TopQuadrant How to Build an AI-Ready Data Foundation
Jun 26, 2026 11:06
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
thank you everyone for joining us this afternoon um we are very excited
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
to be here and joined with all of you with all of you guys um to
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
chat about this topic it's something that we've been spending a lot of time thinking
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about um working with all with our customers and um
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
really rethinking um you
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know uh
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
Thinking about a lot of how we do work based around this
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topic. So anyway,
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
excited to talk about how to build an AI -ready data foundation.
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
And I'm Amar Doshi.
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
I'm the chief product officer here at Top Quadrant.
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
I'm joined here with Steve Hedden,
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who is our product manager.
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
And to talk a little bit about,
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
first, introduce our company a little bit.
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
For those of you who may not know,
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we are at Top Quadrant.
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
We work with mostly large organizations.
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
We serve some of the largest organizations in the world and help them solve
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pretty complex data problems.
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That's kind of been our history.
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
We've been in this space as thinking about the
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evolution of the data stack from various iterations,
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from data fabric,
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data mesh, data,
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all of these things.
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And really seeing this moment in terms of AI as
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one where we're
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were not that different from others but also simultaneously like very very different and
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um
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
And really thinking about the work that we do is foundational and fundamental to that.
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And so we'll talk a little bit more about what that actually is,
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but wanted to kind of give you a bit of background
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about who we are and what we do.
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Also excited to see some of the recognition of the work that we do with
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our customers and how that shows up in the marketplace.
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
And recently we were recognized as in the Gartner
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Magic Quadrant for metadata solutions.
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
a little bit about our background we come from a rich history and kind of the semantic web
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and um in semantics as as a
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as a practice.
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Some of our foundational engineers were some of the earliest kind of engineers
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that helped build Protege,
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as well as some of the other kind of core semantic tooling in this space.
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We've been part of the W3C since 2001,
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and we've had multiple different kind of iterations of our product.
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TopRate Composer was kind of an individual ontology modeling tool,
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which I still think is one of the best ontology modeling tools in
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the market.
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that still gets used.
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And then we've shifted over time to more of an enterprise product called Operate
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Edge.
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That was a core flagship product for a number of years that really
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was focused on our enterprise customers that were trying to leverage semantics in their
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broader data stack.
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over the last kind of six months to a year we've been working on that next iteration of that
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product which is called the tq data foundation and it really brings all of the knowledge
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that we've accumulated through kind of the last um you know 25 years or
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so um and brings it and and and brings
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it and builds a product that meets this moment today with the backdrop of ai and
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um what the the kind of the core problem that we
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are all are solving with the tq data foundation is
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How do we help enterprises make the most out of the data that they have?
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And why we're here talking about it today is that
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You know, we are seeing that AI is,
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you know, across our customer base.
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And as everyone knows,
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everyone's investing heavily in AI and the importance of getting,
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you know, getting AI into production can't be understated,
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right? There's a lot of pressure across all organizations to be able to start
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thinking about this and doing this well.
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And we're seeing the tremendous investment that's happening in it.
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But what the thing that we're also seeing is that customers are struggling to
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get.
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value out of these investments that they're making.
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If you look at kind of the chart on the right here,
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we're seeing customers,
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and this is not just our customer base,
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but broader market,
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that really only about 10 % of customers are getting the,
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are seeing kind of the cost or revenue benefits that they were hoping to
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get when they were building,
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when they started investing in AI.
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And one of the core reasons that we see this being
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the case is we drill into some of the...
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kind of the root causes is around inaccuracy,
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right?
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And we see that inaccuracy being a great kind of something that's holding customers
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back from putting things into production.
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And the reason that they're doing that is because they're seeing as AI solutions
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are inaccurate,
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they're not getting the adoption that they would want.
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And they realize that if they lose kind of trusted stakeholders in the process,
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it's going to be kind of harder and harder to put AI into production.
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And on the flip side,
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we work with a lot of
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highly regulated industries where just putting something that's inaccurate into production is
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just not um you know is not necessarily um you
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know it's just not really an option and we spent a lot of time thinking about
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kind of what are some of the root causes of this inaccurate
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And one of the core ones
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that we focus on is the fact that the historical data stack was not necessarily focused
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on, you know,
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it was not built for this moment,
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right?
