Data Governance

Data Governance is the practice of establishing and enforcing policies centered on data. Data Governance focuses on: Roles & Responsibilities, Data Ownership, Data Usage Agreements, Retention and Destruction Policies, and much more serve as the framework of enforcement and adherence to the rules, both company-defined as well as regulatory-defined, such that your company remains compliant, efficient and trustworthy as it relates to all data practices.

Augustine

12/21/20242 min read

Goals and Principles of Data Governance

Data Governance (DG) defines the

  • principles,

  • policies,

  • processes,

  • frameworks,

  • metrics, and

  • oversight mechanisms that are essential for managing data as a strategic asset.

DG ensures that data is accurate, consistent, secure, and accessible, guiding how it is collected, stored, processed, and used at every level of an organization. It establishes clear roles and responsibilities for data management, sets standards for data quality, and provides a structured approach to compliance and risk management. Through DG, organizations can unlock the full potential of their data, improve decision-making, foster transparency, and maintain data integrity throughout its lifecycle. As clearly seen in the pic above DG is centered on everything data.

Defining Data Governance

Data governance should

  • align with an organization's business strategy and goals,

  • ensure that data management efforts support overall objectives.

  • Facilitates shared responsibility for data-related decisions across organizational and system boundaries,

  • foster an integrated view of data.

Successful data governance requires clarity on what is being governed, who is being governed, and who is overseeing governance. For maximum effectiveness, data governance should be an enterprise-wide effort, not limited to a specific department. Defining the scope of data governance involves understanding what constitutes the enterprise and governing that scope accordingly.

Areas to consider When constructing DG Model for an organization

  • Value of data to the company

  • Business Model

  • Cultural Factors

  • Impact of regulations

Data governance often involves multiple layers, determining accountability for stewardship activities, data ownership, and more. The operating model outlines how the governance organization interacts with those managing data initiatives, including change management processes to introduce the program. It also defines how issues are resolved through governance. Figure 19 illustrates an example of such an operating framework, but it must be customized to fit the specific needs of each organization.

Develop Data Governance Strategy

A data governance strategy outlines the scope and approach to governance efforts, aligning with business, data management, and IT strategies. It should be implemented iteratively and tailored to each organization. Key deliverables include:

  1. Charter: Defines business drivers, vision, mission, principles, readiness assessment, process discovery, and current issues or success criteria.

  2. Operating Framework and Accountabilities: Establishes structure and responsibility for governance activities.

  3. Implementation Roadmap: Details timeframes for policies, business glossary, architecture, asset valuation, standards, processes, and regulatory compliance.

  4. Plan for Operational Success: Describes the target state of sustainable data governance activities.

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