Snowflake is a cloud-based data warehouse platform that allows organizations to store, process, and analyze large amounts of structured and semi-structured data. However, it is crucial to set a framework for Snowflake data governance to stay compliant and ensure data quality. How to do that? Keep reading!
What is the importance of data governance in Snowflake?
While Snowflake can store high volumes of data and be an extremely powerful tool for businesses, it is critical to take care of data governance in Snowflake. Data governance creates a structure for managing stored data, ensuring data quality, and completing tasks. And all of it starts with a framework.
Having a Snowflake data governance framework in place helps with the following:
- Measuring the quality of data;
- Managing the data lifecycle;
- Staying compliant with regulatory standards;
- Minimizing costs and mitigating risks.
Snowflake data governance framework in 5 steps
One can create a Snowflake data governance framework in 5 steps.
- Set a plan for how to manage enterprise data. Understand what data you have, where it is stored, and who can access it. Consider problems that need to be solved with data governance and classify them by type and priority.
- Form a data governance team, otherwise known as a governance council or committee, to master data governance.
- Understand regulations that apply to one’s industry. The most common ones are GDPR, CCPA, and HIPAA.
- Set a procedure to ensure data quality.
- Automate data governance and scale the processes.
The takeaway
Overall, it is important to have a framework for Snowflake data governance to manage and control the organization’s data, ensuring that it is used effectively and in a responsible way.
