The Power of Data Podcast
Episode 9: Mining, Purifying and Extracting Value from Data
Guest: Stephen Daffron, President, Dun & Bradstreet
Interviewer: Sam Tidswell-Norrish, International CMO Dun & Bradstreet
Hi there, welcome back. You're joined by me, Sam. I'm in New York at the moment and I am joined by Stephen Daffron, President of Dun & Bradstreet. Welcome, Steve.
Thank you, Sam.
Steve, you and I, we know each other pretty well, from previous journeys. You recently-ish about a year ago took over as the President of Dun & Bradstreet, the data and analytics business that you and I work at together. Could you give our listeners a little bit of detail about the journey that you've been on, about your career, and maybe some of the stuff that relates to financial services.
Well Sam, we have known each other for a while and you've seen me through a few iterations. The iterations go back a long way because I've been working a long time. My first career was as a soldier, and then my second career was as an academic.
And then I came to Wall Street by actually building my own company, and then selling it to the New York Mercantile Exchange. And then on to Goldman Sachs, where I worked in the data and the technology worlds, and then later into the hedge fund space where I worked with Jim Simons at Renaissance Technologies.
And then back to investment banking and private equity, first working for John Mack as the head of technology and operations and data for Morgan Stanley and then with the Silverlake Warburg Pincus private equity ventures culminating becoming the CEO of Interactive Data Corporation.
And then after Interactive Data was sold to ICE, we – some partners and I – said we should think about this and start our own private equity fund. Which we did, which was Motive Partners, which is where you and I met the first time. And Motive Partners has been involved in the financial technology and data worlds now for a number of years. And was one of the things that led me into the taking a look at Dun & Bradstreet, because Dun & Bradstreet was one of those anomalies in the data and FinTech world, in that it was a tremendously historical company. Been around for 178 years, but at the same time very much present in what was going on. Had clients who were – all of the top Fortune 500 companies were clients, clients who were important to the day to day operation of the global economy, but at the same time, had been falling on hard times, and had been seen as being a bellwether of the decline of some of the data utilities. So Motive Partners, along with other investors, put together the idea of actually becoming the owners of Dun & Bradstreet. Of actually taking Dun & Bradstreet private and becoming, if you will, a lifeline to the industry by being able to put in new capital, new ideas, new technology into Dun & Bradstreet.
Financial services, which Dun & Bradstreet is a foundational block, have always been cyclic, they grow, they develop, they become tarnished, and then they get turned around and we want to be a part of the next turnaround at Dun & Bradstreet.
That's quite a career. You look way too young to be 180 years old.
That's right. Thank you!
Speaking about being 180 years old, Dun & Bradstreet is 178 years old, nearly that old. Can you tell our listeners a little bit about what we're up to a Dun & Bradstreet to help apply our technology and our data and our analytics and insights to the other industries out there that are changing at a lightning pace in parallel?
Well, first, think of Dun & Bradstreet as having a tremendous vault of really, really valuable data. Think of it as gold. And think about it the vault is being the size of the State of Montana. That's the size and shape of the value that Dun & Bradstreet has access to 350 million active records. Literally billions of transactions are flowing through the databases every day that contain value for the businesses that are our clients.
It is very much like gold in the ground because it has to be mined and purified in order to be valuable. And so what we do at Dun & Bradstreet is we bring that data into a framework that allows people to use it. We source it; 30,000 plus independent sources of data flowing through a massive data supply chain where the data is cleansed, normalized, curated, and then delivered to the clients in ways they can use it with their and our analytics.
And now think about this as, as having taken something out of the ground, purified it so that it’s capable of being used and creating value and then helping the clients use it to create value. Let's pick an easy one. One of the major things that happens to startups and to small companies, is they start up but then they fail because of lack of credit, a lack of ability to get capital to grow their businesses. Why does it fail? Well usually this because they don't have enough information to show the people they want to borrow the money from what they're doing. So, they can then come with Dun & Bradstreet to actually see the data that's out there about how they fit into the credit ecosystem. And by taking their information and shaping that information in a way that allows the ecosystem to see how solvent they are, with our data scores, with our analytics, we can actually enable them to get better access to capital and to grow their businesses.
