Speaker 1:
From the Library of the New York Stock Exchange at the corner of Wall and Broad Streets in New York City, you're Inside the ICE House, our podcast from Intercontinental Exchange on markets, leadership, and vision and global business, the dream drivers that have made the NYSC an indispensable institution of global growth for over 225 years. Each week we feature stories of those who hatch plans, create jobs and harness the engine of capitalism, right here right now at the NYSC and at ICE's exchanges and clearing houses around the world. And now welcome Inside the ICE House, here's your host, Josh King of Intercontinental Exchange.
Josh King:
The 2021 markets for the most part are picking up right where they left off last year. The events of 2020 from the pandemic to the lockdowns, they favored large, often public companies that were set up for online business or were able to continue working in a remote environment. The hardest hit they were the small businesses. It's estimated that between 30 and 50% of them will close their doors forever by the time the vaccines have been fully deployed. And while it's important to focus on those short term effects, economists will be studying the impact of 2020 for years. Could this period result in a hollowing out of the economy? Will unprecedented economic pressure push companies to tap the public markets quicker and serve to separate the wheat from the chaff? Or will the next potential marquis NYSC listed companies not be able to get out of the garage to do its Series A fund cycle? Now, with luck and an infusion of cash, maybe we won't need to find out. A well-executed COVID relief package will preserve jobs, opportunity and future prosperity.
Josh King:
In a recent economic paper entitled Has the Paycheck Protection Program Succeeded? Columbia Business School's Glenn Hubbard and the American Enterprise Institute for Public Services' Michael Strain wrote, and I'm going to quote, "The Paycheck Protection Program, the PPP, was the most ambitious and creative fiscal policy response to the pandemic recession in the United States. In this paper," the authors go on, "we present evidence that the PPP has substantially increased the employment, financial health, and survival of small businesses using data from the Dun & Bradstreet Corporation." Unquote.
Josh King:
So joining us today to dive deep into how data tells the story of the pandemic recovery and should be used to navigate the path to the economy of tomorrow is the President of Dun & Bradstreet, Stephen Daffron. Steve along with CEO Anthony Jabbour took the helm of the 180-year-old cornerstone of American business and put the company through a much needed refit. Our conversation with Stephen Daffron, President of Dun & Bradstreet on the company's transformation, the connected universe of data and valuable insights, and how to help manage risk and identify opportunity, it's all coming up right after this.
Speaker 1:
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Josh King:
Our guest today, Stephen Daffron is the President of Dun & Bradstreet, NYSC ticker symbol DNB, and lead the firm through its recent IPO and return to the New York Stock Exchange. He is the founder of Motive Partners and held senior leadership positions at Morgan Stanley, Renaissance Technologies, and Goldman Sachs, and previously chaired SIFMA's Operations and Technology Committee and served on the board of DTCC before becoming President and CEO of Interactive Data in 2013. If that company sounds familiar, it is, because Stephen lead IDS which is now called ICE Data Services through a successful merger with Intercontinental Exchange that now provides the cornerstone of ICE's global data offering. A graduate of the United States Military Academy at West Point with a slew of graduate degrees from Yale. Before his career on Wall Street, Stephen served in various command and staff positions in the US Army. Steve welcome inside the ICE House.
Stephen Daffron:
Thank you, Josh. Good morning. How are you?
Josh King:
I'm great. It's so good to have you here, Steve. As we're recording this episode, small businesses are applying for the second round of PPP loans. Dun & Bradstreet has studied the first round to see how effectively the loans were deployed. What were your findings about its positive impact? And does the second round include any changes that you believe are going to be effective in improving how the money is used?
Stephen Daffron:
Well, you mentioned the work that Glenn Hubbard from Colombia, and Michael Strain from AEI had done. They did that work using our data and building on some work that we had done earlier in the year. Dun & Bradstreet collects data on companies from all over the world. We have 420 million companies in our database both public and private that we track year by year, month by month, day by day, and that lets us have a perspective on what's happening with those businesses. So we've been able to see what's been happening with the onset of the pandemic to these small businesses. And it's been pretty ugly.
Stephen Daffron:
You mentioned before there are economists who are saying that between 30 and 50% will probably close their doors before we're through all this. But the PPP, as I research and as Michael and Glenn highlighted, had a good effect, but it was a stopgap. It did get money to the places it needed to go. But it didn't get all the money that it needed in the right places to get the right kind of effect we would have liked to have seen. We're going to do this again. There's going to be more PPP-type programs going out to get money applied to the small businesses. There are things we can do to do it better this time.
Josh King:
There have also been some highly documented cases of abuse and outright fraud in the first round. In fact, Dun & Bradstreet found that a significant amount of the PPP funds reviewed were misappropriated or granted to companies with questionable credit worthiness that possibly weren't operational. How can data be used to minimize risk without hindering the need to deploy the stimulus to help Americans as soon as possible?
