Many companies are sitting on data assets that could be revenue streams for them, without knowing it. Matt Staudt of VDC discusses making latent data profitable.
Ginette: I’m Ginette,
Curtis: and I’m Curtis,
Ginette: and you are listening to Data Crunch,
Curtis: a podcast about how applied data science, machine learning, and artificial intelligence are changing the world.
Ginette: Data Crunch is produced by the Data Crunch Corporation, an analytics, training, and consulting company.
Ginette: Today, we chat with the president and CEO at the Venture Development Center, Matt Staudt.
Matt Staudt: The company that I’m with is VDC, Venture Development Center. Basically VDC is an organization that works in the alternative big data, bringing buyer and seller together. So we have a unique perspective on available data assets that are out in the marketplace and a unique perspective of the companies that utilize them, and what they’re specifically looking for in the way of points of, uh, value for various data assets.
My background was originally in the marketing and advertising area, where I owned a company for 20 years, IMG, Interactive Marketing Group. I left that in 2007 and joined this, which was more or less of a lifestyle organization. And we made it a full-fledged organization company back in 2010.
Curtis: Now, when you say data assets, can you put a little bit of definition around that for the listeners? Just so they understand how you define a data asset? ‘Cause I imagine there may be some things that you think are valuable that maybe they haven’t thought of, or maybe it’ll help expand our thinking around what a data asset is.
Matt: Yeah, sure. In my, in my terminology “data asset” basically falls into eight different categories, where assets basically come from within the information world. So they could be things like transaction data or crowdsource data. They could be things like search data or social data sets. They fall into various categories, traditional data, meaning assets that are business to business or business to consumer generally aggregated by large companies that most everybody’s heard of Dun & Bradstreet, Infogroup, Axcium, the credit bureaus, et cetera.
Alternative data in our world are companies that have unique data points, unique. They’re collecting unique pieces of information, usually as a byproduct of their core business. And we look at the assets that the data sets, the actual data points that they collect. And we figure out if there might be something of value to take to the marketplace, usually to the large consumers of the data, the big aggregators that I previously mentioned, but oftentimes it also fits well with some of our mid-tier players. And we have a significant amount of relationships in the brand grouping, meaning large organizations that they themselves are looking to try and take advantage of big data and utilize data in sales, marketing operations, in order to transform or help to administer certain activities that they have going on.
Curtis: Do you find that this is maybe industry specific, like for example, a big insurance company, or if you’re in healthcare or something like this, it tends to be more data intensive that you see more activity there or, or is this really applicable across the board? What kind of industries do you find have a lot of applications?
Matt: Yeah. Well, it’s interesting on the surface, you certainly think that there’s probably industries that would have a larger appetite and a larger need for data than, than other organizations, but going, you know, through the list of companies that we’ve helped over the last 15 or 20 years, it really runs the gamut. I mean, we’ve worked with insurances, you mentioned insurance, insurance companies. I mentioned credit bureaus. We work with credit bureaus, risk and fraud, sales and marketing, sometimes large brands within those retail environments. So it really truly has run the gamut for us. There’s, there’s not any specific area that we focus. It runs the full range of organizations and size of organizations actually.
Curtis: Got it. Does the approach typically change? So if I’m, I don’t know if I’m the CEO of small- or middle-sized company versus the fortune 1000, I’m assuming how I approach this might be different, but then maybe there’s some similarities. So how would you recommend people think about data assets maybe differently depending on their organization and industry or, or maybe, maybe it’s the same.
Matt: Well, there certainly are similarities, right? So, so organizations that we work with, I mentioned alternative datasets. So, so these are companies that by and large have a normal day-to-day process of doing business. And as a derivative of that, they create a massive data exhaust. So they themselves are not necessarily organizations looking for data. What we do with those companies is we look at the data that they have, the information they’re collecting. We determine if it’s getting harvested compliantly. If they have it at scale, are they going to get it for the foreseeable future? And if all of that exists, then we engage with them. And when I mean engage, we actually try and monetize that data for them.