How important is that? Well, pretty important because if you want to be a small business who's servicing a larger business, say for example, Walmart, you want to be a vendor to Walmart, you want to sell products to Walmart, Walmart will say to you, in order for you to be a vendor for Walmart, you need to go to Dun & Bradstreet. We need Dun & Bradstreet informature to believe that you are who you say we are. We allow people who don't have good credit to in fact, improve how they're seen in the marketplace and therefore improve their credit. And we coach them. We actually can help people see how they're viewed in the marketplace so they can therefore improve how they're perceived in the marketplace and can improve their credit.
Similarly, companies that are changing dramatically because they actually need access to data that tells them what they need to do next. So now you're, you're a startup and you want to market to pharmaceutical companies all across the country, you want to say ‘Well, I want to market to all the pharmaceutical companies all across the country’. Okay? What are you going to do, pick up the Yellow Pages, pick up the phone book and Google them? And say, here's all the pharmaceutical companies I want to market to? If you do that, you're wasting your time.
But if you realize that what you really need access to is the data that would allow you to be more effective at reaching those companies, you come to us and say, ‘I'm a pharmaceutical company, this is what my perfect client looks like, it has this kind of shape, it has this kind of revenue per year, it has this kind of employees, it's this kind of products. We then help you go through all the data that we have, in those millions of private businesses and public businesses that we bring data to. We shape this with our analytics to show you which of those clients are most important to you, could be most important to you, and which of them are most inclined to buy from you. So instead of spending millions of dollars with a shotgun style marketing and sales approach, you're instead able to narrow your focus only to those clients who are most appropriate for you, and who are most inclined to buy from you. We dramatically improve the efficiency of your operation simply by giving you better access to data.
So let's talk about the Power of Data. And a lot of the narrative we talk is around helping companies increase revenue, helping companies increase margins, and helping companies remain compliant, so they can do it all again. Let's talk about the compliance aspect. Okay, how do we help companies manage risk? And if you're talking perhaps to say, an executive from a large bank, how do we help them do that?
Well, it's the reverse of the coin for the marketing side. Just like we help companies find the best people to sell to. We also help companies find the best people not to do business with. So for example, if you're a bank, and you know that you're faced with all the issues of money laundering that are rife in the world today, you want to know all the people upstream from you and your supply chain. People you buy data from, people you buy information from, people you buy computers from, people you lease buildings from, people who you use to do your searches, you want to know if those people are in fact all on the right side of the law. We help you with that.
Because we have those people, you know, not just identified as individuals, but identified as being as a part of the family tree. And from that family tree is a part of a larger ecosystem. And we can help you literally walk back up that supply chain and show you all the places in that supply chain where those people have had a compliance issue with the German government, or where those people have failed to pay on time, on target, or where those people have been accused of defrauding their clients. So that you are the recipient of all the information we can glean from going up the supply chain and showing you who you don't want to do business with or, at least, you want to go talk to people about who you’re doing business with make sure that they are the ones that you care to do business with if they're not compliant?
Now, one of the things that many people do when they want to ask these kinds of questions is they say ,well, how do you get this information? Well, most of this information is out there and publicly available, where it's not publicly available, we go source it, we go find it, we go bring it together, and then we analyze it. Much of this data is in fact not readily apparent to anybody else, because they don't have the right kind of, of analytic frameworks to bring the components together to know whether in fact the violations are being incurred. When we think about how to figure out who the bad guys are, it's both who they associate with, where they get their information from, and what they do with it. And that ability in our third-party risk and compliance products, is what allows us to do so.
So data is all about the science. And one of the things that became very apparent to me is the area of unique capability that Dun & Bradstreet has specifically is actually about linkage. And the D‑U‑N‑S number is at the very core of that. It's almost the immovable object that makes this company truly special. One of the areas that I was most encouraged by when I began working with you was around the patent aspect of the business. So we are preparing for all sorts of technological innovations. We have patents around distributed ledger environments and linking our D‑U‑N‑S number to new environments and new areas of innovation. This industry is moving at lightning pace. Business Intelligence is unquestionably one of the most exciting spaces on the planet. But there's a ton of innovation happening. And it's not just happening within our firm. Can you talk a little bit about some of the data and analytics trends that you're seeing?
