The importance of a data governance framework
We're thrilled to share a sneak peek of one of the sections in our latest ebook, "Data Governance: An Executive's Survival Guide." This guide is a must-have for anyone with a database, and we have packed it with an abundance of critical points and examples that will enlighten and entertain you.
One of the most valuable assets businesses have is the digital data we work with every day. As more and more data is collected and used, it's important to have a system to manage it effectively. Enter data governance frameworks.
What is a data governance framework?
Much like other aspects of our lives, a data governance framework provides rules and guidelines for collecting, storing, and using data in a business. This framework helps to streamline and scale core governance processes, making it easier to maintain compliance and support collaboration as data volumes grow.
Why does a data governance framework matter?
Having a data governance framework in place is crucial for the success of your operations. It allows your business to define and document standards, accountability, and ownership, making it easier to maintain high-quality data. In fact, 67% of respondents in a Profisee-sponsored Harvard Business Review Analytics Services report say that data governance is essential for achieving high-quality enterprise data.
Example of a data governance framework
If you're wondering how to create a data governance framework, the Data Governance Institute provides a helpful template. The term "data modeling" is often used interchangeably with "data governance framework" and can also refer to the visual representation of the framework. This representation can help to better understand how everything fits together.
Gimme a data governance checklist
The complete list of data governance program components is available in the ebook that's available for free, but we'll give you a glimpse at the top four.
Mission and Vision: Define the mission and vision of the data governance program using compelling language.
Goals, Metrics, and Funding Strategies: Include SMART goals to measure the program's success and explore funding options with stakeholders.
Rules: Outline data-related policies, standards, compliance requirements, business rules, and data definitions. Address gaps and overlaps in definitions.
Decision Rights: Identify who has the decision rights for data-related decisions, including when new rules are created. Establish the decision rights for the data governance program.
You can read more about each of these components, and the other six points in the full ebook.
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