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Data Talks Episode 23: Leveraging the Match Data Profile

The power of match data profile

Host: George L'Heureux, Principal Consultant, Data Strategy
Guest: Rick Venezia, Data Strategy Consultant

Keeping data fresh, up-to-date, and neat is no easy task. For example, let’s say that you have a former executive’s name in the ‘company name’ field (perhaps the company wasn’t named yet when your sales team created the record.) It may still be possible to use this information to match the record to the correct business. The Match Data Profile code lets a business know what data, including alternative data, was used to arrive at the match.

In this episode of Data Talks, our experts discuss the benefits that Dun & Bradstreet matching can bring to our clients, particularly the power of alternate match points.

 

 

Read full transcript

George L’Heureux:
Hello everyone. This is Data Talks presented by Dun & Bradstreet. I'm your host, George L'Heureux, I'm a Principal Consultant for Data Strategy here in the Advisory Services team at Dun & Bradstreet. In Advisory Services, our team is dedicated to helping our clients maximize the value of the relationship with Dun & Bradstreet, through expert advice and consultation. 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 Rick Venezia, a Data Strategy Consultant at Dun & Bradstreet. Now, Rick, how long have you been with the company?

Rick Venezia:
Hello, George. I joined Dun & Bradstreet just under a year ago. However, prior to that, I had worked with D&B Data for going on 18 years. And so needless to say, I'm very familiar with deploying solutions using D&B Data, and really came to depend on D&B Data very heavily, both in maintaining our CRM as well as in developing various analytical models.

George L’Heureux:
I think one of the great assets that we have inside Dun & Bradstreet is that we have a lot of the people who used to be clients like you who've come into the company who understand what it is to be a client, and who use that to inform the way that we work with clients. And I think that that talks to the topic that we're discussing today, which is a really important aspect of one of the benefits that Dun & Bradstreet matching can bring to our clients. And that's this idea of having alternate match points. Let's start with the basics though. What are alternate match points?

Rick Venezia:
Well, alternate match points are alternate data that we maintain within our match reference file, that enable our clients to find the entities that they're looking for. So these alternate match points would include things like former names, former addresses, CEO names, additional phone numbers.

George L’Heureux:
How does having a robust match reference file like that, that includes all these alternate match points, actually end up helping our clients?

Rick Venezia:
Great question. Naturally data does not exist within a vacuum and let's be honest, depending on the age of an account, the complexity of an organization and various other factors, it may have been a long time since the client initially collected the information about the particular entity that they're looking for. And as we've all seen, things can change, company names change, physical locations, phone numbers, people. So the information that the client may possess, may no longer be valid or even certain ports of it may no longer be valid, yet they still have a need to find the right D-U-N-S Number and so maintaining these alternate match points makes that possible.

George L’Heureux:
Rick, do you think it would be fair to say that the more of these match points that we have, the better chance that we share a common view of an entity with that of our clients?

Rick Venezia:
Probably a bit of an over simplification. There is always the more is better rule. No, it's really the true power comes from the structured collection of the data, the depth of the records, and the refresh of the data to even make this comparison possible, that being the comparison between the client's data and our own. And what we find sometimes is that the client's data is, it's widely different from what we believe as the correct and current information. But again, only by having that former data available, are we able to make the matches.

George L’Heureux:
And so the idea Rick then, is that used together these different parts of match insight, the confidence code, the match grade string, the match data profile, they give clients the information that they need in order to understand why and how a match took place and how high we really think the quality of the match is, right?

Rick Venezia:
That's right. And our clients are going to present us with the best information that they have available, so it's on us and our processes. Our processes have to anticipate imperfect input, and then we need to leverage the deep knowledge base that we possess to get to an answer. And we want to make sure that we're transparent in how we got to the match and that's what the match insight allows us to do.

George L’Heureux:
You and I both know that the conversations that we often have inside of advisory services with clients frequently involve discussions around match insight, and especially when an alternate match point has been used to find a particular match. Could you share what some of those conversations might be like? What are some of the things that get brought up that need to be explained?

Rick Venezia:
Yeah, sure. When customers get back a match based on an alternate match point, at least initially, they'll sometimes look at it, and frankly, question why it was matched. They'll say, "This isn't even close to the name that I put in, but yet you're returning it a confidence code of a nine on it." Or they'll say, "How did I get an A on this street name?" And the answer is that they're providing us information that represents one dimension of the data for that entity. And we are in turn returning to them the current information that we possess.

