What are data governance frameworks?

Data governance frameworks must be capable of facilitating collaboration throughout the organization. As such, vital data governance stakeholders can be found across nearly every level of the enterprise.

Continuous monitoring is essentially 24/7 surveillance for network activity. When suspicious, non-compliant, or otherwise unauthorized activity occurs, continuous monitoring provides optimal transparency and gives organizations a chance to respond.

Data stewards are the champions of the data governance framework. Stewards help ensure that any daily data practices within the business are in line with established policies and standards, and that these practices are being followed. Data stewards generally either take the responsibility of caring for the data themselves, or take a more administrative approach by training and consulting others on how to manage data assets.

Data custodians are appointed to handle various technical aspects of the data governance framework. This may include onboarding and maintenance, as well as any data asset end-of-life updates.

The data governance committee may be thought of as the ruling body of the data governance framework. Working together, this group of individuals will have significant say in establishing and approving data standards and policies and will also act as the authority when faced with emergent data issues. Larger enterprises may need to further delineate by creating subcommittees for different data domains within the business. Additionally, the aforementioned roles should ideally be supported by a dedicated data governance team.

Although different organizations may choose different titles or roles for their data governance teams, the basic structure generally includes the following members:

The data governance architect is responsible for supervising the design and implementation of data governance solutions.

The data analyst reviews vital information and applies analytical models to identify trends and gather actionable insights related to data governance.

The data strategist works closely with the data analyst, using data insights and forecasts to develop and execute data strategy.

The data compliance specialist monitors data usage within the governance framework to confirm that all required standards are being adhered to.

Data managers help direct data governance across all levels of the organization.

There are additional roles in the organization you should consider involving in the development of your data governance framework including the platform owner, enterprise architect, security administrator, audit/risk expert, and business process stakeholders.

Although data governance teams may play a critical role, there are also other methodologies for improving data governance frameworks. One such approach is by using a data governance maturity model.

A data governance maturity model is used to measure an organization's data governance framework and initiatives and communicate them in ways that may be understood by the entire company. By comparing their data governance framework against a maturity model, the organization can accurately gauge the effectiveness of their data management initiatives.

In mature data governance frameworks, all the necessary processes are securely in place to access, manage, and improve business functions using reliable data.

The basic maturity model for data governance frameworks is the progressive model, and consists of several phases that may be used to track the progress of their data governance initiatives towards true data maturity:

In Phase 0, the organization is largely unaware of the initiative or the importance of the data in question; processes are almost entirely reactive and non-standardized. To move the initiative to the next phase, relevant goals must be established, and stakeholders and decision makers will need to be educated as to the importance of the data.

Entering Phase 1, the organization has begun to become aware of the importance of the data initiative. Existing data practices are well documented and pain points become more obvious, allowing for the initial development of data governance frameworks to address these failings.

In this phase, the groundwork is being laid for future data governance. Information is being shared between internal teams, and an information management system is in place, though adoption is likely not yet universal. With more collaboration between teams, other issues become apparent.

Data governance begins to play a role in essentially every project within the organization, and the information management system is widely used. A governance team is established, and information owners are assigned. A truly comprehensive data governance framework may now begin to take shape covering the entire organizational structure.

Data and data insights are now appropriately viewed as valuable company assets. Data management policies are well understood and adhered to throughout the enterprise, valuable information metrics are clearly defined, and data is correctly categorized. Organizations can now take a more granular look at any inefficiencies or data weaknesses and work together to implement improvements to the governance framework.

In the final phase, the organization reaches its goal of proper data management. The framework is integral to business decisions, and all levels of the business view data and data insights as essential assets for providing a competitive edge and ensuring future growth.

When it comes to maturity models, there is no perfect, plug-and-play solution that will be an exact match for every business. Instead, decision makers are encouraged to review multiple variations of maturity models, find something that is close to meeting their needs, and then tailor the model where necessary to make it fully compatible.