How Dun & Bradstreet Is Building AI on a Trusted Foundation

Hey, everyone. Welcome back here to Text Trump tv. My next guest is Mr.

Mike Manos. Mike is the CTO of Dun and Bradstreet. I know a lot of you have probably heard of Dun and Bradstreet, but I don't think you really know what Dun and Bradstreet is today.

Mike, welcome to Techstrong tv. It's great to have you on, Alan. It's great to be here.

Thanks so much for having me on. Fantastic. Hey, I'm really looking forward to talking about what Dun and Bradstreet is today, what they're up to, how AI is, is, you know, changing the equation perhaps.

But before we get to that, I always like people to understand who's talking to 'em. I mentioned you're the CTO at DMB, but what, you know, how'd you get here, Mike? What's, what's your career path been like?

Yeah, I think, uh, if I were to categorize it, it would be a little bit of, uh, uh, un uh, undiagnosed a DD meets technology career. Uh, and kind of went all over the place. So I came, uh, I came out first as a developer, moved into, uh, big infrastructure and networking, and then, uh, slowly progressed through my, you know, building out data centers and, uh, you know, uh, throughout my career.

So, you know, I've, I've have, uh, I have the benefit of today working for Dun Bradstreet, uh, a company that's I think, pretty well known, both nationally, internationally. Prior to this, I was the global CTO at Fiserv, one of the, uh, largest FinTech companies, uh, in the world. And, uh, have done, uh, executive stints at, uh, at, uh, a OL Disney and, um, uh, Microsoft, uh, you know, prior to this.

So, uh, it's been a, it's been a great journey. Originally from Chicago, uh, so used to the cold weather, but living today in Jacksonville, Florida, which is, uh, not as cold. So, I'm, I got you.

I'm, I'm from New York where I'm down in Boca Raton. It's, oh, no, it's not as cold, but we've been cold the last couple weeks. You have too.

You've been incredibly, like below freezing cold up there. Um, it's been pretty bad here, but what a great resume, Mike, you know, you've been, you've been in a lot of big, big time, you know, serious enterprises. Congratulations.

How long have you done in Bradstreet? Uh, this is, I've, uh, just complete, I've been in my fifth year just getting close to my fifth year here. And when I got hired on here at Dun Bradstreet, it was really focused on driving change and transformation, really around modernization.

And so I, if you were to sort of characterize the journey over the last five years, it's really been about how do you take a 180 6-year-old company, uh, and move it to, uh, the most modern bleeding edge types of things of launching AI products and, you know, moving out of traditional data centers into the cloud and, and all of these kinds of things. So it's been a, it's been a pretty incredible journey of that modernization for sure. Absolutely.

186 years. You know, when you think about it, they say, if you go back, I think it's 50 years. Like there's no companies from the Fortune 500 today that were there 50 years ago ago.

That's how quick the, you know, the, the turnover is in, in, uh, corporations. 186 years is, is is a testament. I think when I started Alan, uh, I think we still were using, uh, Abraham Lincoln's typewriter to do, uh, you know, access in our data center.

Absolutely. And now we don't even have 'em on pennies anymore. That's right.

But, um, you know, Mike, as we mentioned in the outset, and I think is what you've alluded to a bit, is Dun and Bradstreet, like many organizations, especially in this AI world era that we live in now, has had to reinvent itself. Yeah, Yeah. Right.

So let's, let's talk a little bit about this reinvention, what, you know, and, and it sounds like you are the instrument of change over there, right? You're driving modernization, uh, at Least at the technical side, at least on the technical side, there's, By the tech side, you're right, you're right. So that's what we focus on.

I would not for the rest of it. Yep. Well, if the tech leaves, the other stuff follows, I always think, yeah.

But anyway, Mike, tell us, you know, I, and I'm gonna assume AI plays a starring role. It seems to be the theme, but tell us about today's Dun and Bradstreet. What do people, what should people take when they hear the name Dun and Bradstreet?

What should they think? Yeah, I, I think it's one of those names Ellen, where, um, everyone has heard of Dun and Bradstreet probably for a very long time, but they don't actually know what Dun Bradstreet does. They probably associate Dun Bradstreet with the Dunns number, which you, you know, historically would need to have to get a tax ID with the US government or, um, to sort of validate or verify that you are a real business, uh, or in some cases, uh, especially in the small and medium business side, to be able to get loans, right?

