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Webinar:

Navigating Consolidation in the Transaction Data Landscape: 7 Essentials for a Resilient Strategy

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As consolidation reshapes the transaction data landscape, concerns around pricing power, panel stability and vendor choice are growing.

Join Neudata’s Finn Cousins and Facteus’ Lorn Davis for seven essential strategies to reduce concentration risk and future-proof your data sourcing in a changing market.

In this webinar you will learn:

  • How and when to use Exclusive Data Sources
  • The impacts of regulatory change
  • Ways AI is changing the data landscape now - and into the future
  • Gain an edge with real-time data
  • Benchmark competitors on a granular local level

Speakers:

Lorn Davis
Lorn Davis

Head of Corporate & Product Strategy, Facteus

Finn Cousins
Finn Cousins

Research Analyst, Neudata

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Finn Cousins: Okay, so, hello everybody, and welcome to our webinar session today. Um, today is all about transaction data, talking about the US transaction data landscape.

I’m going to be your host today. My name is Finian Cousins. I’m one of the research analysts here at New Data, and spend most of my time covering the transaction data space.

With me today is one of my esteemed guests, Lorn Davis of Facteus. So Lorn, maybe if you can just introduce yourself to the webinar, please.

Lorn Davis: Great to be speaking with you today, Finn. My name’s Lorn Davis, I lead up our product team here at Facteus, and very excited to be speaking about what we’re seeing in the landscape.

Finn: Amazing! Just a general bit of housekeeping first on how this webinar’s gonna be structured. So, we’ve got 30 minutes for kind of a far side chat between Lorn and myself, and in that, we’re going to be covering things such as Pricing in the market, underlying panels, panel stability, and other things such as market maturity. At the end, we’ve saved kind of 10 to 15 minutes for a Q&A session. But I would really encourage you to submit questions throughout our discussion, and we’ll try and have this as interactive as possible.

Finn Cousins: Let’s recap where we are and kind of why we’re coming together today. Um, so we’ve put together a timeline, um, of the transaction space in the past 5 years. Which you’ll see shortly on your screens. It’s really just reiterating the developments that have happened. And… so this webinar is really focused on that last 6 months, kind of particularly on the consumer edge acquisition of Earnest Analytics, what that means for the market. But also several news items around Yodlee, its panel, its stability, um, kind of culminating in the acquisition of Yodlee by the private equity firm STG. I guess my first question to you, Lorn, is, you’ve kind of been living and breathing, um, these kind of, these past six months at Facteus, so what are you hearing, I guess, from clients, from prospects, um, and what are you doing about, kind of, this… these changes that we’re seeing in the market?

Lorn Davis: At the end of the day, what we’re hearing from clients and funds is, you know, they’re looking at options. At the end of the day, consolidation of this size and scale is going to make them think about how they’re sourcing data. You know, it’s a double-edged sword when you do a consolidation. Especially with Ernest and CE coming together, you could have better supplier relationships, you can get potentially better value.

But you also could have a risk around having all of your eggs in one basket. So I think we’re seeing all of those conversations going on right now. I mean, I think the one that’s probably the more nebulous and unknown is the Yodlee acquisition by STG, I think there’s a lot of questions of what’s going to happen there. Obviously, there’s been some conversations around data stability over the past few months, and whether that’s going to be rectified in the future or not.

So I think there’s probably a bigger question mark there. But when you ask about what we’re doing, I’ll tell you, we’re doing the same thing we’ve always been doing. We’re looking at expanding our proprietary sources of data. Deepening our existing relationships with our data partners. I think our strategy continues to be what it’s been for the past few years, so not enormously a lot different for us, frankly.

Finn: That’s interesting to hear. You mentioned, kind of, there’s been some worry about the stability of Yodlee. That’s something you’ve been hearing. Can you just unpack that slightly? Like, what does that mean?

Lorn: Yeah, for sure. I think the question everyone has is, with new ownership, what’s the new strategy for that business, right? Getting spun out of InvestNet. You know, it happens for a reason. Um, it’s not enormously clear from what is publicly available what the strategy is going to be with Yodlee moving forward. And as we all probably know, the aggregation space has been extremely competitive over the past few years, and that’s the primary source of data for that panel. So I think…. It’s, like I said, a little bit more unknown, and there’s some stability issues over the past couple of months, which certainly, I think, has put people in the position of evaluating other panels and other options, I mean, I can speak for our business, it’s certainly got a pretty big uptake once, you know, some of that news met the market.

