most commonly misunderstood or underestimated about the success measures, data governance.

The misunderstanding is that going after the easy metrics, which is important, they still need to be there.

And sometimes it's hard to collect data for things, but understand that the easiest metrics have no relevance to leadership.

They are for your use to show that you're making progress and, along your rollout of your capabilities.

Okay.

It's more of a program management metric.

We had 18 users at the meeting.

Why is ROI so difficult to articulate in a governance program?

So with that, thinking about that, what does it actually look like? You know, what does good look like and how do we move from the early stages all the way through to being mature?

Well, if you're talking in terms of an operating framework or an operating model, a couple of characteristics.

First of all, Your initial operating models or frameworks are not your permanent ones.

Again, you have to be light on your feet, you have to be flexible.

You're going to start small and expand.

You're going to leverage what you've learned within your culture and then tune it and move forward.

You're going to deal with issues on a smaller scale with whatever protocol you've designed.

You know, why would it fail if you just copied somebody else's and brought it over and said, hey, I'm doing this as is?

have

changed.

Yeah.

And when I heard that, I said, oh, oh, really? 18? I said, so who do I call again? But my background was accounting.

I mean, I've written the CPA exam.

All right.

I know about control.

Well, that leads us to the next question, which is what does doing data governance well actually look like in an organization? Let's think about it from mid-stage, early mid-stage maturity.