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We had a storage layer and there were a lot of ETL tools that helped
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kind of build,
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that helped kind of power the application layer.
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And those ETL tools required a lot of manual manipulation,
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manual intervention,
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required,
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you know, a lot of humans.
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kind of scripting and doing wrangling in between to make these
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applications work.
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Over time,
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we saw, you know,
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there's a whole other category of data solutions that came into place.
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Things that help,
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you know, help you help data discovery,
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mastering your data,
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governing your data,
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making sure that you're using it compliantly.
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But all of these things still kind of were point solutions in this
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stack. And if you think about,
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you know, kind of some of the challenges that still exist with this stack,
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where that, you know,
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we had customers that have spent a lot of...
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time and effort and and dollars building kind of you know their data warehouses and
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the data data lakes but you still end up with
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you know you it stores your data but it doesn't
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necessarily explain
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what that data means or how it should be used,
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right? So it's still missing a lot of that metadata in that context.
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Customers invested heavily in data catalogs and the data catalogs were great
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in terms of discovery and to a limited extent,
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helping identify what resources
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um you know what resources you might want to be using things like that but
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it didn't do it doesn't fully solve the problem how does some how does all the
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data i have in the enterprise relate to each other or how should i be using it how does it relate
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to my policy um and what we still see in you know in
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in a lot of organizations is that humans are still the super glue right they're still
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the folks that we still need a lot of human intervention to be able to to
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to to
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reconcile things on the fly,
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reconcile definitions on the fly,
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or kind of be able to bring in some knowledge that's going to ultimately make these applications
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sing.
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And the challenge is that with AI,
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it's just put a huge magnifying glass on all of these cracks.
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And AI essentially has a whole new set of requirements that
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don't necessarily fit into the modern
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stack.
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First,
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it needs kind of explicit machine -readable context,
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right?
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It needs to be able to understand things that potentially are all
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over your enterprise.
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It needs that context in association with...
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with understanding all of the structured and unstructured data your organization has
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um it needs to understand how your organization operates how do the different departments
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or domains interact with each other um and and lastly
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it needs that context to stay accurate for it to be for for a current
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for it to stay accurate right you're
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organization is constantly evolving and um while a lot of the solutions
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that you know um you know a lot of ai can can
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can bridge some of those gaps how do you make sure that as the organization is evolving it is aware
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of those things as well um and these
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gaps in kind of in the in you know when that context does when ai doesn't have
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that those particular things
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One of the things that we're all seeing is that it does guess,
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right? It fills in those gaps and more and more,
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it's filling in those gaps better,
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but it is still gaps that when you're bringing AI
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to your organization,
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it doesn't know your organization and it does need to kind of build,
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it does build some assumptions in there.
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When we think about those gaps,
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it's like, well, why do those gaps exist?
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And we see that those gaps in context exist in a
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few different places.
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One, a lot of that knowledge is tribal knowledge.
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It's in people's heads in your organizations.
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It's things that are not necessarily formally documented anywhere.
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They're not written down.
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And they're subject matter experts that really own that.
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Another area that we see it is it's in hard -coded logic,
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in workflows,
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in scripts,
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in...
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a number of different areas around,
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you know,
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in your organization where you
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build point solutions or point applications where it's kind of embedded.
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And the last place is it's in unstructured formats,
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right?
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Decision traces that are in documents and PDFs and emails and other
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places where you don't actually have it connected to maybe where the source system that
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has recorded that decision.
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And all of that together is the missing context that AI needs to understand your
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organization to be able to fully reason over all of the data
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that you have.
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And that's kind of what we see the next evolution of that
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data stack being,
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where there is a layer in between that is really powering that context.
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It's abstracting a level above your storage
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and your applications to think about what are the core concepts of your organization and
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building.
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a model around them.
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It's thinking about kind of core entities and components
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of your organization and making sure that they're in a single source,
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then they're referenceable.
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Taking the metadata around all of your structured and unstructured
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data and making it and connecting it to those two previous pillars.
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And then layering on top of it all of the different types
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of things, all of the different implicit and explicit policies.
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rules, strategies,
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guardrails that you want so that it makes decisions according
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to a certain set of parameters.
