Episode Twenty: Democratizing Data

How Data Can Add a Competitive Edge

Understand your relationships, understand how the impacts are changing, and adjust your business strategy to adapt to those market conditions.
 

Stephen Daffron, President of Dun & Bradstreet joins this episode to share more about the Firm’s new Analytics Studio. He discusses how traditional and non-traditional data can be used to support business goals, drive competitive edge and help businesses understand the impact of events such as coronavirus on their supply chains and business operations.

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The Power of Data Podcast

Episode 20: Democratizing Data

Guest: Stephen C. Daffron, President of Dun & Bradstreet
Interviewer: Sam Tidswell-Norrish, International CMO Dun & Bradstreet

Sam 00:00
Hi, welcome back. You're joined by me Sam and Stephen Daffron and president of Dun & Bradstreet. Welcome back, Steve.

Steve 00:05
Thank you Sam, I'm glad to be back.

Sam 00:06
For those that haven't tuned in before, I've had the great pleasure of doing many podcasts with Steve, each time it gets more interesting, each time he gives another military analogy for me to think about for the subsequent months. Steve, can you tell our listeners a little bit about who you are and what you've done?

Steve 00:22
Well, I'm the president of Dun & Bradstreet. I'm part of the team that took Dun & Bradstreet private almost exactly a year ago. I'm very proud to see the progress we've been making over the past year. Before Dun & Bradstreet I was and still I'm a founding partner at Motive Partners, which is one of the private equity firms that invested in taking Dun & Bradstreet private. I’ve been in private equity for a number of years. Before that I was in the investment banking space for Motive in the investment banking space with Morgan Stanley and also before that with Goldman Sachs and with the hedge fund space with Jim Simons and Renaissance technologies. So I've been around a long time.

Sam 00:54
Steve last time we spoke we finished on a profound analogy about bringing the nails. And that point was about being execution focused and getting the job done, particularly when you're right at the end, and people are already doing their celebratory lap, but they haven't put the icing on the cake. Through 2019, we made some significant changes as it relates to our data and the responsible sourcing of it. Can you speak to how we've converted that change into positive outcomes for those we serve, for our clients?

Steve 01:22
Well, the realization that the size and shape of the data universe is growing all the time. So think back to the Big Bang Theory, wherever they expands out from that. Data is growing at that kind of pace, especially in the commercial world where more and more types of data, more and more size and shape of data for the business to business space is growing dramatically. There are probably 400 million businesses in the world and Dun & Bradstreet captures the vast majority of that. And you need access to multiple types of data, as well as advanced analytics to understand it. But what Dun & Bradstreet has been doing for 178 years, and we've been focusing on here at Dun & Bradstreet for the last year, is making strides to provide our clients with the best in class view of that data, and give them a competitive edge, and how to understand and use that data to advance their businesses. We've acquired Lattice Engines back in July, which is AI powered statistics that allow us to understand where the clients marketing sales dollars should go more effectively. We've acquired Orb more recently; Orb Intelligence is actually a firm that we've acquired to improve our ability to have a digital as well as a physical signature. Dun & Bradstreet links the digital and physical world with Orb. We've dramatically expanded our access to data that is considered alternative data or non-traditional data, so as to bring things into the into the picture with our clients that they hadn't seen before. And we've done all this at the same time while bringing on board a new Chief Data Officer. With those who have met him Gary Kovacs is our new Chief Data Officer. We just hired from Bloomberg to come in and help us organize and deliver this dramatically expanding universe of data.

Sam 02:57
Steve, our firm has been around for a long time. I had the recent pleasure of meeting the leadership team of Alibaba, one of their mission statements is they want to be 102 years old so that they can say they've spanned three centuries. It's a lofty ambition. Our company Dun & Bradstreet has already spanned three centuries. It has a really rich past, but all of that was for one reason, to point us forwards. So perhaps you could tell us a little bit about as President, how you're ensuring Dun & Bradstreet continues to service our clients’ needs and continues to be a game changer in this space.