 Finn: Interesting. Could you just walk over, kind of, what happened a couple of years ago, and are there any similarities between what happened and what is happening, to our understanding.

Lorn: Yeah, certainly. And look, I think this goes back to our clients and funds are managing a supply chain of data, just as we, as, you know, ultimately. Providers of that data manage our supply chain of suppliers. So, the challenge is, when you have a product that’s built on a singular source of data, if there’s any disruption to that source of data, you have a pretty material problem. And we certainly worked through that a few years ago, and it’s really difficult.

You’re scrambling because there’s not a lot of forewarning, and you’re trying to figure out, okay, what do I need to do from, like, one day to the next? Typically, you’re not looking at a panel drop, immediately, ideally. So you’re trying to plan out what impact it’s going to have, how you communicate that impact, how can you really kind of mitigate the effects as much as possible for clients.

But it’s… it’s really difficult. And so again, going back to how the conversations I’m having and the market, but also internally, is always around how do you build that diverse portfolio of suppliers, so that you have, frankly, a lot more cushion and redundancy in the supply chain.

Finn: Interesting. And could you walk us through, kind of, what a single-source dataset means, and what a multi-source dataset means?

Lorn: Yeah, so, it all has to do with where is the data coming from, and what processes are touching that data throughout the supply chain. So, when I think of multi-sources, what I’m talking about is having different contracts with multiple entities throughout the financial ecosystem, so that you’re essentially reducing risk if one source were to go dark one day, or to have a disruption in the supply, that you’re not necessarily affecting a material portion of your entire panel.

So it’s really hard, I’ll tell you. It’s not a simple thing to do, but we do think it enables a greater stability, ultimately, of what clients are buying this data for, which is Signal. So at the end of the day, it would be nice if I could point to, like, a really clear definition of multi-source versus single source because many people say, well, it’s coming from many Financial institutions, yes, but it’s all getting processed by the same entity, and that’s, I think, a key consideration. It’s ultimately, how is the sausage getting processed through the factory? If it’s always going through the same machine, then it’s not really multi-source, is it?

Finn: That’s interesting. So you can still have a single source data set, which sources from 20, 50 different banks, or kind of financial services institutions?

Lorn: Correct, exactly. And, you know, from our point of view, we have upwards of 20 individual entities that contribute into our panels. Some are larger than others, certainly. It’s not, like, a perfectly equally weighted distribution, but it does allow us to manage through any potential delays or disruptions in the supply chain much more effectively than if we only had, like, 3 or 4.

Finn: Interesting. So, in your words, you’ve got, kind of, 20 different sausage factories going.

Lorn: Exactly. Exactly.

Finn: Maybe staying slightly on the 1010 fact to your data theme, Facteus acquired, kind of, redistribution rights to 1010’s data, if I’m correct, a few years ago, and I guess there are some similarities between that, um, to what is happening now. So maybe it’d be interesting to hear, like, A, if you could walk us through what, kind of, that factors in 10-10 relationship was, and maybe some of the challenges that you, or that buyers of that data experienced, as a way to maybe think of some of the challenges and potentially positives that could come out consolidation.

Lorn: Yeah, for sure. So, you’re correct, we did partner with 1010, and we’re redistributing and serving all the 1010 clients. And obviously the intent there was to continue providing that quality product, but ultimately, also to supplement and enhance with the other data assets that we had. I think from the fund perspective they’re going to be looking at it from ‘How can I make sure the signal that I’ve come to rely on continues coming?’. And at the same time, if they’re looking to enhance or potentially explore other options, they’ll want to do it as much as possible in tandem.

Being really patient, I think, is kind of the key lesson that we took from the experience. Being open to working with clients and funds with the processes that they have in place, so that over time, you can prove the value of what you’re positioning and messaging to them is better value, but not expecting them, you know, day one being, frankly, that excited.

They just want to know that what they’re currently receiving stays as is. Like, that is the number one thing. And I think that’s probably exactly the way that it’s going for earnest and Consumer Edge at this point, is making those assurances so that everyone continues to receive the quality products that they’re providing.

Finn: Hmm, interesting. That makes a lot of sense.

Finn: I do just want to segue on to a slightly different topic, but I’m sure one that’s going to be front of, the participants’ minds on this webinar, and that is pricing. I think there’s concern that price is going to increase, uh, across the market. So I guess, like, the first question that’s most basic is, why does a transaction data provider kind of have to increase its price? Like, what are the reasons which may be a rational reason for price increases.