Stephen Daffron:
Well, let's answer that question broadly. First, data is like having a microscope that lets you see things you couldn't see before. You remember medicine in the period of the 19th and the early 20th century, when people didn't really understand about germs, and the idea of washing your hands for a surgeon was outlandish? Now, because we can understand and see both germs and viruses, we now understand how to do some of the things we need to stop them from spreading. The same thing can now be used with data. We can see things by using data effectively that we couldn't see before. We can see anomalies. We can do cross-searches of how people are behaving in the business world, in the B2B world, to see what fraud looks like. And then when you see what fraud looks like, you essentially become an ability to compare everything that's happening in the world with that fraud comparison.
Stephen Daffron:
Now, the models aren't perfect, and we can't find everything, but you're right, a lot of the money that was used in that first PPP effort went to places it shouldn't have gone. And it went there because people when you put massive amounts of money into the economy, of course, there's going to be more fraud. And when there's more money, there's more fraud. What's the old saying about why do you rob banks? It's because that's where the money is. Well, why do people go after small businesses? Well, because that's where the money was flowing into. And we had a chance, we have a chance with the use of data to be able to do that in a much, much more complete way than we ever did before.
Josh King:
You recently sent a letter to the Biden-Harris Transition Team summarizing the DNB findings that we've been discussing. You and I were talking a little bit before we started recording about the incoming White House staff and the incoming administration, the Cabinet selections that President Elect Biden has made. Why is it important to begin the dialogue with the incoming administration instead of waiting until January 20th when they're in office?
Stephen Daffron:
Well, because President Biden is going to have 100 days to get a lot of what he wants to get done started. It won't all be finished, obviously. Going all the way back to FDR, that first 100 days is critical. And so it's critical to hit the ground running. We don't want to wait until after everything is sorted to try to go in and get people to use the right kind of tools that are available now that frankly weren't available even five or 10, 20 years ago. Data and data science has progressed so much that we can see so much more, that if we can get the right people in the new upcoming Biden administration to appreciate the capabilities of data science in ways that frankly haven't been practiced in the past, we can get a lot more done to salvage small businesses that so desperately need our help.
Stephen Daffron:
And it isn't one size fits all, it's got to be a recognition that some of these things that we're doing actually have much greater effect than others. To be candid, small businesses do a better job with the money that we give them if we give it to the right small businesses in the right time at the right way. Applying it always through banks isn't working the way we'd like it to. There are things we can do to apply to businesses that are especially left out, like some minority and women-owned businesses that have been true in the past. There are things we can do to make sure that the administration and the private sector that will be leaning in to help the administration with this work will actually be able to do that more effectively this time than we did last time. It's essential.
Stephen Daffron:
You use the term hollowing out, Josh, but I'm concerned because the vast majority of the growth in our economy, the American economy, but it's true globally as well, comes from small businesses. And if you lose those small businesses, you're losing the foundational growth that's going to support the next generation. And we'll take a generation to recover from the kind of growth we're missing out on now.
Josh King:
Talking about the span of a generation, Steve, talking about the tools available to us now versus those that were available to us 20 years ago, think about 28 years in the rear view mirror at the beginning of the Clinton administration that you had some experience with and the transfer of power. The political and business landscape is so far different today than it was back then, but did your experience working with a governor coming from a small state into a role like the President of the United States inform your current efforts in how to prepare Biden and Harris for the work ahead.
Stephen Daffron:
Well, that was a long, long time ago, and the tools were completely different. Remember, we were dealing in a largely still analog set of data at the point at that time. Now, we're dealing with a completely different data environment. We've created 90% of the data that exists in the last two years. The last two years. The global data sphere, I mean in the data world the data sphere we look at all the data that's being created and all the data that you can access. And in 2018 when we took a snapshot, it was 33 zettabytes. And no one can even understand what a zettabyte is, but trust me, it's a lot of zeros.
Josh King:
Yes.
Stephen Daffron:
33 zettabytes, 2018. By 2025, it'll be 175 zettabytes. And the ability to see that data and to do something with it effectively has changed so much in the last 28 years. We're not just a function of the amount of data that's out there. That's the tsunami that will sweep over you and drown things and you'll lose things. And there are small businesses especially, who aren't equipped to deal with the huge amounts of data that's coming their way. But we can empower them with tools that let them see what they need to see and make better decisions.
Stephen Daffron:
It's again, back to my analogy of the microscope, if you didn't know that germs existed, you wouldn't know how to combat them. Now you can see the germs through the microscope, you can understand what you need to do to address it, we should be able to get small businesses to be able to use the tools that exist now that didn't exist 20 years ago to be able to make better decisions. We don't have to have all these small businesses losing their way. Small businesses losing their way for a lack of capital, that a tiny amount of capital would make the biggest difference in the world. But we know who those businesses are, where they are and we track foot traffic.
