What is a Data Governance Framework?
A data governance framework is an organizational strategy that defines who is responsible for managing and protecting information collected by a company or organization.
Data governance policies are a set of rules and guidelines formulated to ensure the quality and accessibility of data, as well as its security and privacy. A data governance framework is a strategy that outlines how to effectively develop, implement and maintain these policies. A framework can be formal, but it doesn't always have to be written in stone.
Many organizations already have some form of data governance framework in place. For example, IT departments routinely create policies for how to share files on company servers or how to manage personal data collected from customers. A formal data governance framework helps business leaders identify what's already in place and what still needs to be done. It also provides a way to collect the existing pieces into a cohesive whole that works together to protect the organization's assets.
A data governance framework is essential for managing the quality of your data. Data governance enables businesses to ensure that their data is correct, organized, and accessible.
How to create a successful data governance framework
- Understand your goals. Why are you implementing a data governance framework? Increasing the quality of your data? Understanding what you want to achieve will help you develop a successful framework.
- Identify stakeholders. Determine who needs to be involved in this process and how they should be involved. Your list might include everything from executive sponsors to end users.
- Define roles and responsibilities. Establishing roles and responsibilities will help ensure that everyone understands what's expected of them, which can reduce confusion and boost productivity.
- Define your processes. Create a clear workflow with defined workflows and responsibilities for each step in the process.
Why do you need Data Governance?
There are several reasons for creating a data governance framework. The most compelling reason is that too many organizations don’t have one, and they need it.
For example, someone in the organization needs data to make business decisions, and they can’t find the data or don’t trust the data that they do find. They then become reliant on the data stewards of the organization to find this data, but may not have any way of trusting how reliable it is.
The second reason to create a data governance framework is because the organization has lost control of its data assets, or needs to scale up their data operations.
For example, there are too many copies of some databases, not enough copies of other databases, and only one person knows where they all are. Transferring all of this knowledge is nearly impossible without set guidelines to do so.
Additionally, some databases may contain sensitive information that needs to be protected from unauthorized access. There must be a process and guidelines in place to ensure that only people with the appropriate permissions are able to access this data- and that the users who are having their data collected, understand who has access to it.
Purpose of a data governance framework
The purpose of a data governance framework is to document how an organization manages its data assets and how those assets interact with each other over their lifetime. A good data governance framework contains these five elements:
- A clear purpose for managing your data assets
- A set of guiding principles for managing your data assets (generally about 10)
- A set of goals for your data governance program (generally about 20)
- A set of roles and responsibilities for supporting
Examples of Data Governance
- Data quality. Ensuring that all data that is being collected is relevant and tagged in a way that aligns with the organizations goals.
- Data security. The data is collected and stored in such a way that is secure and abides by the terms the user has agreed with. Those who can access the data are permitted to do so.
- Data usability and discoverability. People from outside of the data organization and within it are able to find and use the data in a way that is efficient. They're able to do this in a scaleable manner.
- Metadata. The information being collected about the data itself is consistent and well recorded.