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
And this layer is something that we think is fundamentally missing
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in the data stack right now.
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
To drill a little bit deeper about when we're talking about that context layer
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
and that AI Ready Data Foundation,
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
we see it as four pillars.
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It's models,
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and this is something that we think is conceptual models,
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domain models,
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ontologies.
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Really,
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what are the core concepts in your business and how do they relate to each other?
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references.
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So what are kind of core referential data for
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your organization,
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taxonomies,
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glossaries,
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reference data,
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governed data products that can be served up to
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that can be aligned so that there is a consistent set of references for
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teams and AI.
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
The third pillar here is metadata.
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And one of the things that we think about when we think about metadata is actually
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not just connecting structured data in and of itself,
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unstructured data in and of itself,
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but also connecting those together and as it relates to the models and
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the references that you have.
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
bridging not just kind of the semantic layer of bi or kind of thinking about
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
um you know retrieval for for unstructured content but what
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
about scenarios where you need both unstructured content contracts um you
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
know glossaries things like that but also need that in the context of your structured data to
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be able to really make an actionable decision and the last is
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kind of business logic and being able to machine being able to
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
uh create a machine readable version of your rules policies and card rails
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
um that's kind of the the core
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
um the you know the core four pillars that we that that
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
we see as you know are helping customers you know build it
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
build this air ready data foundation um how that
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
shows up for our customers and where we where we see that and here's four examples that we're currently
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
um working with customers on and
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The first,
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you know,
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
the first is around coding.
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
We've all seen kind of the possibility of what happens when,
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you know,
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
what coding agents like Cloud or Cursor can do,
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and they're really able to build fantastic tooling,
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
fantastic applications pretty quickly.
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
And one of the things that we've seen with some of the customers that we've been working
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on is, while that is the case,
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a lot of what they are,
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when they're using these,
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when they're building agents or using agents to build applications that
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are related to their broader suite of tools,
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
they're embedding a lot of context into the prompts themselves.
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They're,
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you know, information about how a,
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you know,
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for this particular customer,
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if they...
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
um it's a software customer they have a suite of products and they're trying to understand
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
an internal use case of how they can um stave off churn or think
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
about upsells things like that they are still embedding a
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
lot of context into those problems about where does customer information live does it live in these six
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
systems and so every time they're building one of these things and it's been it's really effective they're
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
actually they're hard coding this information in and that is a problem it's
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
problematic in two ways one is that you know
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
You're doing it again and again and again.
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
After you built the churn app,
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
you might build an upsell app or you might build a renegotiation app.
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
And these are things that you're going to duplicate that.
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
The second is that it's hard -coded.
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
And you have to think of a way of actually managing that context over time.
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
So as things change,
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
how do you make sure that you're able to kind of reuse those components?
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
The context layer that this offers them is a quick,
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referenceable layer that helps them build,
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
not only power a whole set of agents,
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
but also continue to build things quicker that doesn't require
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
all of the work of getting every detail right.
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
The second is,
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
you know, an R &D use case that I'll chat through,
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
which is,
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
you know, organizations,
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
large pharma companies that want to,
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that are, that are,
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
that were helping think about regulatory submission and report authoring.
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
And the problem has been,
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
you know, a problem that we're helping,
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
I mean, we've been working with pharmaceutical companies for year solving is you have multiple products.
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
They might have multiple different product names.
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different chemical names over time um compound names they they're they have different
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
active ingredients themes and different you know um
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
in different geographies.
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
All of this information is sitting often in unstructured formats
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
in different places,
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
and there's no single source of truth to say this is all the same.
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
And when you're thinking about some of the challenges
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
in terms of regulatory reporting,
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being able to have that single source of truth or being able to have the
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preferred way an organization wants to talk about a particular set
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of products is really important to make sure that
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the reporting that's generated then is comprehensive.
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
Additionally,
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
you might have internal standards compared to the regulatory external
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
standards that are being used.
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
There needs to be a mapping between those,
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
and that needs to be managed in a way that allows interoperability between kind
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
of the reporting that goes out as well as your internal systems.
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
And that's something that requires a lot of subject matter expertise,
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
and it's not something that is going to easily be given away to.
