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Data Talks, Episode 7: How Referential Sources Reduce Data Bias

Episode Seven: How Referential Sources Reduce Data Bias

Host: George L'Heureux, Principal Consultant, Data Strategy
Guest: Joseph Santos, Senior Principal Data Advisor

There's what you know, there's what you don't know, and there's what you don't know you don't know. That’s what can really hurt you, and that’s where referential data comes in.

An organization can function and strategize with its internal data alone, yes it’s possible. However, is it the best strategy? In this episode, we examine how relying on internal data can lead to short-sightedness, and even worse, bias.

 

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Episode 7: How Referential Sources Reduce Data Bias

George L’Heureux:
Hello everyone. This is Data Talks presented by Dun & Bradstreet and I'm your host George L'Heureux. I'm a Principal Consultant for Data Strategy in the Advisory Services Team at Dun & Bradstreet. In advisory services, our team is dedicated to helping our clients maximize the value of their relationship with Dun & Bradstreet through expert advice and consultation. And on Data Talks I chat each episode with one of the expert advisors at Dun & Bradstreet about a topic that can help consumers of our data and services to get more value. Today's guest expert is Joe Santos. Joe is like me, a Principal Consultant for Data Strategy here at Dun & Bradstreet. And Joe, how long have you been with the company?

Joe Santos:
Wow, it seems like forever and a more to that. Thanks George, but almost a year, nearly a year.

George L’Heureux:
And tell me a little bit about what you're doing in your role. Although I think I might have a bit of a clue.

Joe Santos:
Absolutely. So the way I see our role is bringing out thought leadership both inside and outside of Dun & Bradstreet through contents such as this, write ups and through client consultations as well. The most important thing that I've come to know about our role is really thinking what else is possible for data. And that for me defines the role, it has us to really elevate not just the role of data, but the usefulness of data for decision-making.

George L’Heureux:
And before you came to Dun & Bradstreet, you were actually a customer of Dun & Bradstreet. And we've talked about this a lot you and I. You've always talked about how important it was having a referential data partner in the work that you were doing before joining the company. Why was that so important to you?

Joe Santos:
Absolutely. So just to be a bit of a background, I've been on the customer space for over 20 years and a customer for about 10 years. And referential data is like having a referee. If we're going to put an analogy here it's having that referee in a conversation or think of yourself. Well, if I ask you who was a champion for Formula One last year in 2020? In the conversation scenario, there'll be people who are knowledgeable of the topic, who would say it's Lewis Hamilton, British driver from team Mercedes. And there are others who are not as informed that could speculate and create their own answers from their own biases. Now, with referential data, we could go to the Formula One site or be it a Wiki page and removes all of those biases and adds really strength to your data when it comes to facts.

George L’Heureux:
And I'm glad that you know that it's Lewis Hamilton, because I probably would have guessed like Don Mattingly who played for the Yankees back in the '80s. But really when it comes to, what is it that gives us that trust in some outside authority? Why do we trust the Formula One website, as opposed to just my recollection or your recollection or somebody else, what makes them authoritative?

Joe Santos:
So let's take a couple of steps back. So among data practitioners, there's an inside view and an outside view. What's the inside view? It's the data that's generated within the company that has some biases in it, some good, some bad, and a lot of these are done because of their internal procedures, policies, many, many other reasons. And these built in nuances affect the view of the data and ultimately its quality. And sometimes the chicken and the egg thing happens, sometimes at the quality of the data that affects those nuances. And of course, oh, I have my favorite saying, that's how we've done it forever comes also as a hurdle. But with external referential data, it's created by experts. It's gotten through a lot of scrutiny and govern through tried and true processes and policies, simply put, external referential data helps you to know what you don't know. And because of these three things I mentioned; done by experts, added scrutiny, continuous truth to any of the data and tried and true policies, they get to be trusted and be referenced more and more by people.

George L’Heureux:
It's funny what you talked about there, knowing what you don't know, reminds me of, I think it was former Secretary of Defense, Donald Rumsfeld, who would say, there's what you know, there's what you don't know and there's what you don't know you don't know. And it sounds like the referential data is helping us to address the third of those things. But what really is the harm? If you don't use referential data and you're using some of this internal data that has these biases that you're talking about, what's so bad about that. Could you talk about maybe the range of possibilities that could occur when you use that type of potentially biased internal data.

Joe Santos:
Just to add to those quotes, ancient philosophers like Confucius mentioned that true wisdom is knowing what you don't know and more contemporary philosophers. And I consider Jim Rome as one of them puts it so plainly, and this is where the bias has hit us. What you don't know will hurt you, you are limiting your view for the solution you're trying to get to by these known biases. Referential data helps us address these blind spots. If you're looking at mergers and acquisitions and rely on your clients to tell you that, you're already late with your strategies, you need to know when these happen and put this in your data as they happen. How can you do a cross-sell or an upsell when not knowing who owns what. And being able to do that repetitively removes these internal biases and adds relevance, reduces the burden of data upkeep and in turn increases the relatability of your data and reduces those consequences.

George L’Heureux:
You're talking about real life consequences of having bad data or having bias data in it. And I suppose that that's really the key when we are talking to clients, it's about trying to figure out what the consequences are and how they could impact the business goals that our clients have. And using a referential data partner like Dun & Bradstreet allows them to your point to reduce the burden of up keeping their data and have a much more reliable data source.