Best way to illustrate this is to think about the sources of data as being a galaxy. There are literally billions of sources of data that are out there that are in fact available to us. But the ability to source those billions of elements and bring them into a coherence with each other is really, really hard. So bringing technology to bear on how to bring them in and bring them in relationship to each other. In many cases, they're completely unstructured. Some of them are simple, as transformative, as taking a video, a video of a plane flying over Tanzania, over the farmlands of Tanzania, be able to take that video and turn it into data that's usable and searchable so that people can make the decisions about what gets watered, what doesn't, what gets fertilized, what doesn't, what gets bought, and what doesn’t, bring all that data into a usable format from those billions of sources, and then take those billions of sources, bring it through a pipeline, process it in a way that is, you're trying to keep the characteristics of the data separate. Every time you make a copy of a copy, you're losing some of the functionality, some of the variants that makes the data important. So bringing it through in a way that keeps that variance very much mathematically present. So that you can then run analytics on it.
Let’s point to an example. So if I have brought data through that allows me to understand the revenue that's associated with a particular business. Now that revenue is a number, it has variation because it's related to sales, it relates to the number of employees it relates to the market you’re selling to. Can I take all that information and use that to show what that business is about to do? Can I apply analytics that did not just see the snapshot of what is, but with that predict what will be coming. So this is intent data. So I can now not only turn to my clients who are in my sales and marketing space and say to them, ‘this is what the clients who you care about look like’. I can also show them what those clients are doing over time, through their financials and through their movements, and therefore whether they are likely to buy. The intent data actually improves the likelihood of your getting a sale through a particular marketing plan, through a particular marketing campaign, and through a particular sales call.
All that comes from those billions of sources of data, being properly transfixed in a way that maintains the variance and applying a very sharp level of analytics to it. Now, the billion sources of data also reflects in the billion receptors of that data. Because once you find someone who fits those criteria, and you can sell them that, you think of the ability to go look and use your analytics to find all the other people who are like that around the world, so I can sell this in Omaha, I can also sell it in Oman, because I can find those characters in both places that I couldn't have before.
So let's talk about the sources for a moment, right as the Internet of Things comes online. And you and I were with David Rubenstein earlier. David Rubenstein was talking about the fact that, in trends, things don't happen anywhere near as quickly as you think they would in two years, but then over 10 years, things tend to happen a lot faster. The Internet of Things is finally online, and as your car talks to your fridge, talks to your bank account, data is being spawned up everywhere, and it's exponential. I think IDC your previous firm said that every year 50 times more data is created than the previous year. That's terrifying. What are the sources of data that we need to start thinking about?
Glorious, you're creating value, that data that's being spawned up is data that could be used to create value, could be used to save lives, could be used to make the world a better place, all we have to do is figure out how to do that. The data that's out there is latent energy, its energy that's waiting for someone to figure out how, a way, to get to it. So that creates value. What we need to do is to figure out a way to bring that latent energy to bear and all those sources of data, and let's use the Internet of Things as a good example. So one of the things that I care about is climate change. So one of things I can do with the Internet of Things is I can start seeing where the places are that are causing the greatest effect on climate change down to the building down to the automobiles down to the individuals and what effect they're having on climate change, and then aggregate that up to say, well, that's a leads building, that's a building that should be in fact, capable of supporting a net zero carbon footprint, but it's not. Why isn't it? It's the ability to take that data and do something intelligent with it that will allow us to gain more traction. on doing the right thing, I'm convinced that if we have data that’s used properly, that people understand the right thing to do, they will do it. I have this optimistic attitude that now that we see the problem is there, and we have the ability to actually bring tools to bear to fix it, that we will fix it.
That's awesome. Thank you. And climate change is a particularly niche example, one that the US President currently doesn't believe in. But it's a real-world example of something that needs our attention that data can really help. The Power of Data is endless, right? We know that, we've been discussing that. But one of the things that strikes me as an area we need to think about very long and hard as this industry expands and grows is how we source that data, not just the sources and what we use with it, but how we source it. What's your perspective on ethically sourcing the data?
Well, the ethical sourcing and use of data is one of the foundational elements of why we do what we do. Noth personally and professionally, I think that one of our credos has to be if we're going to do something we should do something to make the world a better place, and at Dun & Bradstreet, we take that really seriously. We want to see the ethical use of data as being the core foundational value that we pursue in every avenue.
Let's take an example. We have the ability to know where things go wrong in the data chain. So when you are doing something in climate change, where people are actually causing damage, it's possible for us to know that. And the Internet of Things allows us to see salinity, that allows us to see foot traffic, that allows us to see traffic patterns, allows us to see vehicular traffic, that's all data elements that can be used to actually be a better citizen of the world.