Our, we have a, we have a score called the Paydex score, which is kind of like the, the, uh, the credit score of a, of an individual, but for businesses, which is what something we maintain. But I think if you go back, uh, all the way to the beginning, all 186 years back to the beginning of the firm, we've always been involved in data. I made a joke earlier about, um, about Abraham Lincoln's typewriter.

And you know, I say that because we've had, uh, a bunch of US presidents, Abraham Lincoln, uh, Ulysses, US Grant, a bunch of other ones that have worked for Dun and Bradstreet. And in every case, what they did is they would ride their horse into town, figure out who the grocer was, who the blacksmith was, who the, and they would collect that information, bring it back and create, you know, lists and, and take that data, although it wasn't necessarily called data back then, um, and compile that for the state governments or the, or the federal government at the time. And so, dun and Bradstreet's always been about data.

It's always been about the collection. It's always been about the accuracy of that data, uh, and it's, and, and about going out and getting that. And so over the last 186 years, whether however you've categorized what Dun or Branch trade was or what they did was always about going out and getting data.

And as you know, AI is great, but AI is nothing without data. And I think that's where these stories start to intertwine with each other. Um, you know, if you think about where we've come, we now do business in over 200 countries around the world.

We have, uh, what I call our most valuable natural resource, which is the trust of all of the governments of those, of, of those entities, plus the businesses, um, within those countries, um, to make sure that we will handle that data appropriately, that we will be regulatorily compliant, that we will, you know, honor the, the requirements, et cetera, in terms of the, the different views. Um, and so when you think about just the size, I know we're on Techstrong tv, but you think about like we ingest, you know, a little bit, well, actually a lot north of about five exabytes of data every night. We process that data every night.

We categorize that data, we pro, you know, we process it, we put analytics on it, we get other data around, around that data and the metadata. We put that back into our, into our data. So the data supply chain that we have is absolutely, uh, world class.

And when I say world class, it's because it spans the world and it's compliance to all of the differing, you know, regimes as a computer science, uh, person, everything needs to be efficient. But when you start getting into things like data on the global stage, uh, it becomes a lot more human, uh, or more human factors that you have to factor into, which makes it, um, uh, less efficient, but a, a more interesting harbor problem space to solve for. And so, when you think about down on Brad Street today, today, uh, we are one of the largest data, uh, inges normalizer.

And then, uh, we take that data and we sell that data, or we, we market that data for businesses to use in their own business applications. We do that for, um, risk businesses, for credit businesses, banks, large financial institutions, sales and marketing businesses. Um, and, you know, the, the sheer volume of that data that we consume is, is mind-numbingly large, if you think about it.

So all of that comes into play as we start to talk about ai, because when you start talking about ai, it's great. When you're thinking about a commercial application, or not a commercial, a sort of consumer application of ai, it's, can I make a funny picture that has me dancing like a bear or, you know, I, I'm, I'm being somewhat flip flippant about that. But when you start getting into, um, commercial grade, enterprise grade use cases in ai, you need commercial grade, enterprise grade, uh, data to power those decisions, uh, through the AI interfaces.

And that's really what we've concentrated on over the last five years. So, um, you know, we've modernized, I, uh, we started five years ago, we still had data centers. We're now a hundred percent in the cloud.

We're now, you know, uh, you know, if you think about, this is sort of the last decade's problem, right? We're now a hundred percent in the cloud and we're now, um, delivering out of different regions around the world. Uh, we have this new architecture and we've launched probably over 20 different AI products, specifically focused on the AI delivery of that data, uh, to our customers.

Settled mouth a lot. Couple of mouthfuls there, Mike. Lets, I'm sorry.

No, it's okay. Let me, let me jump in here where I can a little bit. First of all, I, I would put forth the proposition that Dun and Brad, she doesn't just make that data available to partners, customers, third parties, but it's the analysis that you provide, right?

In ingesting that data and analyzing it that makes for, you know, I used to call it actionable intelligence. Yeah, right. That people pay the money for, right?

That's your premium money. But, you know, it's an interesting thing. I, I did an article a couple weeks back.