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
to ai to do and so building those artifacts and managing them in
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
kind of a context layer allows a human in the loop to be able to kind
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
of give this really important context to ai to then be able to unlock
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
use cases that previously were not um achievable um
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
A third scenario here that we're working with customers on,
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
I want a customer on is an optimization kind
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
of challenge, which is if you want,
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
the organization wants to be able to deploy agents to be able to start making decisions
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
around how you might change a project plan,
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
for example, depending on various different outcomes or changes
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Speaker 2 (TopQuadrant How to Build an AI-Ready Data Foundation)
in
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
and how you might change a project plan depending on variables
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
that might impact it.
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
You know, a shortage of labor,
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
a shortage of a particular good,
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
a weather events,
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
things like that.
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
And how can you actually change the project plan in a way that you're going to stay
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
within risk parameters of delivering,
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
you know, on time and at budget.
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
The challenge that you have there is you have multiple different systems that go into this,
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
right? You have an inventory system that has kind of your trade goods.
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
You have a planning system,
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
a designing system,
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
estimation system.
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
And they all kind of talk
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differently.
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
They might have a different set of parts,
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
things like that.
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
When all of that can come together,
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
you're able to...
17:35
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
so we're helping them kind of think of this in two ways one is stitching those systems together and
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
creating kind of a metadata you know a mapping
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
across those systems but also a single set of referenceable trade products that can
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
help
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
that AI can then say,
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
hey, in this system,
17:53
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
this product is the same as this system and this product in this system.
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
And therefore,
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
you can start making decisions on how a particular
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
change might impact a product and the parameters around that.
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
The last here is just one around
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
kind of a credit line optimization.
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
Question ends up working with the top 10 bank who's trying to think of how can they expand some
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
of the offerings that they can give to a customer and kind of a classic example of,
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
you know, they want to be able to run AI over a
18:26
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
more intelligent process over how they extend new
18:31
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
products to existing customers.
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
How do they make sure that they're doing that in a compliant way?
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
And obviously.
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
a highly regulated industry such as financial services that could be fraught with challenges
18:41
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
in terms of what you can and cannot do and being able to
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
make sure that you are doing that in an effective way while abiding
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
by kind of all of the safety parameters and risk parameters that you want to be and
18:53
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
so these are just kind of three or four examples and they're not exhaustive
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
but they're just places where we are seeing
19:01
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
the investment in a context layer actually is able to unlock
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
much higher ROI and much more complicated
19:10
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Speaker 2 (TopQuadrant How to Build an AI-Ready Data Foundation)
use cases.
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
The last and the additional piece on that is it's
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
a reusable foundation where you can build new agents faster
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
on them.
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
And the last is that you can kind of,
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
as
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
the context changes and your business strategy changes,
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
you're able to capture it in a single place and deploy it again and again,
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
deploy that change everywhere so all of your agents are kind of abiding
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
by that new reality that you're living in.
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Speaker 2 (TopQuadrant How to Build an AI-Ready Data Foundation)
So with that,
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
you know,
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
we'll hop into just like a brief demo or pop into
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
our product and kind of show you how we've actually built this for a particular example.
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
Right before I do that,
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
just the TQ Data Foundation,
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
I mentioned it a few times,
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
but the TQ Data Foundation,
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
it's a foundational infrastructure.
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
It's a new product that we've launched.
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
It is foundational infrastructure that translates enterprise data,
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
policy,
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
implicit knowledge into machine -readable context that scales across teams,
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
applications,
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
and AI agents.
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
The way we think about the product is that
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
it does three things.
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
It captures your business context.
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
delivers it to wherever you are building AI,
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
so activates it,
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
and then it evolves it and it grows with you and your organization.
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
So as things change,
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
it makes sure that it is capturing that information and keeping your context
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
current and relevant so that your AI applications
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
don't degrade in performance,
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
your agents don't degrade in performance.
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
On each of these pillars,
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
we're investing in from a product perspective,
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
a few things that
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
a few things that actually make it really easy for you to deploy.
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
First,
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
on the capture side,
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
historically,
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
building with knowledge graphs or building in semantics has been a really hard and tedious
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
task.
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
We've invested a lot in our product so that you can use AI to build
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
this context layer pretty efficiently and quickly.
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
The idea is that you're able to
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
leverage kind of
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
AI to extract entities and build taxonomies,
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
glossaries,
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
things like that pretty quickly.
