DATA GOVERNANCE

A data governance framework essentially is the plan for how to implement data governance within the organization. It details, in writing, processes and procedures, roles and responsibilities, and the roadmap. The following components help define a well-rounded data governance framework:

• Why: Create a charter document that outlines responsibilities and boundaries of data governance activities that align with the organization’s priorities. 
• What: Define the core components and functionalities data governance will provide as a service to the organization. 
• Who: Formalize roles and responsibilities as they relate to data governance.
• How: Define the processes and procedures that will facilitate the goals defined in the charter document.
• When: Create a roadmap based on the data governance framework to stay on track.

It’s important to note that a fully fledged and optimized data governance program takes time. That’s where smart scoping helps. Prioritize your data governance program based on what is achievable with your current capacity and focus on the outcomes your organization needs to drive your roadmap. Most data governance initiatives struggle because the scope of the initiative is greater than the bandwidth required to implement it.

The data governance program will not survive without clear ownership. While it might sound dramatic, assigning owners and stewards gives subject matter experts the opportunity to contribute to the shared knowledge of the organization through their participation in data governance.

To answer the question with another question: Why have a data catalog that is searchable by users when the information available isn’t current? If the data catalog is to be a single source of truth that empowers data-driven decision-making, it must be up-to-date.

Automation plays a significant role in maintaining a data catalog such as scheduling crawling and profiling. More complex jobs can be created to optimize workflows needed to stay up-to-date.

A single source of truth doesn’t appear overnight! Collaborating to answer questions enriches the data catalog as other users can view the history of in-platform discussion for a data object and gain further insights from subject matter experts. If extra information is uncovered through the collaboration features, it can even be added to the data object’s metadata.

Data policies are enforceable through a combination of measures within the data governance platform. Here are just a few examples of how OvalEdge helps implement data policies within the platform:

• Configurable AI models allow you to quickly find and classify PII and other sensitive data across the entire data ecosystem, and it learns as recommendations are accepted and rejected.
• Role based access controls ensures only those with the proper role can view certain data assets.
• Robust access requests and workflows creates a streamlined process that includes how long someone is granted access.

Data policies allow for high data discoverability within the data catalog. Users can still search and view the available metadata, and a user can request access to the data if they do not have the correct permissions to view it. Configured access requests ensure the right person(s) are notified who can assess for a valid business reason and grant access.