Steve 03:32
Well, data continues to grow and expand and become more and more complex. Clients, if we don't do anything to help them, could sometimes be like a deer in the headlights. They can’t actually appreciate how the data is changing and what they need to do with it. So one of the things we've done, as we work to build an analytic studio for our clients, where we go to them and we say, “clients, this is what's changing. You need to be able to see the changes in real-time, across all your business dimensions. Understand your relationships, understand how the impacts are changing, and adjust your business strategy to adapt to those market conditions”. How do you do that? And frankly, for most of the time, in our lifetimes, they haven't been able to do that. To solve for it. We want to give our customers tools where they have ready access for the entire business universe. So think of this as a hugely powerful Hubble telescope to be able to look at the galaxy and see all the rich business insights like the financials, the payment, history, spend activity, and all those signals, like intent data, so that customers can then analyze trends, identify predictors, they can adjust and build or optimize models and then quickly operationalize them. That's what we're doing. That's what really over the last year we've been working to put together something called we called the D&B Analytics Studio, to give our customers all our data, all of that vast amount of data that Dun & Bradstreet has access to at their fingertips and additional all but non alternative to the alternative data, the non-traditional data that's out there and their own data. Sources for them to experiment and evolve their own approach to the business solutions.

Sam 05:05
So this is a totally new topic. And we're gonna dig into this pretty deep because this could be revolutionary for our clients. The Analytics Studio sounds to me and forgive me if I'm jumping the gun here; data has already been somewhat democratized. But the Analytics Studio allows for our clients, the democratization of decision making, using far richer datasets, traditional, non-traditional, and client data, to make truly unparalleled decisions to add competitive edge. Let's get into the detail because this is very high level. How does it work?

Steve 05:40
So your right, data has been democratized. There's a huge amount of data and everybody has access to it. That doesn't mean they can do anything with it. In many cases, the data just washes over them. And it doesn't allow them to achieve the things they need to do in their businesses. We built the D&B Analytics Studio that hosts all our data, plus all of our alternative non-traditional data and allows the client to bring their data into it. Now, the customer comes into the studio, loads their data onto a secure partition instance, and then we take their data and merge it with our data to conduct their analyses. Think about this as the clients have a small tributary of a very, very large river. And that tributary has a particular kind of expectations in it. They know what their clients are, they know what their businesses are. They know what they think they're doing with this very small tributary. We bring that tributary and we merge it with a much larger, the mighty Mississippi, if you will, of all the Dun & Bradstreet data, and now we bring our analytics to bear, along with their analytics to help them understand how their data, their needs, can be read in that much larger stream. So now the two data flowing downstream together, allows them to see the business trends, allows them to see the top predictors of which elements of the data allows them to know which way their businesses are going.

They take their models and evolved them. Models are no good if they're standing still. This data allows them to evolve those models to keep up with what's changing in a quick manner. Because as the market changes, they want the class to be there and have the conditions to be there when the market is competitive. This gives them a chance to look ahead. And not to do so just in one instance, but to do so in multiple instances. For example, if you have a client who's dealing in the manufacturing space, and they want to know how the manufacturing space is changing, in terms of the demands for the particular set of clients, we can test new external data sources. For example, shipping data, for example weather data, for example, parking lot data. It allows them to improve the lift to understand how those changes will affect their business demand, to prove the current the performance of their model and say “okay, now that we have these variables, we know how to adjust our models to better predict how the clients will use this information? Not every client we have is huge. Yes, we have 90% of the Fortune 500, but we also have large numbers of clients who are smaller who don't have their own fully capable in-house analytics teams”. Okay, come to us. Bring the data and let us use our highly functional, highly capable analytics team to help you understand what the data means for your business. You get to ask the questions and let us help you analyze the market trends. Let's help you segment the performance before the fact, so that you know how things are going to change. We're doing this now with the coronavirus. We're spending a lot of time with clients who are helping them understand not just the coronavirus and what's happening, but how it's going to affect the supply chains now and later. So they could actually inject their data into the broader stream of data to understand how the overall competitive marketplace is going to change.

Sam 08:46
This is reminding me of a conversation I had very recently at the World Economic Forum and someone talked about data and the evolution of data analytics, a little bit like the car industry. The horse, you know, pre Henry Ford's time is a bit like an SME. Not using, or even a micro SME, not using data analytics at all. Then you have the Ford Capri, combining data (the petrol) with the analytics. You now have the Tesla, okay, that's an analytics engine; data, chips, battery. And then the next evolution of that is taking outside information into an automatic car, so you know how to circumvent congestion and you know how to do stuff automated and make truly the right decisions. What you're saying by the sounds of things, Steve, is that the Analytics Studio brings that end capability, that true incredible decision-making capability, to any bit size of business.