Joe Santos:
Absolutely. That's where rubber hits the road George. If you take a look at the sales and marketing perspective, if you have an account strategy that you want to accomplish, but without outside reference data, will you be successful? Maybe. I've seen some do it, but there are limitations that are inherent to that strategy or the lack of strategy. But why take that chance? Why create an ABM, a campaign or a go to market campaign? ABM is account-based marketing, without knowing the layers and the players. Why risk your bottom line? And that's a company like Dun & Bradstreet can really help.

George L’Heureux:
I think one of the things that I always reflect on when we get to talking about stuff like this, is that the way that companies grow is organic. You start, you have a product, you have an idea, you start selling it, it gets some traction. You don't necessarily plan as you're in a growing for all the steps that you're going to need along the way. So how do you and you're former customers, so maybe you can speak to this. How do you know, how did companies know when that point has come, that it's appropriate to figure out which referential data providers you need?

Joe Santos:
So that's an excellent, excellent question. You have to understand what your goals are and what you really want to achieve. For instance, in around 2010, we have this need to make sure that we understand a company's credit worthiness so that we could extend the line of credit in, was it the proper proportions to reduce risk, because that really puts the company in a huge level of vulnerability that's really unneeded. So initially we would take a look at just the deal size, perhaps a company size, send out a survey, or just give out a default amount, but why again risk that when it is attached to our bottom line and we know areas we're in we were lagging, or we were failing at that. But being able to have a company like the Dun & Bradstreet to help us with risk referential data allowed us to really customize the credit extension that we could give to these companies and allowed us to really make these good choices with regards to assessing risk and as well as improving our company relationships.

George L’Heureux:
So I want you to try and take off your Dun & Bradstreet hat for a second, but not completely. You're a former customer. You got to that point with your company, where there is an obvious need for referential data. What is the decision process like? How do you come to choose Dun & Bradstreet and why?

Joe Santos:
With regards to choosing a referential data partner, we need to be able to define what partnering is. It's really sharing accountability. And we need to maximize that investment within the vendor. With trying to find a vendor or trying to find that partner or a vendor that we could partner, we need to take a look at the depth of the data that they have, the reputation within the market or within the industry. And of course, is there data relevant for us? And those were the three things that we typically looked at in looking for a referential data source. And in this case, the fastest way to get there is to be talking to a vendor who are experts within the data scheme.

George L’Heureux:
So we'd talk about this every day with our clients. And I think it's important to draw out some of these points here. Once customers engage with Dun & Bradstreet, and we're working together with them, what can they do on their side to help maximize the value of the referential data that they can acquire through us?

Joe Santos:
So I'll bring up again, partnership. That's a word that's very crucial to this. Partnering up means sharing accountability. Within getting an external partner you need to make sure that you get that from handshake and outside of this handshake there's the partner side or the vendor side, and there's the client side. You need to make sure that to maximize that, your data is in a form where in its ready to have that handshake. And in the past, when we brought in Dun & Bradstreet, we did that. We brought our data into a language where in, we could get that outside view repeatedly, and then we hold Dun & Bradstreet accountable for the accuracy of the data that they're giving us.

Joe Santos:
And this is what's needed before we can really truly maximize this outside referential view. We need to make sure that we have this before we have an enterprise view off the customers, because if we don't have this, the enterprise view, you'll be doing a lot of things within the company in different silos, reinventing the wheel within multiple sub works. And it happens a lot George, and there's a lot of duplicative efforts that's being done that sometimes it's unnecessary, people might be doing or having the same issues, but not asking the right questions. So maximizing partners like Dun & Bradstreet, we involved them in these communications. We involve them with the problems that we have so that they can in turn consult best practices beyond the data that they offer.

George L’Heureux:
I think that that's one of the things that I enjoy in you that expressed to me, we enjoy together most about the role that we share here at Dun & Bradstreet is that ability to encourage partnership and to really underline the idea of it being a two-way street of accountability, where we want to help them, and we can speak sometimes difficult trues to them about what needs to happen in order for them to gain the maximum value from us. But Joe, before we finish off, let me ask you if you could summarize what the one thing is that you want people to take away from this conversation today, what would that be?

Joe Santos:
Getting the right data George, is only half the battle. It sounds like a GI Joe ending, right? It really is just half the battle. Yes, you need to get the data. You need to get the right data to make the right decisions, but you have to get beyond that immediate gap, that need for that gap to be filled because in reality, an ad hoc request is much more than an ad hoc request. It's sometimes just a symptom. We need to really take a look at bringing in referential data on a consistent basis to strengthen our data practice internally, as well as to widen our view of the outside world.

George L’Heureux:
Well, thank you, Joe. I appreciate you coming on today and sharing your expertise on this topic of referential data.

Joe Santos:
Thanks again, George, it's been really fun. Let's do this again sometime, dude.

George L’Heureux:
All right. Our guest expert today has been Joe Santos, a Principal Consultant for Data Strategy at Dun & Bradstreet. And this has been Data Talks. We hope you've enjoyed today's discussion. And if you have, please let a friend or a colleague know about the show and for more information about what we discussed on today's episode, we encourage you to visit www.dnb.com or to talk to your company's Dun & Bradstreet specialists today. I'm George L'Heureux. Thanks for joining us, until next time.