The DAMA Data Management Framework can also be depicted as an evolution of the DAMA Wheel, with core activities surrounded by lifecycle and usage activities, contained within the structure of governance.

Core activities, including Metadata Management, Data Quality Management, and data structure definition (architecture) are at the center of the framework.

Lifecycle management activities may be defined from a planning perspective (risk management, modeling, data design, Reference Data Management) and an enablement perspective (Master Data Management, data technology development, data integration and interoperability, data warehousing, and data storage and operations.)

Usages emerge from the lifecycle management activities: Master data usage, Document and content management, Business Intelligence, Data Science, predictive analytics, data visualization.  Many of these create more data by enhancing or developing insights about existing data.  Opportunities for data monetization may be identified as uses of data.

Data governance activities provide oversight and containment, through strategy, principles, policy, and stewardship.  They enable consistency through data classification and data valuation.

The intention in presenting different visual depictions of the DAMA Data Management Framework is to provide additional perspective and to open discussion about how to apply the concepts presented in the DMBoK.  As the importance of data management grows, such frameworks become useful communications tolls both within the data management community and between the data management community and our stakeholders.

A third alternative to the DAMA Wheel is depicted in the DAMA Data Management Function Framework. This also draws on architectural concepts to propose a set of relationships between the DAMA Knowledge Areas.  It provides additional detail about the content of some Knowledge Areas in order to clarify these relationships.

The framework starts with the guiding purpose of data management: To enable organizations to get value from their data assets as they do from other assets. Deriving value requires lifecycle management, so data management functions related to the data lifecycle are depicted in the center of the diagram.  These include planning and designing for reliable, high-quality data; establishing processes and functions through which data can be enabled for use and also maintained; and, finally, using the data in various types of analysis and through those processes, enhancing its value.

The lifecycle management section depicts the data management design and operational functions (modeling, architecture, storage and operations, etc.) that are required to support traditional uses of data (Business Intelligence, document and content management).  It also recognizes emerging data management functions (Big Data storage) that support emerging uses of data (data Science, predictive analytics, etc.) In cases where data is truly managed as an asset, organizations may be able to get direct value from their data by selling it to other organizations (data monetization).

Organizations that focus only on direct lifecycle functions will not get as much value from their data as those that support the data lifecycle through foundational and oversight activities.  Foundational activities, like data risk management, Metadata, and Data Quality management, span the data lifecycle.  They enable better design decisions and make data easier to use.  If these are executed well, data is less expensive to maintain, data consumers have more confidence in it, and the opportunities for using it expand.

To successfully support data production and use and to ensure that foundational activities are executed with discipline, many organizations establish oversight in the form of data governance.  A data governance program enables an organization to be data-driven, by putting in place the strategy and supporting principles, policies, and stewardship practices that ensure the organization recognizes and acts on opportunities to get value from its data.  A data governance program should also engage in organizational change management activities to educate the organization and encourage behaviors that enable strategic uses of data.  Thus, the necessity of culture change spans the breath of data governance responsibilities, especially as an organization matures its data practices.

Aiken's pyramid describes how organizations evolve toward better data management practices.  Another way to look at the DAMA Knowledge Areas is to explore the dependencies between them.  Developed by Sue Geuens, the Functional Area Dependencies framework recognizes that Business Intelligence and Analytic functions have dependencies on all other data management functions.  They depend directly on Master Data and Data Warehouse solutions.  But those, in turn, are dependent on feeding systems and applications.  Reliable Data Quality, data design, and data interoperability practices are at the foundation of reliable systems and applications.  In addition, Data Governance, which within this model includes Metadata Management, data security, Data Architecture and Reference Data Management, provides a foundation on which all other functions are dependent.

July 2023 Newsletter.pdf

Peter Aiken's framework uses the DMBoK functional areas to describe the situation in which many organizations find themselves. An organization can use it to define a way forward to a state where they have reliable data and processes to support strategic business goals. In trying to reach this goal, many organizations go through a similar logical progression of steps.

Phase 1: The organization purchases an application that includes database capabilities. This means the organization has a starting point for #datamodeling / design, #datastorage, and #datasecurity (e.g., let some people in and keep others out). To get the system functioning within their environment and with their data requires work on integration and interoperability.

Phase 2: Once they start using the application, they will find challenges with the quality of their data. But getting to higher quality data depends on reliable #Metadata and consistent #DataArchitecture. These provide clarity on how data from different systems works together.

Phase 3: Disciplined practices for managing #DataQuality, Metadata, and architecture require #DataGovernance that provides structural support for data management activities. Data Governance also enables execution of strategic initiatives, such as Document and #ContentManagement, #ReferenceDataManagement, #MasterDataManagement, #DataWarehousing, and #BusinessIntelligence, which fully enable the advanced practices within the golden pyramid.

Phase 4: The organization leverages the benefits of well-managed data and advances its analytic capabilities.

Aiken's pyramid draws from the DAMA Wheel, but also informs it by showing the relation between the Knowledge Areas. They are not all interchangeable; they have various kinds of interdependencies. The Pyramid framework has two drivers. First, the idea of building on a foundation, using components that need to be in the right places to support each other. Second, the somewhat contradictory idea that these may be put in place in an arbitrary order.

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

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.

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.

June 2023 Newsletter.pdf

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.