Knowledge Cast by Enterprise Knowledge
Sep 29, 2026 · 18 min · 8 segments
Enterprise Knowledge’s Lulit Tesfaye, VP of Knowledge & Data Services, speaks with Ole Olesen-Bagneux, Vice President & Chief Evangelist at Actian and keynote speaker at this year's Semantic Layer…
Ole Olesen-BagneuxGuest
Lulit TesfayeHost
Basically, when I when I began, so basically I communicate and write and travel and talk all the time.
And so as a book author, I had the choice of writing a new book or updating my existing book, The Enterprise Data Catalog.
in a second edition, and I chose to propose a second edition at the time because I could see that many things had changed.
But when I look back at my proposal, I can also see that just in the span of like ten, nine, ten, eleven months, things have changed quite dramatically again.
And I dare say, at least for AI in the enterprise, I dare say to the better.
Metadata in general is becoming such an important component of AI architectures.
I think we have quite a lot of good years ahead of us for those that care about semantic structures, ontologies, and so forth.
So basically, the way I'm thinking about updating my manuscript is about everything AI, how it transforms not only what a data catalog, how it operates, but also what it is.
in the era of the modern data stack, right? So it was a technology that was about helping data engineers prepare data for data scientists, right? If you really, really want to boil it down, that was what a data catalog was at a certain point in time.
But I think what we're seeing now is that they're actually turning into something better.
Done correctly, data catalogs can really bridge the underlying data layers and the ontological structures that agents would need.
Basically, when I when I began, so basically I communicate and write and travel and talk all the time.
And so as a book author, I had the choice of writing a new book or updating my existing book, The Enterprise Data Catalog.
in a second edition, and I chose to propose a second edition at the time because I could see that many things had changed.
But when I look back at my proposal, I can also see that just in the span of like ten, nine, ten, eleven months, things have changed quite dramatically again.
And I dare say, at least for AI in the enterprise, I dare say to the better.
Metadata in general is becoming such an important component of AI architectures.
I think we have quite a lot of good years ahead of us for those that care about semantic structures, ontologies, and so forth.
So basically, the way I'm thinking about updating my manuscript is about everything AI, how it transforms not only what a data catalog, how it operates, but also what it is.
in the era of the modern data stack, right? So it was a technology that was about helping data engineers prepare data for data scientists, right? If you really, really want to boil it down, that was what a data catalog was at a certain point in time.
But I think what we're seeing now is that they're actually turning into something better.
Done correctly, data catalogs can really bridge the underlying data layers and the ontological structures that agents would need.
The rest of this transcript — segmented and speaker-labeled, so you land on the exact moment something was said
Search every transcript — by keyword, by phrase, or by meaning, across every show Radar indexes
Trends — what is surging across podcasts, measured against its own baseline
Alerts — when a name you follow appears in a newly indexed episode
No account is needed to search Radar.