So on the sell side of our equation, we have organizations that run from the large traditional players that I mentioned to these small alternative players and the process around that is evaluating the data that they have, the information that they’re collecting, the data points of value and how we might be able to administer them into a buying situation on the buy side that is similar as you know, regardless of size of companies, the logic of what you go through, the way that you look at the data that the data of importance is usually similar. It’s really just a matter of scale and utilization, right?
So large organizations we deal with maybe making products sets or looking for specific types of insights to enhance an existing product or solution offering, or because of our engagement with them, we understand where a specific gap in that data exists. And so we’re constantly looking at trying to augment that similarly in smaller organizations, the same type of activities go on, right. But just at a different scale, different level.
Curtis: And is there a, maybe like a pick a large or a small company, but I wonder if there’s a, maybe a concrete example or story you could share, without giving away any of your client names or any secrets of course, but just something to help kind of wrap, wrap our minds around what is a problem one of these data assets has solved for people? What has been the result of the monetization for the company in terms of additional revenue? And these kinds of things. Can you help us, uh, maybe put some, put some meat around that?
Matt: Yeah sure. So back five or six years ago, we started looking at the data that was being collected by a lot of these apps on location data. And we were fortunate in that we were able to tie in with one or two that were brand names well run, tightly run organizations. And they had massive amount of this data that was being collected at, based on where the mobile device was and the logic of why that data was being collected was to determine behaviors of people so that the appropriate ads could be served to them in app. Our review of that was there was just this massive amount of, again, data exhaust that existed. And we started looking at that from a perspective of, well, what compliant ways might there be to extract certain pieces of that information and utilize it in an off-line type of environment where we could bring certain insights into an existing data asset for, for marketing and sales.
And so we took those solutions out to our buying entities and we offered them up. And in fact, it turned out that there was a large appetite for that, right? So for this company that by and large was a, for lack of a better description, maybe a mobile DMP. You know, now all of a sudden they’ve got this data arm of the organization where we’re taking this derivative data set and we’re, we’re generating income for them. And it’s not unusual in situations like that, where the activity starts accelerating, gaining traction, where these companies can make a fair amount of money on this brand new revenue stream. So that, that would be, you know, one specific example for sure.
Curtis: That’s interesting, especially in terms of kind of the environment that we’re in right now. I’m sure you’ve heard this from clients, but a lot of, a lot of business leaders are looking for ways to find new revenue streams, right, because of COVID and our economic environment. Are there recommendations you would give companies and leaders in terms of how they think about using data to, to open up new revenue streams, given the circumstances?
Matt: Yeah, sure. I mean, you know, the interesting thing there of course is privacy, right? So, so what we, back to my example, a moment ago. We started doing this six, seven years ago, and, uh, you know, as time went on more and more companies began doing that. Some, some really pushing the boundaries of what could be used with geolocation data, right? So of course now you start getting into privacy issues. Well, a condition of ours is everything that we do is based on first making sure that everything adheres to the privacy issues and doctrines that are associated with it.
So that becomes an aspect. And then, then you look at, within the context of COVID, that’s a really good example, right? Because when you look at that, there was, there was a lot of news and a lot of press being presented around geolocation data and the fact that it was, you know, potentially an invasion of privacy, and depending upon how it is used, that’s clearly the truth. Right. But then you start looking at how it all impacts within the world of COVID and contact tracing. Right. So now it’s a question of, “okay, well, this is really meaningful information. It’s just a question of how is it going to be used and how do you make sure that it’s used appropriately and done so in, in privacy specific manner?” So, you know, that’s a, that’s an immediate interaction of what happens, where the conditions change and, and data can be incredibly valuable, right?
Curtis: When you’re looking at this and try to figure out where’s the value? how can we use this data? And there’s privacy concerns and laws that, you know, are different in the States versus in Europe and other places, would you say that the privacy and the compliance is, is one of the harder aspects of this, or what takes the most time and the most thinking to get something like this actually done? Is it that analytics piece? Is it the privacy and the laws piece? You know, what goes into this?