Stephen Daffron:
We're doing this now for FEMA, because Dun & Bradstreet started a long time ago working with FEMA to help FEMA understand how to react to natural disasters and how to use the data that's available to say, "Here's the businesses that were affected by that hurricane," or, "Here are the businesses that will be affected by that wildfire," and to be able to help them find and prevent fraud. To go back to your other point, prevent fraud of businesses that portray themselves as being damaged. But now, we can do that same thing on a much, much broader scale because the data is there and the computer science tools are there that allow us to make those kind of judgments that help people avoid the kind of germs that cause these small businesses to lose their way. And now with a relatively small amount of capital applied using data science, we can help those business stay afloat and help refloat our economy.
Josh King:
This passionate student and applier of data that you are now Steve, I'm curious how it all began. In my introduction, I mentioned that you served in the US Army, you went to West Point. I'm curious, what led you there? And how did your West Point education and military service lead you to become this data and technology entrepreneur that you are?
Stephen Daffron:
My first career was in the military, I was a soldier. And while I was a soldier I studied at West Point and I studied economics and engineering and other things. And I was interested in how the data was used to price things. So when I went back to graduate school, I went to graduate school expecting and in fact did return to West Point as a professor. But in graduate school, I did some of the delving into data that looked at pricing for weapon systems, and it led me into building some of the models that allow us to understand why things cost what they cost and how they were sold and how those prices changed over time. Well, models were, when I look back on them now they were pretty primitive, but they led me to be called out into the Reagan administration to work during the Reagan administration on ways to price and export weapon systems.
Stephen Daffron:
After the Reagan administration I went back to teaching, and the itch was still there, except the data was hard to find. And the data that was just coming into digital format at the time was data on futures and options. Futures and options, especially on energy products. You remember that was the period of the oil glut, and the energy prices were fluctuating dramatically. And so I began to build models that became my own approach to electronic pricing of futures and options on oil. It worked pretty well. It worked well enough that the New York Mercantile Exchange came to me and said, "We'd like to take your models and industrialize them for use on the NYMEX." And that became a little company called Access became NYMEX ACCESS and became one of the early electronic trading systems. That led me still further deeply into the data where I went from commodities to I went to Goldman Sachs, and then with fixed income, and then later with equities, and it's been really a career that's been built on reading and understanding where the data was taking me.
Josh King:
You're a teacher Steve. Another teacher that we've talked about on this show was Jim Simons. You served as the COO of his Renaissance Technologies. We recently had Greg Zuckerman, author of The Man Who Solved the Market: How Jim Simons Solved the Quant Revolution, on the podcast. How did your time at Stony Brook working with Jim and the other teachers and professors that he assembled there, shape your perspective on the market, the world and the rest of your career?
Stephen Daffron:
Jim Simons is the greatest mathematician I've ever met, and one of the greatest people I've ever met. He was one of the people who first talked to me about not that I hadn't heard it before, I had, but Jim talk deliberately about not just doing well, but doing good and making sure that you're spending part of your time and part of your intellectual capital doing good things, which is why his efforts to promote mathematics and to do things in the world are such a great role model. And I learned that from him.
Stephen Daffron:
I also learned and this goes back to the data question, Jim had a habit of bringing people together and letting people critique things. He would bring the work, the people, the brain trust together into this big classroom setting and getting people to talk to each other about what they were working on and how it worked and whether it didn't work and how to critique that kind of open conversation, that push and pull that made his business work best.
Stephen Daffron:
I mean, I'd argue that Jim is probably one of the greatest entrepreneurs that's ever been because he took a vast amount of ideas and used them to funnel into a data-driven set of decisions that allowed him to make decisions about the marketplace almost instantaneously. But if all you see is the last piece, you miss the entire breadth of information that he was gathering, assessing, culling, choosing to make that work. What did I learn from him? I'd say more than anything else I learned to be critical of the process and to let people around you be critical of the process to get to the right outcome. None of us are smart enough to do this by ourselves, and getting everyone who has the intellectual capacity to weigh in on it, you can generate a much, much better result.
Josh King:
Jim's unique blend of harnessing and analyzing data created billions in wealth for him and those in the fund. I'm curious, Steve, how the role of data in business and trading evolved over your career? And what led you to Interactive Data, a company that I got to know basically by going up and down Route 128 in Boston and seeing the big sign on that building?
Stephen Daffron:
Well, first, Interactive Data, and I would say they're like Dun & Bradstreet, and I've been a client of Dun & Bradstreet since my golden days. So for 20 plus, 25 years I've been a client. But Dun & Bradstreet had not changed as the data sphere had changed and as a technology had changed, and hadn't kept up with it. The same had been true at IDC and both at Goldman and at Morgan Stanley, and with Renaissance Technologies. Fixed income data was mushrooming, it was dramatically changing all the time, not just in size and shape, but in complexity. The complexity you needed to trade a bond with at Salomon Brothers in 1993, versus the complexity you needed to trade a CDS in 2007, totally different. And companies have to... Companies that deal in data have to be prepared to deal with the dramatic difference in size and shape and complexity of the data. And they have to be prepared to bring the technology to bear that can handle that change.