Steve 09:37
Yes, and I'll go one more level below that. The Automobile analogy works, but only if you realize that when you're driving a car, you're not really just driving a car, you're actually looking around you with the entire university driving through. Since the traffic that's coming with you to the traffic going against you, you know the weather conditions, you know the road conditions. The Analytics Studio allows you to do all those things. The competitive advantage that comes from the Analytics Studio is that you can improve your agility and improve your speed to market, because you can test multiple scenarios at the same time, you can showcase expected results. When you drive a car and you're looking down through the windshield, you're not just seeing what's there, you're predicting what's going to happen ahead of you. That's what the Analytics Studio allows you to do. Multiple scenarios, because every company large or small, never makes a decision one at a time. They're constantly, we, I say we because we do the same thing using our own tools, we're managing multiple scenarios and variables. And we're all aiming at accelerating growth. We want to improve how we do business and analytics sandbox, sometimes some call a sand table, allows for the modeling and the analyzing of multiple scenarios simultaneously. So that if something happens, a car comes out of a site unexpectedly, your competitor suddenly leapfrogs you and technology that you didn't know about, or a coronavirus interrupts your supply chain. All those things are things you can see in model in the sandbox that you can constantly be updating and understanding what's happening across your company. This is where you bring in multiple personas and I say this, especially to the small and medium businesses, because the large businesses do this all the time. They have Chief Risk Officers, they have Chief Financial Officers, they have Chief Marketing Officers. And the big companies, all those chiefs are looking at multiple scenarios at the same time. What we want to do with the analytics sandbox is give that capability to every company, so that you can bring all three or four of those personas in to look at the scenarios at the same time, so that you're not a deer in the headlights, you're actually seeing what's happening beforehand and can make decisions logically and constructively before the crash happens.

Sam 11:33
So let's talk through some of those examples. And we talked already about non-traditional data. But let's talk about it a little bit more. That's the next frontier. Can we give some examples of alternate data and why it's so important to our clients?

Steve 11:45
I'm going to stretch your car metaphor to the breaking point here, by saying that alternative data is like giving you a thermal imaging device to go in the front of your car, to see things you can't see with the naked eye. Because in the data we've had, in most businesses, we've only been able to see that quote unquote normal data, we can see revenue, we can see numbers of employees, we can see lots of things that are quote unquote normal examples of how we judge a business's effect. But that's necessary but not sufficient. Our clients are telling us and this is again, because we're listening to the clients that are coming telling us “we want to be able to know this before, we need indicators that are long before we get to the revenue the data to have credit indicators, we indicators long before our marketing data comes back. How can you help us understand the rapidly expanding world of new information being constantly expanded by the conversion of business processes that were manual and are now digital?” We bring all that digital data in to enhance the information we already provide. And by the way, this is new to some of our small and medium businesses and new to our non-financial businesses, but it's not new to most of the financial services industry which has been using non-traditional data for years. Hedge funds, asset managers have been enhancing their forecasting models with social media, sentiment analysis, credit/debit card swipes, geospatial images of parking lots and foot traffic and shipping lanes, all the things that that you wouldn't have thought are quote unquote normal data that you use to evaluate a business that we now know you need to help you get a complete picture of how the business are changing.

One of our unique selling propositions is the ability to link that kind of non-traditional data. So geographic data, for example, to specific business identities via the DUNS number. We can link that kind of information to the DUNS number to trade credit experience. We could have additional insights that allow our customers to make data inspired decisions rather than simply reacting to things when they happen. They can get ahead of the curve instead of being behind the curve. It makes our clients the first in their categories. And you'll see this and how our clients are coming to us and saying, “if you help us gather this data and help us interpret it”, again, it's not enough to just gather the data. You have to have the analytics, intelligent analytics to interpret it. That makes our clients the top of their fields. It makes it more competitive because we're using our data and our analytics.

Sam 14:03
You've just given me this analogy of a film I used to love when I was slightly younger – I was gonna say a child but not quite – of a human being walking around as an analog individual thinking their own thoughts, doing their own things, and then Arnold Schwarzenegger in Terminator where he walks into the bar with his leather jacket on and everywhere he looks he's having ultimate data pumped into his vision that he can make smart decisions and smart insights from. Is that what the D&B Analytics Studio is doing?