So therefore we have to make sure that that, that exists properly. So we start with privacy and then it’s looking at the individual contracts and the utilization of that data from the company that’s harvesting it, you know, and then it comes down to beyond that is, is it is a good business decision. So we have to look at it from a perspective of, does it make sense for this company to be selling this data in these types of use cases, because there’s a lot of money that can be made on a compliant data asset. And so I have a data asset that’s harvested compliantly. I have a significant amount of volume. I’m going to get that volume for the foreseeable future. There may be use cases out there where the data could be very valuable, meaning it can generate a lot of money, but it may cause a lot of friction with the core business. So we balance all three of those things in order to try to figure out how we can truly help an organization that’s looking to try and monetize their data assets.
Curtis: Got it. And that friction that you mentioned is that maybe usually a result of the, the clients or the customers of that company, not liking that, they’re sharing that information, even if it’s compliant, it’s just sort of, uh, they would lose business as a result of that, or where does that friction typically originate from?
Matt: Yeah, well, it could be that it could be, it could be just where the information could be utilized by somebody to short a stock for argument sake. Right? So, so there’s a lot of ways that this data, because just the nature of, of the information business, there’s a lot of ways where data, if blended with some other data asset or other data points becomes more and more impactful. So you’ve got to consider how the data could ultimately be used, what it could be blended with and what ultimately the, the value of it is to the person buying it to the organization, buying it, and what are they going to do with it. Right. And that’s where sometimes that friction is most notable. So unless you’ve really navigated those waters and you understand exactly what that use case could turn out to be, and it’s, it’s gotta be well-defined, you know, there are some potential hurdles.
Matt: And you talk about the use cases. How do you go about identifying those use cases? That seems like an interesting problem space, right? Because you’re talking about, well, can we combine this data with other data that may be available publicly, or may be available from somewhere else, and then who ultimately will use this data and trying to find out bring all those pieces together and finding a solution seems like a, uh, an interesting problem space.
Curtis: So how do you guys approach doing that? Is it, is it a result of your network that you’re able to kind of parse that? Or how do you do that?
Matt: Yeah, well, it’s certainly a result of the network and it’s also, you know, a result of the last two decades of really doing this. We’re in an interesting position, we work with buyers of data and sellers of data. So I guess, you know, the best way to look at this as we list the house and we sell the house. So we know who all the, well, the good things about it and all the bad things about it, all the challenging things about it. And that puts us in a unique position, both positively and negatively: first, from a positive standpoint, we know this information, so we have to be careful how we’re presenting it. And we have to make sure that that, that the full details are being conveyed to both buyer and seller, you know, in the negative position, if we didn’t do that well, we wouldn’t have a business. So the fact that we’re still doing this and growing and a vital part of the ecosystem for the last, you know, 15, 20 years, it shows that there’s a spot in the marketplace for this. So trying to make sure that you’re constantly understanding how all of that pivots and how it can be used is, is imperative for us.
Curtis: We talked about how, you know, whatever industry you’re in there’s, there are use cases likely for the data that, that you are collecting as a, as a course of doing business. Are there patterns on the buying side? I know you mentioned like credit bureaus, and I have to imagine marketing and sales is a huge consumer of this kind of information, but are there some other ones that maybe people don’t think about as much?
Matt: Yeah, sure. So you had marketing, sales for certain, right? There we’ve used data to try and gain perspective on competitive footholds, how, how various stores are competing within their channel or with competitive stores. Another big use case for us for data is, is risk and fraud. That’s significant, right? I mean, that, that, that data set that or that world of data consumption is, is, is, is large. I mean, that’s certainly within the context of the credit bureaus, but it’s also with, you know, the large companies that are looking for, questionary measures around insurance and other, other aspects as well. So, so a single data asset might have utilization for a sales marketing, or some sort of digital play, but, but it’s, it’s not uncommon for a similar asset or an asset in that category to also be very valuable in a risk and fraud situation.
And they can be very basic, right. They can be, they can be effective email address an active email addresses, right? That’s, that’s valuable for sales and marketing, but that’s also really valuable for credit bureaus, and it’s valuable for companies involved in the risk space. So it can be these very rote, basic assets that you’re not necessarily thinking about as being, being, you know, powerful, but having very, having very accurate email address is important. And from a perspective of what are the active email addresses? I may carry five different emails, but I really only use two. So, which are the active ones that I’m honestly constantly interacting with. So it goes from that to much more significant data points. The basic data and kind of a basic processing of that data holds a lot more value than people tend to realize. And the more advanced, you know, if we’re going to do predictive modeling or do machine learning or do something like this, that definitely has an immense amount of value and use cases, but I think the basic value is often overlooked.