Stephen Daffron:
I came to IDC at the request of a friend of mine from Warburg Pincus, Jim Neary and a guy from Silver Lake, Mike Bingle, who both came in and said, "We have a company, we see what they have. They have this tremendous set of data and this tremendous potential, but we're not getting access to it. What can we do? And we had the conversation. It was marrying the idea of what the data's doing, how the data is changing, how the data is growing with the technology you have to use it. Because at that time in-memory processing was just coming to the fore, and being able to use that data and build data structures that allow you to make judgments in real time on fixed income products was brand new.
Stephen Daffron:
IDC and now Dun & Bradstreet are both in the same boat. And I'd argue that this is true for a lot of the small companies that are coming to the NYSC in the next decade. It'll be the companies that are able to understand how the river of data is changing, and how the technology they're using to address that river can make people's lives better. Now that we can see what causes the disease, we can see what causes the credit issues, we can see what causes the supply chain issues that we couldn't see before. I'll give you an example. One of the pieces of data that I'm spending a lot of time looking at is air conditioning data, air conditioning data, especially air conditioning data in China. Why? Because I-
Josh King:
We love air conditioning data on this podcast. We just did an episode on air conditioning a couple of episodes ago.
Stephen Daffron:
I love it because of what it tells you, of what's moving and what's not moving, what's working and not moving. That kind of understanding allows you to do things for your clients that they couldn't do before. And back to the fraud, understanding what fraud looks like and showing people here's what it looks like when someone's committing fraud on your business. Here's what it looks like. We now sometimes know what happens when someone steals an individual's identity. What happens when someone steals a business's identity? I can't tell you the amount of time I'm spending though with people who are shocked that the business they want to do isn't getting done when they don't realize the simple things they could do to actually solve it.
Stephen Daffron:
We have a relationship with a lot of big companies like Walmart, like Apple and others who say, "Before you can do business with us as a vendor, before you can put an app on our platform, you need to go to Dun & Bradstreet and have them validate who you are and how you work." And they go to us and then they go to Apple or they go to Walmart, and Apple or Walmart says, "Well, here are the standards you have to meet." They don't understand the data that's available to them to deal with when those gaps happen.
Stephen Daffron:
By helping them see where those data gaps are, in many cases it's something as simple as how are people seeing you? How are the payments that you are making as a business being perceived by the marketplace? Do you understand what it means when that payment isn't there? Or do you understand what happens when the vendor who you're expecting to pay you doesn't pay you? How that changes your role in the marketplace? How that changes how you're perceived as a part of our supply chain? Getting people to see that data, lets them make decisions that keeps their businesses afloat in ways they wouldn't have been able to before.
Josh King:
In our story on your career trajectory, Steve, where we've got to the point of you're at Motive Partners, and you're looking at this opportunity at Dun & Bradstreet and it's similar to I think what Jeff Sprecher's acquisition of the New York Stock Exchange was in terms of being able to take it to the next level.
Stephen Daffron:
On a much, much, much, much smaller scale.
Josh King:
I don't know. But before we get into the recent history, for anyone who knows the Dun & Bradstreet name, but may not be familiar with the firm beyond the ubiquitous DUNS number, can you give us a 30-second history of the firm that began really shortly before the election of Abraham Lincoln, who I think was also an early contributor to the firm?
Stephen Daffron:
Yeah. Actually, think about Dun & Bradstreet as may be one of those foundational companies, the companies that we sometimes take for granted now, but those names have been around for generations, like Macy's and like Morgan, and like Colgate, those companies that have been around literally since the 1840s. Dun & Bradstreet started out with people who were trying to find out who it was good to do business with, how to understand the credibility the people who you were willing to do business with day to day. And they got it by sending couriers out to talk to people about who was a good businessman and who wasn't, who paid the bills on time? And we, Dun & Bradstreet, became one of the foundational elements of how to get credit made to people who were trying to sell things to what was in the developing frontier.
Stephen Daffron:
One of our early credibility assessors was a man, a young lawyer in Illinois named Abraham Lincoln, who would go from town to town as part of his job for Dun & Bradstreet and ask people about who was a good merchant, who's a good supplier, who can you rely on? Another one later on was another future president named Ulysses S. Grant. Another one named Grover Cleveland. Another named McKinley. So we've had four different US presidents who had worked at one time or another. We still have ledgers signed by Abraham Lincoln. But the point even in that purely analog, much, much slower environment was still the same point. It was to gather and organize information, now we call it data, but to gather and to organize data so that you can understand how best to do business, so that you can protect and grow your business for the long run.
Stephen Daffron:
Now, Dun & Bradstreet has been doing it for 179, soon to be 180 years. And that kind of foundation is what makes it possible for us to come to the new Biden administration, to the economy at large and say, "Come to us. Let us show you what we can see, and what we can help you see that could make your business understand how to better do business." We have something we use called the Analytics Studio, where we actually have people: clients, come to us and say, "I need to understand how my business would work in this kind of environment." And we give them a studio that comes in as essentially a secure, partitioned part of our cloud that lets them bring their hypotheses about how they think their data should work, and experiment with them using our data.