Lorn: Yeah, so rational price increases can come from, you know, obviously if there’s higher cost in producing the product. So that could be either fully upstream of how you’re sourcing the data, if it’s increasing cost there, if it’s in terms of your compliance requirements, legal requirements. All of the servicing of the product, that could get more expensive. All of those reasons can lead to a price increase.

As well as if you’re, you know, continuing to add value to the product, if you’re adding more data, if you’re adding more enhancements.

Those are things that, you know, I think fairly would justify price increases over time.

Finn: Interesting. What kind of price increase do you think you would expect to see? Like, panels increasing, maybe there’s increased compliance concerns there. Is there a figure which you see, kind of, the industry standard of price increases per year?

Lorn: Um, you know, I don’t have exact numbers, but I mean, I think what I’ve seen as typical is probably in the 7-10% range. Again, I can’t speak across the board, but that’s kind of what I’ve heard is fairly average.

But I think…. Since we’re on the topic of pricing increases, I don’t think the average price across the board is necessarily increasing, or should increase, I don’t think. Based on the fact that, you know if you go back to what I was just talking about, there isn’t really any of those factors that would lead to a justified pricing increase, like the amount of data hasn’t materially changed. And the quality of the data hasn’t materially changed, enhancements, etc. I mean, I don’t… I can’t speak to that, but from my point of view, and certainly the Facteus’ point of view, is we’re much more focused on reach and accessibility than necessarily trying to always pursue growth by increasing price. That’s certainly not the way that we view this market. You know, it’s a pretty mature market, so price should be pretty stable, and from what we’ve seen in the past couple years, it’s been much more stable, at least for us, I will say.

Finn: I guess on that note of, kind of, reach, and you’re saying, kind of, Facteus is focused on increasing its reach with some of the longer tailor funds that are now subscribing to transaction data. Do you think there’s a concern that transaction data might be getting too expensive for them, and they might be priced out of the market?

Lorn: For sure. I mean, like, the single reason we hear of smaller teams not using transaction data has much more to do with the cost associated with it than necessarily the value. And we think of it, I think, as providers, we’re a bit too focused on the cost of our product, as opposed to the total cost of ownership for a fund in terms of actually having the team.

Having the technology, having the, you know, compute and storage. There is material costs that, frankly, in a lot of cases, outweigh the cost of the data itself that a fund has to evaluate.

So when we think about reach and accessibility, we’re much more focused on how do we bring the same types of insights that the data can provide, or signals to an audience that isn’t going to have, you know, multiple data scientists and a full stacked technology platform, but instead is maybe more interested in getting a visualized view of the data and some kind of forecast of the data integrated into their existing models.

We see that much more where there’s opportunity than necessarily trying to squeeze out a big pricing increase from the players who already have massive budgets, um, that…. Really aren’t just the data itself.

Finn: Hmm, interesting, really interesting.

Finn: Talking about data sources here, kind of touch back upon that subject, because in my eyes, kind of one of the underlying drivers between the consumer edge and earnest acquisition was to kind of make sure the panel was an exclusive panel, so if you were an investor client, and you wanted this underlying panel, the place you’d go to, to receive that was with Consumer Edge, or the kind of… the Consumer Edge conglomerate. So I guess, my question is, how important are exclusive sources, potentially in the context of, you know, alpha generation, alpha decay.

Lorn: Yeah, great. So, I think from those point of views, it definitely helps for a fund to buy from an exclusive provider, because you have some level of assurance that there’s minimal overlap.

Between the data you’re purchasing from one provider from another provider. So you’re really ultimately diversifying the signals that you’re receiving. So I think that can matter. And obviously, we can say that part of that diversification can be achieved by, you know, different paneling methods, different tagging, etc. But ultimately, I think from a fund perspective, there is value from buying an exclusive panel because it gives you some of that base-level assurance. In terms of overcrowding and alpha decay, I don’t necessarily know if that correlates with buying exclusive. Because at the end of the day, like, it has more to do with how the data is being treated, what signals are being built on top of the data that will lead to those effects. But to go back to your question and answering it from our business point of view, having exclusive data sources makes a fairly big difference in the sense that we’re not competing, then, with other providers on, like, the Apple-to-apple basis of data, and only having to fall back on, like, service and price as differentiation.