Curtis: Do you find in your work that is the case, or are you finding oftentimes you’re taking this data and doing some sophisticated modeling to derive value from the assets?
Matt: Yeah, well, it’s both. I mean, there are certainly clients where we, where we know they have a specific need for information. And in some cases on the buy side, you know, we’re actually working with those organizations to go out specifically to find particular data assets or new sources of information in a particular category. So, so that exists.
In the other case, it may be where there’s a particular need. A company is looking for alternative data assets that gives some sort of indication about someone who is looking for a refi or a mortgage, you know? And so, so there’s a lot of different alternative data assets that if looked at and if blended together, give us very specific set of insights on that. So the challenge there of course is, is the ongoing utilization of that data. Because if you bring information in from two or three different data sources, there’s this constant data flow that’s occurring, there’s a constant mapping of the data to the, to the data layouts. And then there’s the constant interlinking or stitching of the data. So there’s, there’s a lot of different pivot points along that way, but understanding what the desire is and how it can be facilitated is really where we start. Right? We put together these things called the visual data composites that begin with that exact point of reference. This is what you’re looking for here are the eight different or so categories of the data that we have. Here are assets that fall into those categories and the relevant pieces of information that exist within each one. And that’s the rationale on why we’re, we’re presenting it as a data point for consideration.
Curtis: Got it. Interesting stuff. This has been a really interesting dive into, into how you can do some of these things. I wonder if you could just give us maybe your vision for, uh, for VDC, where do you guys go in, you know, and where do you want to be in five years? Where do you think the market is going? Do you think there’s going to be changes in how data assets can be bought and sold that people should be thinking about or new regulations coming down the pipeline, or what do you, what do you think is coming?
Matt: Yeah, that’s so that’s a broad, broad discussion. So I think the answer is there’s going to be constant regulations. That’s just the way, the way it is there. I think there’s, there are less bad players in the space now than when I began doing this, you know, 15 years ago, just because they, they were pushed out, right. It’s, it’s harder. And you see a lot of those organizations just closing up.
So I think the ongoing regulations are going to make that and maintain that and keep it tight. So, so that exists for us as an organization. You know, we realized a seven or eight years ago that one of the value adds that we had wasn’t beyond the strategy was, you know, trying to stand in the middle of these contractual obligations in order to try to facilitate testing. So again, if I go back to my example, I have all these small alternative data asset companies as one group that we deal with, and they’re not data companies, so they don’t have data evaluations or testing agreements or things like that.
So what we’ve done is put in place a process where we can stand in the middle of that with the large buying entities, the big brands or the professional service organizations or consulting organizations or ad agencies. And so we helped facilitate that process for testing. So that was a good decision for us, and it allowed us to accelerate a lot of these, uh, unique data assets for consumption and evaluation. And then in the actual contracting.
For us as an organization where I think, uh, we clearly have to go is now to the next step where we’re actually doing some of that blending, that interlinking, that stitching of data, as I mentioned, right?
Previously, or our process is one where we’re going to bring forward the assets for consideration and the companies themselves, the buying entities, the larger organizations. I mentioned. Some have full data science teams, and they feel that they’re equipped to handle this ongoing integration of data.
And in fact, they have the wherewithal to do it, but just the, the volume of the data that’s coming in, the information that’s available and the way that it can change becomes very difficult to manage. So, so one of our next steps is to make sure that we have that in place, that we can actually help with that and not just orchestrate it, but actually do it conducted through that interlinking. And then the next step beyond that would be setting up sort of a general data exchange, where again, we’re just taking some of the ongoing problems and delays and hoops and hurdles out of the sequence where we’re handling every aspect of it to try and make it as seamless as possible. So, I mean, that’s for us as an organization where I see the longer term value for, for the organization.
Ginette: Thanks, Matt Staudt, for being on the podcast. If you’d like to see this episode’s transcript or our attributions, head to datacrunchcorp.com/podcast.
“Loopster” Kevin MacLeod (incompetech.com)
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