Stephen Daffron:
So now you can take a data set that's far, far larger, 420 million companies around the world and say, "Well, how would my approach work if I apply it to this set of clients? Oh, my clients look like this. My best clients look like this. How many other clients are there out there like that?" And we can show them. That kind of clarity, that kind of visibility that they can have now that they couldn't have before is a direct lineage back to Abraham Lincoln walking and riding his mule from town to town to say, "Who's a good businessman? Who can you trust to pay?"
Josh King:
From Lincoln riding his mule, Steve, to the value of data being unlocked through technology that allows artificial intelligence automation and human ingenuity to draw conclusions from the information, how has DUNS transformed? And what does that say about the potential for the rest of the company?
Stephen Daffron:
The DUNS number is like a fingerprint. It says, "I know this company, at this time, at this point." That fingerprint allows us then to build data around it to tell you not just who that company is, who its CEO and who its officers, and who its board is, but also who it does business with. Who does it pay first? Who does it pay second? Oh, and you can collect all the things that are in the private world, because this is not just public companies, these are private companies. Private companies who don't necessarily publish all the information about their company, but we can extract it both from them directly, because many cases they contribute it to us. But also we can extract it from how they interact.
Stephen Daffron:
A large part of what we've done is build systems and capabilities to extract from the digital sphere all the information there is about that company. What are their IP addresses? Who do they talk to? Who do they interact with? Now, that data is interesting in and of itself, but it's built around that fingerprint, so we know we can connect it to that entity. And from that entity who it's connected to.
Stephen Daffron:
Go back to the point about frauds. One of the things you can find when you do anomaly detection on frauds is once you've built that entity around that fingerprint, it makes it much easier to track who's legitimate and who's not. Who's actually acting in a way that's not the way you would expect them to act? Who's saying that they're producing, when in fact their air conditioner's off? When you can find those anomalies, that allows you to do that because you can track it back to that fingerprint. That allows you to both defend your company, to understand how people are interacting with you, but also to just grow your company because now you can say, "I can see who else is out there."
Stephen Daffron:
Once again, one of the most important things that I'm seeing now in account-based marketing is people need to see what other companies look like. And that data that allows you to see what other people look like has to be organized in a way that allows you to connect it back to that fingerprint. We think about this in the American economy, and the area that we're most obviously focused on is the American economy, but it's true across the world. We just closed on a new company called Bisnode that's based in Sweden and Germany, which gives us access to even better data across Europe. We have a joint venture now in China that allows us to see those supply chains that extend back into China. So the breadth plus the depth.
Stephen Daffron:
And the third, the third dimension, and this will sound a little about pollyannish, but the third dimension is what you can do with it. Yes, you could help your businesses grow. Yes, you could help your businesses protect themselves from credit issues, but you can also help people do things that they want to do that aren't necessarily directly related to their business. We have lots of clients who come to us and say, "Tell us which partners we can find who actually have the kind of ESG credibility that causes us to want to do business with them, that can help us find partners who can help us purge the oceans of the mountains of the plastic that's piling up in them. Help us find partners who can help us do good for the climate and the globe. Help us find partners who are doing good for people." That third dimension's important to people. What you can do with data to make the world a better place, and we can.
Josh King:
It sounds tremendously exciting for 2021, Steve, similar I think to the story that you told about what you found when you happened upon Interactive Data. 50 years after first listing on the NYSC, Dun & Bradstreet was taken private for an overhaul. When you and your colleague, CEO Anthony Jabbour, first sat down I guess two years ago, February 2019, to chart the company's path forward, had you already identified what needed to be fixed? And how did you begin to make those needed changes?
Stephen Daffron:
Well, Anthony and I were actually brought together by a famous investor, Bill Foley, who's the head of Cannae and is our Chairman at Dun & Bradstreet. And Bill has a history of finding companies and doing this. He was one of the original creators of FIS. He was one of the people who has... The FNF, it's another one of his companies. Black Knight, another company. And Bill had seen through his own work and frankly, through the interactions with lots of other private equity firms to say, "There's this gem out there that has this massive, beautiful set of data that's not being used effectively and is frankly degenerating." Because data, not unlike a crop, if you don't cultivate it, if you don't take care of it, it actually wastes away. Data has to be moving, has to be growing so it'll be worth something. Data that's stagnant becomes rapidly unusable.
Stephen Daffron:
And Bill recognized that, and spent quite a bit of money and quite a bit of time in the year prior to 2019 looking at the company and saying, "What would it take to actually turn this company around and make it better at servicing its clients and better at producing money for its investors?" And Manav who's the analyst at Barclays actually wrote a letter to the board of Dun & Bradstreet back in 2019 saying, or 2018 saying, "Here's what you need to do." And Manav was very pointed and very direct saying, "Here are the things that need to be done." And Bill and the due diligence team that he had put to work on this, came to the same conclusions that the only way to make Dun & Bradstreet change was to make the change wholesale. And that meant buying the whole thing and taking it private. And Bill put together a set of private equity firms, including mine and Motive Partners, T. H. Lee, CC Capital. Chinh Chu from CC Capital was one of the people who had a lot to spring this deal together.
