scope, competencies and governance. Operations must account for infrastructure, processes and skills. The relationship between the pieces help an organization understand both the strategic fit of the different components and functional integration of the pieces. Even the high-level depiction of the model is useful in understanding the organizational factors that influence decisions and data and data management.

  • 06/05/2023 9:30 AM | Anonymous member (Administrator)

    The important implications of the Key Activities of the Data Lifecycle from the Data Management Book of Knowledge are:

    * Creation and usage are the most critical points in the data lifecycle
    Data Quality must be managed throughout the data lifecycle
    Metadata Quality must be managed through the data lifecycle
    Data Security must be managed through the data lifecycle
    Data Management efforts should focus on the most critical data

  • 05/30/2023 12:00 PM | Anonymous member (Administrator)

    The key Data Management Principles from the Data Management Book of Knowledge are:

    * Data is an asset with unique properties.
    * The value of data can and should be expressed in economic terms.
    * Managing data means managing the quality of data.
    * It takes Metadata to manage data.
    * It takes planning to manage data.
    * Data management is cross-functional; it requires a range of skills and expertise.
    * Data management requires an enterprise perspective.
    * Data management must account for a range of perspectives.
    * Data management is lifecycle management.
    * Different types of data have different lifecycle characteristics.
    * Managing data includes managing the risks associated with data.
    * Data management requirements must drive Information Technology decisions.
    * Effective data management requires leadership commitment.

  • 05/15/2023 3:13 PM | Anonymous member (Administrator)

    The DAMA Wheel defines the Data Management Knowledge Areas. It places data governance at the center of data management activities, since governance is required for consistency within and balance between the functions. The other Knowledge Areas (Data Architecture, Data Modeling, etc.) are balanced around the Wheel. They are all necessary parts of a mature data management function, but they may be implemented at different times, depending upon the requirements of the organization. These Knowledge Areas are the focus of Chapters 3 - 13 of the DMBoK 2nd Edition.

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