So for example, let's say a given client has all trade style names in their data. And let's say that we match on this trade style and give it a rating of an A. But the business name or the legal name that we return to the client could be very different. And so at least initially, that could cause some confusion. But again, remember the match was a perfect match. It was an A, but it was on a different dimension of the data than the true business name. And so in this case, it was on that trade style.

George L’Heureux:
So aside from trade style, you mentioned earlier that some of the match points that we have, represent former data, like former names or former addresses, former executives. It seems to me that, that could represent potentially useful information to the client in and of itself.

Rick Venezia:
Right, yeah. Absolutely. Let's take an example where you submit an executive name and we return a match, and we inform you via that match data profile that we matched on the former executive. Well, that should set off the alerts on your side to say, "Wait a second. I thought this was a current executive," and in this case, you might want to get your stewardship team in there to verify the information that you have is accurate and current. Another thing that we see on a fairly regular basis is the client will submit more than one record that they believe are distinct and not related to one another, but we will in turn end up matching them back to the same D-U-N-S Number. While everyone wants to have perfect data, this type of input is very helpful. It's showing you that you potentially have some overlap within your data set and gives you the awareness so that you can address that.

George L’Heureux:
We've talked a lot about the benefits of having that deep reference file. And you just mentioned a couple of those additional sort of almost hidden benefits around alternate match points, but can we compare what's happening inside Dun & Bradstreet against the rest of the industry? What does that comparison look like when you take a look at it?

Rick Venezia:
Well, simply put, the data that we collect in the Data Cloud is unrivaled vis-a-vis business data. It's truly this bank of business data that sets us apart.

George L’Heureux:
That's what feeds into the match reference file as well as what we're able to return back to the client. Do customers need to do anything special to prep their data in order to take advantage of the match insight that you were talking about or these alternate match points that can get them there?

Rick Venezia:
Well, so D&B is set up to ingest whatever information you have. The depth of our records is really intended to prepare for a variety of characterizations of a given account. And so the preparation, if you will, would be, once we return the MDP to you, you could have your own processes to act upon the information as is appropriate. So in this sense, the preparation becomes a question really of governance and stewardship. What are the rules? How do you implement them upon receiving this new information? Another way of saying this is, how do you optimize the experience of your internal stakeholders based on what you get back?

George L’Heureux:
Rick, as we get close to ending our conversation here today, I want to ask, really, what's the bottom line? What would you want to make sure that someone who's watching our conversation today or listening to it, that, that person would take away from this conversation?

Rick Venezia:
All right, well, first let me say that having worked closely with data stewardship teams for years, I have an enormous respect for their role in the impact that they provide. And I know that any opportunity to improve the efficiencies of their processes saves the company money. Now, I'm sure I'm not telling you anything new, when I say that most people are going to achieve the vast majority of their matches by way of the confidence codes in most use cases, eight or greater, but it shouldn't end there.

This additional information provided via the match data profile will, again, help you to better understand the way that we arrived at the match and help you prepare and maintain your data, which leading to more predictable outcomes. So also leveraging the insights from the match data profile in combination with certain match grade string patterns, will allow you to fine tune your matching and extend your match acceptance. And let's face it, the more data that you can confidently match via an automated process, the greater velocity that you'll recognize within your operations.

So this is the long way of getting to my bottom line, but the bottom line I would say, is that learning to harness the full power of the information in the match insight, means you can optimize your matching and stewardship processes, to allow you to achieve the velocity that your stakeholders demand.

George L’Heureux:
Rick, I really appreciate you sitting down and taking the time to share some of your perspective and your expertise, both from having been a client of Dun & Bradstreet, and now inside the Advisory Services Team here at Dun & Bradstreet. Thank you so much for your time.

Rick Venezia:
Thank you, George. I enjoyed it.

George L’Heureux:
Our guest expert today has been Rick Venezia, a Data Strategy Consultant at Dun & Bradstreet and this has been Data Talks. I do hope you've enjoyed today's discussion. And if you have, please let a friend or a colleague know, and if you'd like more information about what we've discussed on today's episode, please visit www.dnb.com or talk to your company's Dun & Bradstreet representative today. I'm George L'Heureux, thanks for joining us, until next time.