Josh King:
And we had Chinh Chu on the podcast a couple episodes ago as well.
Stephen Daffron:
He said, "This is what is possible to do if we can get the right kind of leverage." And leverage I don't mean just financial leverage, I mean, operational technology data leverage. And we spent the six months prior to February 19th, putting that team together so that by the time we hit February 8th, 2019, the team was in place. A lot of the people who were coming in were people who had worked for myself or Anthony in their previous lives. Our CTO had worked for me in Morgan Stanley. Our chief data officer had come up from Bloomberg. We brought in people from Wall Street be a part of Dun & Bradstreet to actually make this massive change because Dun & Bradstreet well, for all the beauty of the data it had, was languishing in a lack of organization and technology to bring that data to bear for its clients.
Stephen Daffron:
And so yeah, we had a pretty clear game plan, not unlike frankly, what Jeff had done with IDC to bring the technology to bear, to make the data better, better of higher quality and more readily available to our clients in a way that they could use it. A year later, by the time we got to the beginning of 20, the clients were saying and we were seeing a dramatic change in how Dun & Bradstreet could create value for our clients. We had clients coming to me and saying, "This is great. Can we have more?" So that created the growth that led us to having a much higher growth rate than Dun & Bradstreet had had at least for the previous decade.
Josh King:
Why was last summer the right time to list? And how has the company performed even over such a volatile period?
Stephen Daffron:
Well, two parts to that answer. First is we had a good team in place. So we had prepared. We put Citrix in place. We had paired with part of our disaster recovery to have the ability to work remotely. So we were prepared as the pandemic hit, credit for our forward thinking technology and operations team to be able to allow us to do it. But when that happened, we were able to move with a level of control because that's essential during this period. A level of control to say, "We're still doing the things we need to do, continuing to improve the quality of the data, continuing to improve the reliability of the delivery." So even as the pandemic hit and we had to go into this, the clients needed us more than ever.
Stephen Daffron:
And gain, we wrote the original supply chain white paper in January of 2020, and that just opened up the doors for people who were saying, "Help us do this better. Help us understand how to do this." We launched 31 different COVID-19 specific products to help people understand how the pandemic was affecting particular kinds of businesses. And it was dramatic in terms of how people wanted to use that data to help them manage their way through the pandemic. That's how we got involved with FEMA again, and with the Small Business Administration, and with the Federal Reserve Bank, and with many of the state and local agencies that wanted to say, "Help us to do a better job." We have testimonials from New Hampshire that says that they were able to use our data to help better apply the monies they had to keep their businesses afloat. And at the same time, we are carrying a pretty heavy burden of debt.
Stephen Daffron:
So as we were growing nicely, and had every expectation to continue to grow nicely, and carrying a heavy burden of debt that didn't affect our cash flow, the New York Stock Exchange helped us. We put this together with the idea that even though there was a pandemic on, and even though this was a difficult time, we could in fact deal with the debt problem, grow the equity capability of the company and improve our ability to expand more and serve our clients better. July of 2020, we went public again, and we've been continuing to grow since then, with better capital that we had before, with more capacity than we had before that allowed us to grow and do acquisitions, the Bisnode acquisition, which is the one we've just closed on this last Monday. It was very much a function of that decision to go public in July of 2020, in the middle of a pandemic. And I know that sounds crazy, but it worked.
Josh King:
But you need a currency to do those deals and that's what the NYSC provides. After the break, Stephen Daffron, President of Dun & Bradstreet and I will discuss how the firm in addition to deals like that is transforming and evolving to provide clients with the insights that they're going to need this year in 2022 and beyond.
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Josh King:
Welcome back. Before the break, Stephen Daffron, President of Dun & Bradstreet and I were discussing the effectiveness of the federal government's COVID aid packages, Steve's own career arc, and the process of returning DNB to the public markets. Dun & Bradstreet released a survey recently, Steve, that found business leaders used data to make 16 decisions a day, but almost 50% of them felt their companies weren't armed with the insights they needed. Why is it so hard for companies to make sense of all the data that's surrounding them?
Stephen Daffron:
There are four obstacles that companies need to overcome to make the best use they can of the data that surrounds them. The first one is to consciously avoid availability bias. People who do this have done this for a long time, and they have... Especially the ones who've been successful at it, build an availability bias of what they've known or what they've seen, and therefore they want to use that as the foundation for the decision they're making right now. Which is not a good way to do it all the time. It's a good rule of thumb, it's a good something to rely on as context for your decisions, but not to allow that availability bias which was based on an analog world to drive what's happening in a digital world.
