Many organizations struggle with the design and implementation of their data governance programs for a variety of reasons. They may not understand the need for an enterprise approach to data governance and may attempt to implement policies via a line of business, an application, or a functional area division.

This is a recipe for failure, as all successful data governance programs are enterprise-wide; they span all lines of business, all applications and all functional areas. Without this holistic view and the support of all the enterprise’s areas, the goals of data governance will not be met.

Data governance is the development and implementation of policies, practices, standards and procedures that manage data and information across an organization. If the enterprise view and full organizational support do not exist, then the existing data quality challenges and metadata management issues will remain and be reinforced.

Even smaller organizations need data governance, since every organization must determine how they will manage their decisions based on “facts and data.” Each organization, regardless of size, needs policies and standards to ensure those decisions are consistent and provide confidence.

Challenges in Implementing Data Governance Council

Implementing a data governance program poses several common challenges, especially given the dynamic nature of data and technology. A unified data governance strategy is crucial to address these challenges effectively. Key obstacles organizations encounter include:

  • Adaptability of Governance Policies: With the rapid evolution of data and technology, data governance policies must remain flexible to accommodate changes. A successful data governance team structure will integrate routine reviews to ensure policies remain relevant to emerging data assets and trends.
  • Change Management Tension: Poorly managed changes can lead to friction between departments, particularly if roles and responsibilities in data governance initiatives are unclear. A robust data governance framework should designate clear roles, involving senior executives like the Chief Data Officer (CDO), data stewards, etc., etc., to mediate and align priorities across business units.
  • Legal and Regulatory Compliance: Governance policies must adhere to relevant regulations and standards, especially concerning data access, storage, and security. Organizations can establish a governance council or committee to oversee compliance and resolve critical data-related issues.
  • Continuous Improvement: Governance efforts should include ongoing updates and adaptation to align with evolving business objectives and maintain data quality. Regularly refining data management practices and aligning governance priorities with strategic direction ensures that the organization’s data governance program remains effective and valuable. Addressing data quality issues is essential for maintaining the accuracy and integrity of data within the organization.

Building a Data Governance Team

Define the Objectives

Defining the objectives of a data governance team is a crucial step in establishing a successful data governance program. These objectives should align with the organization’s strategic goals and address key areas such as data quality, compliance, security, and usability. By setting clear objectives, the governance team can focus its efforts on the most critical aspects of data management.

Executive sponsorship is essential for the success of any data governance initiative. Having a high-level executive, such as a Chief Information Officer (CIO) or Chief Data Officer (CDO), champion the data governance program ensures that it has the necessary support, resources, and authority. This executive sponsorship not only provides the governance team with the backing it needs to implement policies and practices but also signals to the entire organization the importance of data governance.

Identify Key Stakeholders

Identifying key stakeholders is another essential step in building a data governance team. These stakeholders should come from various departments, including IT, business units, legal, compliance, and risk management. By involving representatives from different areas of the organization, the governance team can ensure a comprehensive approach to data governance.

These stakeholders will play a critical role in making decisions related to data governance and should be actively involved in the creation and ongoing activities of the data governance team. It is important that they have a clear understanding of the organization’s goals and objectives and how data governance can support these goals. By engaging key stakeholders, the governance team can foster collaboration and ensure that data governance efforts are aligned with the broader organizational strategy.

Effective Data Governance Program Structure to Address Data Quality Issues

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Although these groups may use different names in other organizations, this structure has been proven to be the most successful approach to implementing enterprise data governance programs in organizations of all sizes and every industry. Effective data management within this framework is essential for maintaining data quality, security, and compliance, ultimately supporting efficient decision-making in organizations.

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Figure 1: Sample Enterprise Data Governance Model – © EWSolutions, Inc.

Data Governance Roles and Responsibilities

Data Governance Council

The Data Governance Council is a cross-functional team responsible for overseeing the organization’s data governance program. This council plays a pivotal role in developing and implementing data governance policies, procedures, and standards. Its responsibilities include ensuring data quality and security, as well as coordinating data governance efforts across the organization.

The council serves as the central body that provides guidance and support to the data governance team. It should include representatives from various departments, such as IT, business units, legal, compliance, and risk management. The council should be chaired by a senior executive, such as a CIO or CDO, who has the authority to make decisions and allocate resources. Regular meetings are essential for the council to discuss data governance issues, review progress, and make informed data governance decisions.

The responsibilities of the Data Governance Council include:

  • Developing and implementing data governance policies, procedures, and standards
  • Ensuring data quality and security
  • Coordinating data governance efforts across the organization
  • Providing guidance and support to the data governance team
  • Reviewing and approving data governance initiatives
  • Allocating resources and budget for data governance initiatives

The Data Governance Council plays a critical role in ensuring that the organization’s data governance program is effective and aligned with the organization’s goals and objectives. By clearly defining and communicating the council’s responsibilities and activities to all stakeholders, the organization can foster a culture of strong data governance while ensuring the success of its data governance efforts.

Incremental Approach to Data Governance Program Structure

Many organizations do not want to or cannot start with a large-scale data governance program structure. This does not mean they cannot implement a successful program; the optimal structure is scalable. Organizations that have adopted a LEAN or Agile approach often resist DG program team development, but in data governance, the program team is an essential component. As stated above, the team is small and built with experienced professionals. It should be focused on data governance activities, knowledge transfer, coordination with data stewardship teams and other groups, and the management of the Data Governance Council and other relevant bodies.

Additionally, the development of a Data Governance Council is a crucial component and should not be omitted from Agile or LEAN data governance implementations. This group makes the important decisions concerning data and its management for the organization, setting direction that will be important for continued success, especially for an iterative or incremental approach when some decisions may be revisited by later business units.

The need for business data stewards, both the lead business stewards and the line data stewards, is especially important in an incremental approach to data governance. Using this method, the organization usually establishes one or two data stewardship teams to address specific data-related challenges with the support of the small Data Governance Program team, after they are trained in data governance and data stewardship concepts. The data stewards may be asked to support the DG program team in writing policies that apply to their specific issue – and that will be expanded to include the organization in general – under the approval of the Data Governance Council.

Additional data stewardship teams should be enacted according to a schedule developed by the DG Program team and the DG Council, for projects identified by the DG Council. This schedule should be assertive, to maintain program momentum and to retain interest across the organization in data governance and data stewardship, while demonstrating value. In the starting projects, and perhaps with smaller organizations, the data stewardship teams may consist of a lead business data steward and one to three line business data stewards, based on the organization’s subject areas.

Conclusion

Every organization that implements data governance should adopt a program structure based on the proven best practices for a long-lasting successful endeavor.