One of the, like, godfathers of AI says that we're never gonna reach super intelligence with the current LLMs. Part of the reason is, is that the LLMs that all these frontier models are running, you know, they've scraped all the data they could scrape right? Publicly available data, but yet they estimate that 90% of the data that could be accessed via the internet is behind corporate firewalls, is, is not publicly available.

I would imagine in Dun and Bradstreet, of all these exabytes of petabytes of data you guys are ingesting, there's a real good chunk of it that is public, but there's also probably a real good chunk that's private, especially when we talk about the analysis, you know, the analyzation that you guys do, and that using that data to train a model or to use it for inference, like with RAG and, and other kinds of AI technologies, allows you to make a, a better mouse trap, if you will. Yeah. Than, than what you might get, you know, just with a frontier model.

No, absolutely. I think, I think if you think about our data, all the different feeds, it's that highly curated private proprietary data that makes the biggest difference. That's actually what most of our customers come to us for.

It's not necessarily just the public data we have that we normalize that we have, people have probably heard of the DUNS number, right? So the DUNS number mm-hmm. Sort of like the, the ultimate index around a business.

Uh, and we can get, you know, ultimate beneficial ownership of who owns it. And we can, we have all these different factors around the public data, but we can then merge that and, and sort of enrich that with a lot of private data and a lot of differing data sources that really give you the real value of that. And I think, um, public data is fraught with, uh, lots of, uh, you know, there's, there's inaccuracies, there's, uh, you know, there's, uh, regulation around what can be sent, where it can be sent, who can store it, where can it be stored, all these different things.

And so if you're a commer, if you're an enterprise consumer trying to take this data in or make decisioning on a, you know, from an AI perspective, to do that, you need to make sure that your ability to hallucinate is closer to zero than one. Uh, and I say that through the AI lens, right? So for, you know, in our world, a hallucination is a wrong answer that might cost a bank millions of dollars on a credit decision, right?

So you have to be, you have to be so focused on making sure that the quality's there, the, the, it's the veracity of the data. It's not just the volume of the data, it's the veracity, how good that data is. Do you have the lineage of that data in terms of, can I back that up that I got this from a reliable source that would be a good source of those things.

And then also on top of that, it's, it's, you know, making sure, and this is something I think it gets lost a little bit now today because of the, the hype in, in such it's around ai, but when you start thinking about the security around AI and do you have the entitlement to use just because you have this data, do you have the right to use this data in the way that you want to be able to use it? Or that you've been permission to use that? And when you start getting into things like, you know, identity and access management, but at a, at an agent level, for example, um, it really gets super complicated.

And that's where things like, you know, we've spent a lot of time driving, uh, uh, a big global platform that's standardized, that ensures, uh, both compliance, uh, to security regulations around the world, around the data as well as the, uh, the on-ramp for us to be able to launch products very quickly, you know, launch MCP services around different kinds of data, but also make sure that our customers have the confidence that the data that they're receiving is, is, has all of the characteristics that they need to make sure that they can make the smartest and best decisions in their use cases. And that that data is something that is trustworthy and compliant, you know, on a worldwide stage. I think that proprietary component of that data mixed with the public, unified on the DUNS numbers, which, which, you know, people all over the world use the DUNS number as the index is absolutely key and, and a big differentiator for us actually For Absolutely.

Hey, Mike, we're about outta time 15 minutes ago. So quick here, I apologize. com is the main Dun and Bradstreet, uh, website.

Where can people go within the website though, to kind of see sort of, you know, dun and Bradstreet eating its own dog food, living this AI experiment in real time? Where, where would be the best place to send them? com today, and you looked at our website, there are AI journeys.

We talk about our chat DB, which is the ability to kind of interface with that data. And we have various journeys and various use cases that you can follow on that website, uh, you know, at any time to, to, to get a little bit of a, a deeper insight for sure. Very cool.

180 6-year-old company reinventing themselves right before our eyes. Right. Good job, Mike.

I have, I keep up the great work. Come back and keep us posted on what's going on there. Will do.

Thank you so much, Alan. All right. Mike Mano, CTO, dun and Bradstreet here on Tech Drunk tv.

We're gonna take a break. We'll be back.