Steve 14:31
It is. It's only giving that digital data that helps the – I don’t want to use the term Terminator here – I'll say the, the world's strongest client make good decisions, but allows the world's strongest client to interact with the other world's strongest client. One of the things that I've seen most often now talking to clients, you get the different personas to interact. The Chief Risk Officer is talking to the Chief Financial Officer. The Chief Financial Officer is talking to the Chief Marketing Officer. Bringing all of those data perspectives together digitally, allows them to predict and prepare, predict what's going to happen and prepare for the change before it happens, so they're able to make judgments not even in real-time, in before real-time. We're working again using the coronavirus as an example. We're spending a lot of time with clients who are saying to us, “if only I'd known this six months ago”. We're working with FEMA; FEMA says “if only we'd had this data, we’d look at this data before the hurricane hit”. We have the ability now to do that kind of mapping and understanding before the fact. It's not enough to just be there in real-time. You want to be there and have thought about the alternatives before the fact.

Sam 15:34
So we're talking real-time capability. We're talking forward looking inside deriving capability. The Analytics Studio is already off to a flying start. We have incredible new talent leading it with Gary joining us from Bloomberg. We have our acquisition strategy firmly in place, Lattice Orb Intelligence, augmenting our existing capabilities. And we have our ultimate data strategy which is truly market leading. What's next?

Steve 16:01
I think the difference between having a child play Beethoven's Piano Sonata and having the New York Philharmonic play Beethoven Sonata, the difference is practice, practice, practice. But also having a best in class set of talent doing this day in and day out. The clients who are there first in the data analytics area or are experiencing how much better they are using the data, how much better they are at using analytics, once they've gotten into it. It's getting started that matters. So what's next is getting the clients to come into the Analytics Studio to learn how to use these tools that we've developed to improve their particular business. We've worked hard to listen to the clients to develop tools that will let them do better at their businesses. What's next is getting the clients to come in and learn how best to use those tools.

Sam 16:50
That's a super point. I've been in a few customer meetings, client meetings recently. And that's exactly what they're most fearful of. They see our incredible capabilities and some of the talent we bought into the firm, they're worried, they don't have it themselves. So educating and bringing on that journey, I think is going to be a really core component in the niche.

Steve 17:10
And I'd love clients who come in and say to us, “we'd like to learn how to do this”. We're not about just selling you data. Yes, we do sell data, and we can provide you the data you want. And if we don't have it, tell us what you want and we'll get it. We have a vast organization and basket builders of reaching out and getting all the data you think you need to improve the predictability of what you're doing. Come in and tell us what you need. But then come in and tell us what you need and then let us help you understand how to a) go get it and b) how to use it. You don't have to reinvent the wheel. You don't have to go hire the dozens of data scientists that we have so that you can actually do this kind of data science. You come in and ask us the question, and let us help you understand how to use this data.

Sam 17:48
Absolutely right. And I think applying the Analytics Studio to our clients existing defensive and offensive strategies, but also helping them come up with new strategies that they haven't even thought of yet. The new use cases is going to be super exciting.

Steve 18:02
Absolutely. In fact, one of the things that we find most exciting is when a class come to us and tell us things that we haven't thought of yet. Say, can you combine these two sets of data that we have in our particular little narrow tributary with what you've got this much larger river? You combine the two, you see things you've never seen before.

Sam 18:18
And that's the stuff that puts tingles down my spine. That's where next generation capabilities come from, the real collaboration. So we're coming towards the end, we always do an analogy. We've done a film analogy today, we've done an orchestral one that you threw in. And usually we do military analogies, and I've used them countless times since, but there's so many useful analogies to draw from sport. And I'm putting you on the spot here I know, so forgive me. What sport analogies have you got for me today?

Steve 18:46
Well, I'll use the analogy that’s the same one I was using before which is “what's the old joke about how do you get to Carnegie Hall? Practice, practice, practice”. The best athletes, people who are best at their craft and sports, practice constantly. As a rule of thumb, that says we'd have to do 10,000 hours to really become the best at anything. The data that we're dealing with, the size and shape and complexity, we're still in the first real decade of the Big Data era. The way to get good at this is practice, practice, practice. Not wait until everyone else has done it. Come in and practice come in and say “here's the data that I think I care about. Let me experiment”. You know, the people who create value are the people who are willing to a little bit break the rules. So come in and experiment with the data, combine data elements that you didn't think actually made a difference to find out whether they do or not. If you're doing multivariate data analysis, if you're running graphs on these things, you're gonna find things you didn't see before, but only if you practice, practice, practice.

Sam 19:45
There's no better way to end, practice, practice, practice. I'll keep practicing Steve. Thank you very much. It's been great to chat.

Steve 19:51
Thank you.