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In today’s episode, you hear from Laura Madsen with 20+ years in data and analytics, authoring books on data governance and healthcare analytics, and co-founded Minneapolis-based consulting firm Via Gurus.
Data Governance is about 4 main categories:
- Data Lineage / Management
Data Governance Ops:
Create an environment in which you’re always introducing new things and testing them out.
Data Governance is a journey, not a destination:
Most organizations are not prioritizing governance and putting the dollars behind it to ensure it’s successful. Once you start data governance, programs and initiatives, they should go on forever. And your job as a person that does that work is to make sure that they do go on forever long after your tenure at the organization. The hard thing is most organizations don’t even realize they need it until it’s too late.
Data Governance Should be Democratized:
Typically the end-users of the data (business stakeholders/consumers) see these data in two places, they see it in the very beginning when they’re entering an in and they see that the very end in a beautiful, fancy dashboard, there’s a crap ton of stuff that happens to that data between the point that they enter it.
The problem is we’ve spent so much of our time protecting those business stakeholders from the thing that happens in the middle. That’s when they ask questions about what they see at the end and that pretty little dashboard. Um, we often aren’t really in a great position to answer that question either because we haven’t had data management or data lineage tools, but, but also what happens is it degrades trust almost immediately because they know what they entered.
The way that we can actually improve the quality of the data and improve democratization is by doing the one thing that we thought we weren’t supposed to do, which is you let people in a little bit. They to build trust is if we create an enormous amount of transparency so that people can understand and help us become better stewards of our own data and improve the quality along the way.
Everybody in the organization Should be a Data Steward. If you’re looking at a report or metric that seems wrong, it’s your responsibility to escalate it. That process should be welcoming and efficient.