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
We've also invested in a conceptual diagramming tool that helps bring
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
in external stakeholders,
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
not external stakeholders,
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
but non -technical stakeholders who often have a lot to contribute,
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
but have often been a difficult stakeholder to kind of get into bringing
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
and helping build knowledge graphs given some of the technical nature.
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
We also have...
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
invested in a product marketplace.
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
So to bring external standards and ontologies and vocabularies and
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
models that already exist into the TQ data foundation to help customers
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
that don't, not have to start from scratch.
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
The second pillar here is activate.
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
We're trying to, you know,
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
we're trying to, once you build the context layer,
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
we want to make sure that it is everywhere you are trying to build AI.
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
So that's,
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
we've invested in an MCP server,
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
making it enter,
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
making sure that we can integrate into agent frameworks,
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
as well as where your data already lives in terms of data warehouses,
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
data lakes like Snowflake,
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
Databricks,
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
and various graph databases.
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
And then the last thing is,
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
how can we make sure that it's easy for you to keep this information
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
current?
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
And we spend a lot of time thinking about how do we leverage AI there as well,
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
sense things in your organization to bring that back to your context layer,
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
to suggest changes,
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
making sure that humans have a role in that to say,
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
yep, this is a valid change to the business and to the context
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
layer, and it'll be accepted and disseminated.
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
And ultimately,
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
that leads to being able to stand up fast,
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
deploy it everywhere,
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
and then kind of update it centrally.
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
With that,
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
I'll hand it over to Steve on our product
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
side to kind of give a quick demo or walkthrough of how you actually would do this.
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
Thank
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Speaker 2 (TopQuadrant How to Build an AI-Ready Data Foundation)
you, Amr.
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Speaker 2 (TopQuadrant How to Build an AI-Ready Data Foundation)
And I've shared my screen.
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Speaker 2 (TopQuadrant How to Build an AI-Ready Data Foundation)
Can everyone hear me and see the screen?
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Speaker 1 (TopQuadrant How to Build an AI-Ready Data Foundation)
Looks good.
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Speaker 2 (TopQuadrant How to Build an AI-Ready Data Foundation)
Yep,
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Speaker 2 (TopQuadrant How to Build an AI-Ready Data Foundation)
so thanks for that.
23:21
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Speaker 2 (TopQuadrant How to Build an AI-Ready Data Foundation)
I think Amr went through really well,
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Speaker 2 (TopQuadrant How to Build an AI-Ready Data Foundation)
you know, why AI needs context.
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Speaker 2 (TopQuadrant How to Build an AI-Ready Data Foundation)
I hope that all...
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Speaker 2 (TopQuadrant How to Build an AI-Ready Data Foundation)
make sense.
23:30
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Speaker 2 (TopQuadrant How to Build an AI-Ready Data Foundation)
Hopefully I don't have to convince you of that.
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Speaker 2 (TopQuadrant How to Build an AI-Ready Data Foundation)
It's more now we could show you exactly how to build that
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Speaker 2 (TopQuadrant How to Build an AI-Ready Data Foundation)
context layer,
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Speaker 2 (TopQuadrant How to Build an AI-Ready Data Foundation)
that data foundation to ensure your AI has a context it needs
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Speaker 2 (TopQuadrant How to Build an AI-Ready Data Foundation)
and doesn't create its own context or go off the rails and do its own thing.
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Speaker 2 (TopQuadrant How to Build an AI-Ready Data Foundation)
So I'm going to start with an example of what it would look like for
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Speaker 2 (TopQuadrant How to Build an AI-Ready Data Foundation)
AI to have context.
23:53
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Speaker 2 (TopQuadrant How to Build an AI-Ready Data Foundation)
And then what it would look like without.
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Speaker 2 (TopQuadrant How to Build an AI-Ready Data Foundation)
And then we'll go through the steps of how I got there.
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Speaker 2 (TopQuadrant How to Build an AI-Ready Data Foundation)
So for this example,
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Speaker 2 (TopQuadrant How to Build an AI-Ready Data Foundation)
this is I'm asking a question,
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Speaker 2 (TopQuadrant How to Build an AI-Ready Data Foundation)
which customers can I market a credit card to?
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