Jun 17, 2026 · 18 min · 12 segments
In this episode of the Finovate podcast, host Greg Palmer sits down with Caitlyn Truong, CEO and co-founder of Zengines, one of the Best of Show winners from FinovateSpring 2026. Caitlyn shares her…
Caitlyn TruongGuest
Greg PalmerHost
One of the phrases that came up a lot is data lineage, which obviously is a central thrust of what you're going to be talking about today.

Can you go into a little bit more detail on what exactly you mean by data lineage when you talk about it?

So at its most simple form, data lineage is the tracking of data from its origin to its destination.

And in today's world, I would say that organizations look for data lineage information to help them be able to trace data.

So a lot of the tools in the marketplace today are helping someone know what that pathway is.

And I would share that what Zengens has done is provide this next level of comprehensiveness as well as context and explainability to not just what the pathway is, but why did it exist and how did it get there? And what were the reasons for some of the pathway branches? So in that way, it's more than just the surface level data lineage, but for Zengens, because we get down into the weeds of the code, It's more like data lineage based on contextual code intelligence.

Why is that so important? Why is it important to really get down into the weeds there?

Because the problem that a lot of organizations have in the financial services space, as well as other industries that either are regulated or have applications that started 30, 40, 50 years ago, is the need to really understand what was happening in the core.

So the core tends to be a very central system that manages the transactions, right? It holds all of this data.

And if you are in a regulated industry, you will find that there are a lot of regulations and audit requirements associated with providing evidence to understanding what happened to your data.

So that will literally show up in some of these regulations that are mandated to these organizations.

I would say sometimes that's the stick when you think about the stick or the carrot.

But the other use cases include the one that I would say is the most highest volume one, which is the organizations looking to modernize or get through transformation initiatives.

And in that case, it's the how do we de-risk some of these legacy applications for whatever reason, right? Not necessarily because it's old or non-performing.

But in other cases, it's just, I want to be able to continuously manage this, even though the population is shrinking.

One of the phrases that came up a lot is data lineage, which obviously is a central thrust of what you're going to be talking about today.

Can you go into a little bit more detail on what exactly you mean by data lineage when you talk about it?

So at its most simple form, data lineage is the tracking of data from its origin to its destination.

And in today's world, I would say that organizations look for data lineage information to help them be able to trace data.

So a lot of the tools in the marketplace today are helping someone know what that pathway is.

And I would share that what Zengens has done is provide this next level of comprehensiveness as well as context and explainability to not just what the pathway is, but why did it exist and how did it get there? And what were the reasons for some of the pathway branches? So in that way, it's more than just the surface level data lineage, but for Zengens, because we get down into the weeds of the code, It's more like data lineage based on contextual code intelligence.

Why is that so important? Why is it important to really get down into the weeds there?

Because the problem that a lot of organizations have in the financial services space, as well as other industries that either are regulated or have applications that started 30, 40, 50 years ago, is the need to really understand what was happening in the core.

So the core tends to be a very central system that manages the transactions, right? It holds all of this data.

And if you are in a regulated industry, you will find that there are a lot of regulations and audit requirements associated with providing evidence to understanding what happened to your data.

So that will literally show up in some of these regulations that are mandated to these organizations.

I would say sometimes that's the stick when you think about the stick or the carrot.

But the other use cases include the one that I would say is the most highest volume one, which is the organizations looking to modernize or get through transformation initiatives.

And in that case, it's the how do we de-risk some of these legacy applications for whatever reason, right? Not necessarily because it's old or non-performing.

But in other cases, it's just, I want to be able to continuously manage this, even though the population is shrinking.
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