Stephen Daffron:
Second. So many of the small companies especially don't have the computer capabilities, the technology they need to bring the data in to organize it and to shape it so that they can make the right kind of decisions. That kind of technology is available, but especially because for small businesses they may say, "Well, I can't afford it. I can't spend that kind of money to do it." So they need to look out to find places where they can bring data in, in a way that doesn't cost them an arm and a leg. So don't allow the fact that technology is expensive to stop you from thinking about how to look for data. I mean, it could be something you consider, and I'm not saying you have to, you can ever ignore it, but don't allow that to be the only question.
Stephen Daffron:
Third, be conscious of the places of the environment that you live in, in the data sphere. You are more than any place in the world in this current business environment, you're connected to everything around you. And the people you do business with, and how you do business with them, determines how you will be seen and how the data that you have access to will be used to make the right kind of decisions. There's data available to you to understand who your creditors are, who your suppliers are, what your supply chain looks like. That data is available to you if you take advantage of it. And you'll realize who you're connected to matters.
Stephen Daffron:
Yes, there are government regulations that say you can't do business with bad guys. There's the OFAC list that says who the bad guys are. And that's essential, but it's only just the basic. Go beyond that. Look at who you're doing business with and how you're connected to them, and the data that they touch you with becomes part of who you are. That's the third dimension that you need to be conscious of.
Stephen Daffron:
And fourth, and this is one that in this world of digitization and computerization, we have to realize is it's people account. And the people who you have working for you and the people you choose to empower as to how they use the data, will determine what data you get access to. In this remote world where we're almost all of us are working not in the office as we used to work in, but are working sometimes we're only seeing each other on video instead of in person, we don't realize how much more important that makes the people who work for us. The intelligence they have and the intelligence they bring to the data they allow in, and the places they choose to interact with is all the more important. And understanding the intelligence and the expectations you have with the people who are working for you remotely who you may not have seen for the last nine months, but who are making decisions about what data comes in and what data doesn't come in, makes all the difference in terms of how you grow your business.
Josh King:
Talking about working remotely, data can be such a powerful tool to improve business operation and prepare for risk, but it's also an expensive liability that increasingly falls under government law, such as the EU's General Data Protection Regulation. We've seen that the emergence of a global remote workforce has resulted in moving trillions in economic output from relatively secure office buildings and skiff environments, to living rooms and coffee houses, Steve. How has that shifted the conversations around managing, using and protecting data?
Stephen Daffron:
First, it's moved us forward because this is the direction we were headed already. We were moving in this direction. I can remember [inaudible 00:49:23] being at Goldman Sachs, and there were only a set number of terminals and we were all the green screens, and that was everything very, very controlled. But you couldn't create anything from it because it was right there. Unless you could code, you couldn't do anything with it. Now, it's the opposite. Everything is everywhere, both in terms of physically where people are working from, but also digitally in terms of what they get a chance to interact with. That's wonderful in terms of the growth it can create. And it's moving things forward in ways that we're probably, we've leaped ahead a decade in terms of the digital presence that we have now and the creativity that's going with it. That's a great thing, but as you said, it also opens up a lot more avenues for people to come in and do us harm.
Stephen Daffron:
How do you manage it? Well, another little Arkansas phrase is how do you milk a porcupine? Very carefully, okay? That's what you do. You have to be prepared to say, "We're going to do this business remotely, but I'm going to make sure that I have the right people with the right training and the right kind of supervision management so that we're controlling our risk." The golden ticket problem, which is a problem that happens when you have someone come in and almost all of us on this podcast will be using Kerberos as part of our security devices. A golden ticket problem is when someone can counterfeit their Kerberos identity and then go in and do whatever they want to, acting as anyone they can get access to. That kind of identity theft that occurs inside a firm can be prevented with the right kind of care, the right kind of right people, the right training, and the right kind of supervision, and the right software can help you stop that. It just takes care.
Josh King:
I was listening to an appearance that you made in the podcast, The Power of Data. You explained your business as 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." You and I talked about this at the beginning of the conversation. How does your 2020 acquisition of Orb Intelligence and CoAction.com help transition these processes into the digital marketplace?
Stephen Daffron:
And Georgia Maria, if they hear this will say that I'm using their intellectual horsepower to do this. Orb helped us see things we couldn't see digitally about companies. Dun & Bradstreet has always collected, really done a really good job of collecting the physical and analog data on a company. Where a company sits, I know that sounds crazy, but understanding the latitude and longitude of where a company sits is actually makes a lot of difference, especially in natural disasters. And we can collect a lot of information around that company. Who was there. What their financials were. What their people were, even the foot traffic.
Stephen Daffron:
But we didn't always have the ability to collect all of the signatures that came with the IP addresses, the signatures that were digital around the company. There's almost a cloud. Again, going back to the issue of the analogy of germs. There's almost a cloud that surrounds a company, that's identifiable to a company, if you can figure out how to find it and translate it into information about the company, about who they do business with. That made a big difference. Orb did that really, really well. And I think that's given us insights on companies we didn't have before.
