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What are some core principles of data governance?

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By Admin Account | Last updated: February 3, 2023

With the rise of GDPR and other compliance measures, data is under more scrutiny than ever before. Yet as organizations invest in more data and more data accessibility, keeping that data discoverable and well-maintained has never been more challenging.

That’s why data governance has become a must-have for data-driven organizations as a way to increase data's security and integrity across disparate stakeholders. But despite its importance, many leaders struggle to get a data governance initiative off the ground. Here are five critical steps to launching a successful data governance program:

  1. Think of data governance as a process, not a project. It’s not uncommon to approach data governance like a lofty project, but the fact of the matter is it’s an iterative process with many stages. As your company grows and evolves, your metric definitions and data needs will evolve too. Data governance is less about the right and wrong of your metrics and more about changing your company’s cultural approach to those metrics. Don’t think of data governance as something to be completed -- think of data governance as something to be adopted. (Also read: Data Governance is Everyone's Business.)
  2. Assign ownership and accountability. Data is produced -- and used -- by teams across the company, with different access permissions, use cases and personas. It’s critical that data is segmented by “domain” or “function” and assigned owners who are accountable when data breaks. No one wants to be on the other end of the screen when your CEO messages the data engineering team about “wrong data.” By assigning clear owners and setting up incident management processes accordingly, issues are fixed before they become a headache.
  3. Understand what data matters to the business. Once you’ve identified the first few domains you’d like to focus your energies on, the next step is to ensure that the data you’re governing is actually worth being governed. Not all data is created equal, and in today’s economic climate, it is not inaccurate to say that some data is worth more than others. For instance, data forecasting your company’s revenue next quarter is probably more worthy of your attention than a duplicate table sitting in a dusty corner of your Snowflake warehouse.
  4. Start small - don’t boil the ocean. Once you’ve identified the first few domains on which you'd like to focus your energy, the next step is to ensure the data you’re governing is actually worth being governed. Not all data is created equal, and in today’s economic climate, it is not inaccurate to say that some data is worth more than others. For instance, data forecasting your company’s revenue next quarter is probably more worthy of your attention than a duplicate table sitting in a dusty corner of your Snowflake warehouse.
  5. Prioritize data trust. Before you can even govern data, you need to be able to trust it. Here’s where data quality and observability come in. Data observability ensures stakeholders can trust whether or not their dashboards, and the data that feeds them, are reliable across five key pillars: freshness, volume, schema, quality, and lineage. Once you have data you can trust -- and you’ve determined what data actually matters and to who -- you’re well on your way to spinning up a solid data governance strategy.

Data governance is a living, breathing process that involves multiple stakeholders across the company, from data analysts and engineers to business users. The sooner we accept that data governance is a journey, not a sprint, the easier it will be to drive adoption iteratively for the domains and data sets that matter most. (Also read: How AI Can Ensure Good Data Quality.)

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