But we have to be conscious of how we collect it. Privacy is a fundamental right of human beings. And if we, in order to be the best kind of company we can be, to be supportive of the ethical use of data, we have to be very conscious of not violating privacy laws, privacy norms, as we collect our data. One of the things that happens, unfortunately, is that people don't guard their privacy. There are people who click on OK to allow people to go into their databases and collect their data, both their personal data and their company data, and then use that to inform a database that's being sold. I think that's unethical. Why? Well, because you're actually impinging on the privacy of the person whose data you're taking. But just as importantly, you're also impinging on the privacy of all the people who that person is corresponding with. Because when you're compiling data coming out through that person's network, you're not only taking that person's privacy and putting their privacy at risk, but anyone who talks to them, because you're putting their data, their URLs, their websites, their phone numbers, their personal information available to whoever that company sells it to. I think that's unethical.
We should be conscious of who we are taking data from, when we are doing, how we're doing in a way that's in line with the terms and conditions of the website if we're in fact scraping, or if we're in fact taking downloads, it's done deliberately. And with a deliberate acceptance, not a just click okay about some junior person in the firm, but a deliberate acceptance of the legal ramifications that goes with having someone give us their data.
That's where privacy extends so far beyond permissions. Human beings don't tend to understand a lot of the stuff that they're engaging with, you know, a great US entrepreneur once said if you ask the people what they wanted, they would have said a faster horse. And instead, we gave them the car. I say we, Henry Ford did. So yeah, you're absolutely right. I think there's a degree of misunderstanding about what this world is doing. And it's the responsibility of the companies, the stewards of this data to do that. It won't surprise our listeners to hear that, as well as the President of Dun & Bradstreet, you’re also the Chief Data Officer, you've taken a very hands on approach to this stuff, which is, I think imperative, and it's great news for the company. But it means you don't have a lot of time if you had more time, what would you do with it?
Dun & Bradstreet is a great company. And a great example of a company that can be refocused revitalised to do more and to do better by having a focus on data analytics technology. There are other companies out there like that, that I'd like to connect the same kind of drive, the same kind of insight that we see at Dun & Bradstreet into those companies. Why? Because I think the value that Dun & Bradstreet can bring to our clients will be greater if we can actually kind of spread the wealth of how we do this to other companies. Now, either through partnering with them, through acquiring them, through licensing our data, our analytics to them, or, frankly, by buying access to their data and their analytics, so that we can bring them into the modern age. We've recently acquired a company in San Mateo, California called Lattice, which is a tremendously interesting company because they've been so forward-looking, they’ve had such clear thinking about what to do next, with how to make the world a better place. And we want to bring them into the fold and find more companies out there like them. So we can bring them into this idea of both making data more available and making data be more present for doing good.
One of the things I love about spending time with you, Steve, is your analogies. And you often come out with military analogies. I come from a military family so I’m probably a little bit biased. But what lessons can you take from your military background that you are applying to data and financial services?.
The story of Marshal Ney during the Battle of Waterloo, when Napoleon sends him to sweep the British battery off the field with his cavalry, spike the guns and withdraw. And Marshal Ney, a brilliant cavalry officer, brilliant officer in fact swept the field, went around the flank and took the battery and sabred the gunners and drove them off. But no one brought the nails to spike the cannon with. So when the British infantry came back, they drove off the French cavalry, and the guns were back in action. That story tells you that when you go to seize the cannons, don't forget the nails, you have to be prepared to remember what the mission is that you're about. There are lots of people out there in the private equity world who talk about how to make a company like Dun & Bradstreet better. But the thing that makes it work is remembering to bring the nails, is remembering o say what are you here to do? It's to make the data work all the way through from the source to the client every time. The military analogy is apt because all too often people get bound up and saying well I did this or that and I created this kind of brand awareness or I created this kind of client influence. We didn't deliver the goods. We have to be able to, in Dun & Bradstreet, make sure that we're actually delivering the data in pristine form, on time, on target. We have to remember the nails. It's not enough to show up. It's not enough to be there, it’s not enough to make an impression. It's not enough to give a speech, we actually have to have the people who are writing the code, who are doing the operational deliveries, who are answering the clients’ questions and make that happen every day. That's what I would say I learned. Follow through, deliver every time.
That was perfect. I don't think I can get any better than that. Okay, Steve, thank you so much for being on the Power of Data podcast. It's been an absolute pleasure.