Is that fair?

Yeah, it's been a fun but challenging transformation in the data space.

So over the last four or five years, we've consolidated our data science platform where we've truly got many different types of data that our data scientists are out playing and thinking up new great things.

In the same space in Connected, we've got our enterprise data warehouse.

So you've got that data coming in.

We've got linkages where how do we pair that with our telemetry data? What's going on in the organization? So it's been really fantastic to see it all in one platform.

But doing that has really required us to think about how we manage data.

Talk a bit about some of the exciting developments happening across Zebra in some of those functions.

And so in that scenario, if the data is bad, does that mean that you won't be putting it in the correct place, but you'll be putting it in a place that's dangerous? So does data quality really effectively become a safety control?

it can become a safety control.

And this goes back to what we were talking about before.

What are our use cases that we're really going after? If we put a pallet in the wrong place in our distribution center, that's not necessarily a safety issue, which is where we can start there.

I've talked to peers that work in the medical space, and the first thing they're going to deploy AI is not going to be distribution of medicine, right? And making that decision.

So, The data, though, definitely makes a difference.

If you have a sensor that's going off and saying that this pill container has aspirin, but in reality it's codeine, that's a very big mistake.

So is the human really deciding or are they just sticking their name on the diagram or they're sticking their name on the sheet to say they've said it even though they haven't really looked at it?