Stephen Daffron:
CoAction, same idea, but in a very narrow sense, in this case collections. To be able to say to a company, not just here's the company and here's how they're doing, and here's what they're paying, but here's where this company fits in your overall understanding of the collections world for what you're doing. Because the old analog approach of well, you should be going after the person who owes you the most, that's how you should [inaudible 00:53:53] collections, it's actually not the right way to do it. In fact, what CoAction we've learned, and but the acquisition of CoAction gave us access to was bringing in a better set of digital understanding of what actual risks are assigned to those kinds of delinquencies, and therefore shaping the kind of collections that go with that in a way that reduces your risk in the long run.
Josh King:
So Steve, from the COVID-19 impact index to the COVID-19 commerce disruption tracker, you've armed your customers with such an impressive array of products to navigate the pandemic. Why was DNB so prepared to pivot? And are you investigating how these products can be used for other potential global disruptions due to climate change and other catastrophes?
Stephen Daffron:
I give credit to the people at Dun & Bradstreet, the team who were thinking about this before the pandemic happened. People who were in our European analytics group, as well as the ones here in the US, as well as the ones in Asia, who were thinking about this and writing about it before it happened. Fortune favors the well prepared. Well, we were fortunate because we had well-prepared people who were thinking and writing about this well before the pandemic hit. And so we were able to pivot to that direction.
Stephen Daffron:
And what is next on the horizon in terms of being able to do this better and faster? I actually will tell you that climate change is hard because it's so amorphous. And one of the things that we're trying to do now is getting a much better handle. Neeraj Sahai, who's our International President is the one who's leading this charge, is actually getting a better handle on what's real in the data space about ESG. And so one of the things that we're doing now, again, back to Abraham Lincoln of asking the question of who's a good business person? Who can you trust to pay you? Who can you trust? We're trying to do that and to apply that same kind of lens now to the ESG space, which is build a process that allows us to discern, important verb, to discern which kinds of data that represent real change in companies that are actually working to combat climate change, and which is really just greenwash?
Josh King:
So discerning the tools from the greenwash, Steve. Dun & Bradstreet has been around for almost as long as the NYSC, each becoming an iconic emblem of American business. How will Dun & Bradstreet continue to evolve to serve businesses and clients for the next two centuries?
Stephen Daffron:
Well, this goes back to that question of and I think it's data, and my career has been based on data, so I'm probably my availability bias is showing through there. But I think it's the understanding of what data is there. John Thompson, who's the Chairman of Microsoft, in The Power of Data, the conversation I had with him, gave me a real insight. He said, "It's really more about the data than it is about the software." Why? Well, because the software will allow us to get access to and to use the value that's in the data. But we have to be able to know that that is there and know it's trustable before we can do it.
Stephen Daffron:
How do we understand the data that's there? How do we shape and organize it so we can use it for the right purposes? How do we shape and organize it so people don't use it for the wrong purposes? This is where privacy comes back into the matter. Privacy is both an offensive and a defensive question. Offensive in the terms of we have to make sure that we're doing the right thing to show people that we're controlling how the data is collected, how it's organized and how it's used to protect them. And defensive because we want to make sure that no one's doing something with that data that's bad for us.
Stephen Daffron:
I think the way that Dun & Bradstreet will work in the next, I can't see the next century, but I can definitely see the next decade. In the next decade, Dun & Bradstreet will be seeing things in the digital space that allow us to understand the details of the data that exist all over the world. Not just the US, and not just in the existing data sphere. Not in the existing data sphere of 33 zettabytes, but in the data sphere of 175 zettabytes. Where it's not just air conditioning data, but it's data on everything that we can get that tells us how to best manage our world to make the world a better place, to make it a better place for our clients and our investors and our employees.
Josh King:
Well, Steve, when we get to 175 or 200 zettabytes, we'll have you back on the show and we'll figure out where we go as we make our way to 500 zettabytes. Thank you so much for joining us-
Stephen Daffron:
Thank you.
Josh King:
... Inside the ICE House.
Stephen Daffron:
I've enjoyed it.
Josh King:
Well, and that's our conversation for this week. Our guest was Stephen Daffron, President of Dun & Bradstreet, that's NYSC ticker symbol DNB. If you liked what you heard, please rate us on iTunes so other folks know where to find us. And if you've got a comment or a question you'd like one of our experts to tackle on a future show, email us at [email protected] or tweet at us @ICEHousepodcast. Our show was produced by Pete Asch, with production assistance from Ken Abel and Ian Wolf. I'm Josh King, your host, signing off from the library of the New York Stock Exchange. Thanks for listening. Talk to you next week.
Speaker 1:
Information contained in this podcast was obtained in part from publicly available sources, and not independently verified. Neither ICE nor its affiliates make any representations or warranties, express or implied, as to the accuracy or completeness of the information, and do not sponsor, approve, or endorse any of the content herein, all of which is presented solely for informational and educational purposes. Nothing herein constitutes an offer to sell, a solicitation of an offer to buy any security, or a recommendation of any security or trading practice. Some portions of the preceding conversation may have been edited for the purposes of length or clarity.
