Jul 17, 2026 · 28 min · 13 segments
Welcome to *Inside Applied Data Governance*, a DATAVERSITY Talks podcast where we explore the practical realities of data governance with the experts who helped build the Applied Data Governance…
Jimm JohnsonGuestBarbara NeshawHost
It represents a perfect world, perfect conditions, perfect data, perfect behaviors.

And it's almost like, you know, the demonstrations when IT vendors come in and they pitch their tool, the demo is perfect.

I mean, that's not reality because you go to implement that tool and you realize that there's all kinds of challenges that are not represented in the perfect world.

It's based on what we know, what we what we can observe and what we believe is possible based on what we know and observe.

You walk out of the theory and you're in the real world and conditions change and data is messy and people are challenging and culture is strong.

And if it does work, how well does it work? And you discover gaps and assumptions about your theory or your understanding of it.

But with theory and this feedback loop that you create by trying things and learning from them, you're troubleshooting in real time.

So there's a saying, I think it goes, in theory, there's no difference between theory and practice.

But in practice, there is, right? So If you're data, I mentioned if you're a data or IT person, I mentioned earlier, you know, the perfect demos, that's theory, it's a vision, you can work towards it, but it's going to take you time.

It represents a perfect world, perfect conditions, perfect data, perfect behaviors.

And it's almost like, you know, the demonstrations when IT vendors come in and they pitch their tool, the demo is perfect.

I mean, that's not reality because you go to implement that tool and you realize that there's all kinds of challenges that are not represented in the perfect world.

It's based on what we know, what we what we can observe and what we believe is possible based on what we know and observe.

You walk out of the theory and you're in the real world and conditions change and data is messy and people are challenging and culture is strong.

And if it does work, how well does it work? And you discover gaps and assumptions about your theory or your understanding of it.

But with theory and this feedback loop that you create by trying things and learning from them, you're troubleshooting in real time.

So there's a saying, I think it goes, in theory, there's no difference between theory and practice.

But in practice, there is, right? So If you're data, I mentioned if you're a data or IT person, I mentioned earlier, you know, the perfect demos, that's theory, it's a vision, you can work towards it, but it's going to take you time.
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