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Every Data Governance program needs a team to coordinate and manage its activities, and a leader to oversee the team’s responsibilities and guide its interactions with the rest of the organization

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.

Effective Data Governance Program Structure

Successful data governance programs use an organizational structure based on best practices. The structure includes:

  • A Data Governance (DG) Program team that consists of data management and data governance professionals. The team members develop the policies and practices in conjunction with the lead business data stewards and set the standards for data and metadata to be used throughout the organization. This group is not a large unit, but it must be staffed by experienced data management and data governance professionals, so all the activities are implemented correctly according to best practices while aligning with the organization’s culture. This team is led by the Data Governance Program Manager or Director, with deep experience in all areas of data governance and enterprise data management.
  • A Data Governance Council formed from senior leadership representatives of all the business areas and IT. This council approves the policies, practices and standards for the program team. They also appoint the data stewards and select the lead business data stewards for each subject area. Usually, the council chair is the Data Governance Program Manager, supported by the program’s Executive Sponsor.
  • A set of data stewardship teams from each business area that helps design the policies and practices with the data governance team and implements them in conjunction with the data custodians from IT. Each team of data stewards is directed by a lead business data steward. The leading stewards then form a data stewardship coordinating group that settles cross-group differences in policies and practices and refines definitions and other metadata as needed.
  • Data stewardship teams that work on the metadata management (data definitions, source-to-target mapping, semantic resolutions, etc.), data quality (profiling, data cleansing, data error identification and remediation, etc.), policy and regulation implementation activities. Each data stewardship team is assigned a project from one of these data management categories according to the Data Governance Council’s priorities, and the projects may be long or short depending on the depth and breadth of the issue. Each subject area and business unit is represented by one or more data stewardship teams.

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.

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

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.


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


Dr. David P. Marco, LinkedIn Top BI Voice, IDMMA Data Mgt. Professional of the Year, Fellow IIM, CBIP, CDP

Dr. David P. Marco, PhD, Fellow IIM, CBIP, CDP is best known as the world’s foremost authority on data governance and metadata management, he is an internationally recognized expert in the fields of CDO, data management, data literacy, and advanced analytics. He has earned many industry honors, including Crain’s Chicago Business “Top 40 Under 40”, named by DePaul University as one of their “Top 14 Alumni Under 40”, and he is a Professional Fellow in the Institute of Information Management. In 2022, CDO Magazine named Dr. Marco one of the Top Data Consultants in North America and IDMMA named him their Data Management Professional of the Year. In 2023 he earned LinkedIn’s Top BI Voice. Dr. Marco won the prestigious BIG Innovation award in 2024. David Marco is the author of the widely acclaimed two top-selling books in metadata management history, “Universal Meta Data Models” and “Building and Managing the Meta Data Repository” (available in multiple languages). In addition, he is a co- author of numerous books and published hundreds of articles, some of which are translated into Mandarin, Russian, Portuguese, and others. He has taught at the University of Chicago and DePaul University.

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