July 06, 2022
5 minutes read
Information Insight

Best Practices for Building a Data Governance Framework

Collecting and utilizing data is a critical component for businesses to remain competitive in today’s market. Regardless of industry or vertical, data is the lifeblood that allows companies to run operations effectively and is a primary driver of better decision-making and strategies.

According to a Seagate and IDC survey, organizations are beginning to realize the benefits of data collection and are expected to continue collecting more and more data. In fact, the survey projects a 42.2% annual growth rate for enterprise data collection because of its abundance of benefits, such as creating actionable analytics, utilization of IoT devices, and more seamless cloud migration initiatives. 

As the volume of data organizations collect increases, the complexity of organizing, managing, and utilizing that massive amount of information does as well. Furthermore, discrepancies in data sets make it even more difficult for organizations to have accurate information on their customers, eliminate inefficiencies, and make more informed, data-driven decisions.

These barriers to the effective utilization of data lead to 43% of data remaining largely unleveraged. But there are ways to reduce the chances of errors in data and improve the organization and useability of data with a well-crafted data governance strategy. A data governance strategy and related organizational framework can help companies capture a diverse amount of internal and external data and derive value from that data with well-defined, consistent processes and responsibilities. This will lead to an improved data culture throughout the organization and lay the foundation for more advanced predictive analytics.

What is Data Governance?

Data governance is the collection of clearly defined policies, procedures, standards, and/or roles that ensure the effective and efficient use of data in enabling an organization to achieve its goals. At its core, data governance defines who can take what action, when, and how, based on the data itself. 

The Benefits of Data Governance

The benefits of well-crafted data governance are wide-reaching, yet vary for each individual company depending on how the data framework is set up. Some benefits of data governance include:

      • Increased trust in an organization’s data through improved integrity and a reduction in data errors
      • Future-proofing in order to allow easy introduction of new technologies and data tools that further enhance data utilization and its value to an organization
      • Minimized costs through the reduction of effort needed to correct and maintain data 
      • Maximized profits with better visibility into customers, providing insights for enhanced customer satisfaction and increased revenue through cross-sell, up-sell, and enhanced marketing capabilities
      • Improved efficiencies by reducing duplicative work for data feeds, stores, and reports

Why Would You Need a Data Governance Framework?

If you read the list of benefits of a data governance framework above and feel as though perhaps your company is not organizing or leveraging its data effectively, then you might need a more resilient data governance framework. In addition, if your organization is experiencing any of the below pain points, that is a further indication that your company could benefit from improved maturity in its data governance practices.

Some of the symptoms of an organization with poor data governance practices and maturity include:

      1. Inability to share common definitions and/or data values for common data elements across all business areas
      2. Inability to determine customer risk ratings because of inconsistent client or other relevant business information
      3. Inability of multiple businesses or departments to reconcile their data
      4. Failure to effectively meet regulatory requirements
      5. Inability to fully understand the depth of customer relationships because your data is conflicting or incomplete
      6. Inability to assess the profitability of specific business areas, product lines, or customer relationships
      7. Time-consuming, manual processes for generating executive scorecards and dashboards

If any of these pain points hit home for you, your company should consider implementing a new data governance framework or refreshing your existing one. Keep reading to learn more about how to go about this.

How To Develop a Successful Data Governance Framework

A data governance framework has three general pillars: 1) Policies and standards, 2) processes, and 3) roles and responsibilities. There are many ways to implement a data governance framework, but all data governance frameworks have this same basic bone structure. 

The approach can vary across organizations depending on the desired breadth for the data governance framework (whole company vs. single business unit/department), big bang vs. iterative (we recommend the latter), or the formality of the policies and standards being sought. For example, highly regulated companies will have a more rigorous approach to defining and implementing policies than non-regulated companies. Smaller companies might seek to institutionalize the data governance standards through more informal but equally effective channels.

Policies and Standards

Having clear policies and standards in place is one of the most important aspects of a successful data governance framework. Effective policies make an organization’s data easy to manipulate and leverage into a consolidated reporting and analytics platform that provides up-to-date information insights.

Furthermore, policies and standards will ensure your company’s data is “fit for use,” whether that use is reporting, analytics, and/or distribution to downstream systems.

By creating these policies, organizations create rulebooks and statements that define how data should be governed and managed, how it should be used, who can use it, and what the roles and responsibilities are for those who are accountable for said data. Additionally, putting these policies and standards into place allows for more effective data processes, which alleviates the need for data cleanup and simplifies data visualization and analytics.


Once policies and standards are put into place, the next step in improving data governance is to establish how the implementation of policies and standards will work. Processes need to be defined to ensure the policies and standards are followed throughout the organization. Some of these processes will deal directly with the data while some will relate to how each area will define and govern their critical data. 

Additionally, as the processes are developed and implemented, it is critical that appropriate change management practices, including training, are included in order to ensure adequate organizational adoption of the processes, policies, and standards.

Roles and Responsibilities

Designated roles with predetermined responsibilities help make a data governance framework a reality. These individuals can then become members of data governance specific key forums which form the foundation of the data governance framework for the company. Examples of these forums include a Steering Committee, a Working Group, and the Office of Data Governance.

      1. The Steering Committee: Responsible for sponsoring and funding data governance activities and enforcing all individual data governance accountabilities across the company.
      2. Data Governance Working Group: Oversees the more general, day-to-day execution of a data governance implementation and processes. They promote training and education efforts, and awareness of data governance practices and standards. This group consists of key stakeholders across the organization or area being governed.
      3. Office of Data Governance: Partially composed of members from both the Steering Committee and the Data Governance Working Group. Responsible for the overall data governance, including defining the standards, metrics, and procedures across the organization. Metrics should include information about the benefits derived from maturing data governance, including improved data quality, reduced reporting costs, and efficiencies/communication improvements across the organization.

    Kenway’s Data Governance Methodology

    Here at Kenway, we have the passion, expertise, and skills needed to effectively partner with organizations at any point in their data governance journey to begin maximizing the value of their data.

    As we walk through the process of developing or improving a data governance framework for a company, we start by aligning company objectives. We identify the most critical pain points and high-value use cases while still establishing a data governance mission and vision that aligns with corporate objectives.

    Then we establish business value opportunities. After understanding where a data governance program will drive the most value for a business, Kenway can assess where the organization’s data governance maturity level lies within a curve:


    Data Governance Maturity Curve


    Based on the company’s data governance maturity, we build a data governance roadmap to determine the actionable steps necessary to begin implementing a data governance framework. We often partner with organizations to translate the roadmaps into results, from 1) writing anchor and identifier policies, to 2) designing and implementing a data governance framework specific to the company’s needs, to 3) developing data governance tools, processes, and technologies. 

    If you’re ready to take the next step in your data governance journey, connect with one of our consultants to learn more.

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