Answering the closing question about the biggest gap in data tooling, Steven Renwick of Tilores says it is a trustworthy identity layer. The line challenges the common assumption that centralizing data makes it reliable.
Yep, I mean, we're certainly always trying to keep an eye on it. We consider ourselves AI adjacent, so the more people are using AI, the more they need to have clean structured data, so that works nicely for us.
…follow along with the work that you're doing, I'll have you add your preferred contact information to the show notes. And as the final question, I'd like to get your perspective on what you see as being the biggest gaps in the tooling or technology that's available for building data systems today.
Yeah, so one of the biggest gap we see is this trustworthy identity layer. There's always been this idea that if you have all your data in a data warehouse, then the data warehouse becomes your source of truth. But all you've really done is taken disparate data from multiple different places and put it into one place. It still doesn't become data that you can trust. That I think is a big gap. It's obviously part of what we are trying to solve. So the next thing is then how do you measure how good your data is? Measuring the quality of data is pretty challenging. Measuring the precision and recall of data at scale when you're doing entity resolution is pretty challenging. And that's where we see some gaps that we want to be trying to help fill.
Well, thank you very much for taking the time today to join me and share the work that you've been doing on TeloRes and just the overall space of entity resolution and all of the complexities that come along with it. It's definitely a very interesting problem space and one that, as we've pointed…
Thank you, Tobias. It's been a good chat.
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