Data Governance policies are increasingly important in the enterprise due to growing regulatory and security concerns in today’s business environment. In addition to these issues, organizations embarking on an AI journey quickly discover the urgent need for an improved level of data quality surrounding information assets. A comprehensive master data management (MDM) program, integrations and reference data help to create consistent and accurate information which is available for decision support data storytelling.
I. Establish Governance Framework
A governance framework is the foundational structure that outlines how an organization will implement and maintain its data governance policies. It includes roles, processes, responsibilities, and technologies that support the policies to be implemented.
The key activities in the governance framework include these steps:
a. Define the vision, mission, and objectives
b. Establish the guiding principles and scope.
c. Form a Data Governance Council.
The roles needed in this phase include the Chief Data Officer, who leads the framework development, the Data Governance Council, and a member of the Legal or Compliance team to ensure alignment with relevant legislation. The Governance Council provides oversight and strategic direction to the framework policies.
II. Identify and Classify Data Assets
This involves creating a comprehensive inventory of data assets and categorizing them based on sensitivity, criticality, and usage within the enterprise. Classifying data with appropriate labels such as public, confidential, or restricted, helps to implement security policies within the enterprise. In this process, roles which are most familiar with the application data supply documentation to add metadata to the data assets.
These roles include Data Stewards, who are everyday users of the data, Business Analysts, who provide business context, and Data Engineers or Architects who perform discovery and map integrations and systems with lineage.
III. Define Data Ownership and Stewardship
Identifying clear accountability for data within the company ensures that each data domain has responsibility assigned for quality, access, and the lifecycle requirements for information. Each data owner assigns stewards, who have awareness of the functionality of the data in their segment. For example, a company’s CFO might be the owner of accounting data, and accounting managers may be data stewards within each accounting department.
The role of the Data Owner is to hold accountability for data quality and compliance surrounding policies that protect the data. Data Stewards represent the operational aspect of relevant governance policies which they cover.
IV. Develop Policies and Standards
In this phase, policies and standards are written to provide the rules and guidelines for managing data across the organization. These will create the necessary framework for consistent and secure handling of information. The key activities include standards for these areas:
Policies and standards for data quality, privacy, and security.
Data life cycle and retention standards.
Regulatory and legal compliance.
In many organizations, specialized roles are needed to oversee privacy and security functions due to the complexity and technical nature of the systems involved. If there is a legal team in the organization, they will have insight into the regulatory compliance activities as well.
V. Implement Data Quality Management (DQM)
Data Quality operations assist data owners with processes that ensure data is accurate, complete, and reliable in their ability to provide management with decision-making.
In this phase, metrics are designed to keep Data Owners apprised of the state of each domain. Roles such as the Data Quality Analyst conduct data profiling and analysis while Data Engineers develop validation and failure checks into pipelines.
VI. Deploy Governance Tools and Technology
Governance tools support automation of processes, monitoring of data domains, and enforcement of implemented policies. These may include metadata management and augmentation, lineage tracking, and security controls.
The key activities in this step include the selection process for the governance platform, the integration with existing data infrastructure, and the configuration of the workflows within the platform. Many governance tools include dashboards and reports to implement as well.
The roles required to complete this phase are the IT teams to deploy and maintain the software, and the governance analysts to configure and manage the toolset.
VII. Monitor, Audit, and Report
There is a need to monitor the status of an organization’s implementation of data governance to ensure that there is compliance with policies, particularly if there are regulatory considerations. Reporting allows for transparency into the state of data quality and usage of curated data objects. Finally, audits are critical to ensure that security policies remain intact.
This function is accomplished by internal audit teams and governance analysts. These two roles have the authority to report key metrics and to escalate issues. Key metrics include definitions for data quality levels, completeness of data, and compliance measures.
VIII. Educate and Train
This phase is important in creating a data-literate culture that spans every employee group. Each group should understand their role in preserving data governance and in following the best practices established by leadership.
The key activities include:
Development of training programs and materials.
Conducting workshops and awareness activities.
Promoting data literacy across all departments.
These may be delivered by HR, Training teams, or designated champions within the Stewardship role who have a vested interest in advocating governance policies.
Finally, as these policies and procedures are developed and completed, there must be an approval and formal executive adoption of the governance solution. The roles and responsibilities of the key members should be named and timelines established for each phase. Key signatories of the plan include top management, security, and information technology teams responsible for implementation. Provisions for reviews and any necessary updates should be part of the adoption plan, with regular transparent updates on progress across the organization.
About
The Technology Strategic Advisory Group exists to promote the professional development and career growth of CITP credential holders and other stakeholders by creating and curating resources that address emerging needs in technology and business. Committed to fostering continuous learning, innovation, and adaptability, the group provides insights and support to help professionals navigate challenges, expand their expertise, and lead with confidence. Through collaboration and strategic initiatives, the group ensures that the CITP community and related professionals remain connected, informed, and prepared for the future.
Howard Fulks, CPA, CITP
Howard Fulks is the Director of Analytics and Data Scientist at The R.D. Offutt Company. A CPA and CITP credential holder, Howard began his career as a cost accountant in manufacturing before transitioning into technical analysis, scorecarding, and database development at Great Plains Software (later acquired by Microsoft). He went on to serve as a data platform architect in IT. Howard holds an MBA with a concentration in Business Intelligence and a Microsoft certification in Data Science.