Projects that use personal data should have a disciplined approach to the use of that data. They should account for:

• How they select their population for study (arrow 1)

• How data will be captured (arrow 2)

• What activities analytics will focus on (arrow 3)

• How the results will be made accessible (arrow 4)

Within each area of consideration, they should address potential ethical risks, with a particular focus on possible negative effects on customers or citizens.

A risk model can be used to determine whether to execute the project. It will also influence how to execute the project. For example, the data will be made anonymous, the private information removed from the file, the security on the files tightened or confirmed, and a review of the local and other applicable privacy law reviewed with legal. Dropping customers may not be permitted under law if the organization is a monopoly in a jurisdiction, and citizens have no other provider options such as energy or water.

Because data analytics projects are complex, people may not see the ethical challenges. Organizations need to actively identify potential risks. They also need to protect whistleblowers who do see risks and raise concerns. Automated monitoring is not sufficient protection from unethical activities. People - the analysts themselves - need to reflect on possible bias. Cultural norms and ethics in the workplace influence corporate behavior - learn and use the ethical risk model. DAMA International encourages data professionals to take a professional stand, and present the risk situation to business leaders who may not have recognized the implications of particular uses of data and these implications in their work.

August 2023 Newsletter.pdf

Defined simply, ethics are principles of behavior based on ideas of right and wrong. Ethical principles often focus on ideas such as fairness, respect, responsibility, integrity, quality, reliability, transparency, and trust. Data handling ethics are concerned with how to procure, store, manage, use, and dispose of data in ways that are aligned with ethical principles. Handling data in an ethical manner is necessary to the long-term success of any organization that wants to get value from its data. Unethical #datahandling can result in the loss of reputation and customers, because it puts at risk people whose data is exposed. In some cases, unethical practices are also illegal. Ultimately, for #datamanagement professionals and the organizations for which they work, data ethics are a matter of social responsibility.

The ethics of data handling are complex, but they center on several core concepts:

• Impact on people: Because data represents characteristics of individuals and is used to make decisions that affect people's lives, there is an imperative to manage its quality and reliability.

• Potential for misuse: Misusing data can negatively affect people and organizations, so there is an ethical imperative to prevent the misuse of data.

• Economic value of data: Data has economic value. Ethics of #dataownership should determine how that value can be accessed and by whom.

Organizations protect data based largely on lows and regulatory requirements. Nevertheless, because data represents people (customers, employees, patients, vendors, etc.), data management professionals should recognize that there are ethical (as well as legal) reasons to protect data and ensure it is not misused. Even data that does not directly represent individuals can still be used to make decisions that affect people's lives.

There is an ethical imperative not only to protect data, but also manage its quality. People making decisions, as well as those impacted by decisions, expect data to be complete and accurate. From both a business and a technical perspective, data management professionals have an ethical responsibility to manage data in a way that reduces risk that it may misrepresent, be misused, or be misunderstood. This responsibility extends across the data lifecycle, from creation to destruction of data.

Unfortunately, many organizations fail to recognize and respond to the ethical obligations inherent in data management. They may adopt a traditional technical perspective and profess not to understand the data; or they assume that if they follow the letter of the law, they have no risk related to data handling. This is a dangerous assumption.

The data environment is evolving rapidly. Organizations are using data in ways they would not have imagined even a few years ago. While laws codify some ethical principles, legislation cannot keep up with the risks associated with evolution of the data environment. Organizations must recognize and respond to their ethical obligation to protect data entrusted to them by fostering and sustaining a culture that values the ethical handling of information.

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.