So I think from that point of view, it certainly makes sense for a business to want an exclusive source.

Finn: That makes sense. And, um…. I guess kind of factors market itself as an exclusive source. What does exclusive mean to you? Like, you know, can it be repackaged through, like, a KPI provider, like, is, like, what does exclusivity mean to you?

Lorn: Yeah, great. So, exclusive… excuse me, exclusivity for us is really talking about the source data sets that we’re supplying and ultimately sourcing. So when you think about, like, the banks, credit unions, and partners like that that we’re going to, when we’re signing an agreement with them, we’re, you know, partnering with them, and we’re saying, ‘Over the next 5 plus years, we’re going to be providing a fairly material amount of revenue’, and for them, it’s, you know, it mostly drops right to the bottom line for them.

Exclusivity is important because we’re trying to make that commitment to them of saying, we’re going to do this for you, having it sold to other providers without our ability to control that makes it really hard for us to uphold our end of that commitment. So that’s what exclusivity means for us, is, again, it’s about that partnership model, and really binding ourselves with those data providers.

Finn: Mmm, interesting, interesting.

Finn: Maybe going one step back, you were talking about, kind of, preserving signal quality, and you mentioned, um, maybe, like, re-panelizing through different normalizations of the data. Is that a way which, kind of, you’ve seen, uh, kind of, some clients preserve alpha?

Lorn: Yeah, for sure. I mean, I think very sophisticated users of this data are aware that there is no perfect answer in terms of how you panel, or how you think about the signal that you’re getting from the data. So they tend to diversify signals quite a bit, um, and the nice thing is you can do that across providers, you can do that within some of the providers by sub-selecting cards and panelists from there, and that’s, I think, a really powerful way of doing it.

But what I’ve also seen is a lot of platforms have been doing a great job of blending across different types of data, as well as panels to provide that to, again, that farther reach of analysts and teams that don’t have the ability to do that, or just don’t want to invest in that ability.

And we’ve seen that be fairly effective. So, seen great work by many providers in the field who are able to take even aggregated sets of data that are paneled one way, but get it granular enough that they’re combining different parts of that panel together to build multiple signals.

Finn: Interesting, interesting, really cool. Leaving these kind of panels behind. I’d love to touch a little bit upon the US regulatory environment, kind of the tail end of last year, we had a lot of news about the CFPBs, this is the organization, that kind of protects and governs laws around consumers’ personal financial data, um, kind of making, greater access rights to individuals’ personal financial data. With a new administration, those laws seem to have gone away. So I just wanted to ask you, have you seen, kind of, this loosening regulatory environment, should we say, in the transaction data space, affecting sourcing, maybe even kind of how you work the data as well.

Lorn: For sure, I mean, you get it every time you’re talking to, you know, a compliance officer, um, and what we’ve seen is a pretty marked shift over the past couple of months, certainly. And I think what it’s going to do is it’s probably going to potentially increase the amount of data. I think one of the more interesting things was, uh, JP Morgan’s announcement a few days ago, where they’re gonna start charging, actually. I think that’s gonna have some interesting knock-on effects. For providers that are getting data from more aggregation-type methods.

From our perspective, and the way that we structured our data sourcing strategy many years ago, is going to direct to the source, so we have very clear rights in we can assure our clients that our Bank partners have those rights, because we’re talking directly to them about the explicit use case of monetization. So, from our point of view, it doesn’t really change an enormous amount for us, but I think there’s some questions that are going to be opened up because of the lack of regulation requiring open banking. And allowing players like the JP Morgans of the world to start charging tolls on the aggregators, and that could potentially have effects on different players that are sourcing data in different ways.

Finn: Interesting. And these questions that are opened up, is that kind of questions for the compliance officers wanting to onboard a dataset, or, like, who are those questions opening up from?

Lorn: It used to be those questions are now going away. It’s gonna go now more around the supply and stability of that supply, because if you’re charging a fair amount of money, which again, I don’t know what they’re looking to charge.

But that’s introducing a cost that. Is going to make people rethink of how they’re sourcing that data, and how they pass that cost along.

Finn: Interesting, interesting. So you’d be concerned that an underlying data provider which has this kind of open banking data, that relationship might be subject to kind of a fast-moving change in a looser regulatory environment.

Lorn: Yeah, essentially, it’s opening it up a bit more to the free market price is going to get implied.

Finn: Interesting. I appreciate we’re moving fast to the end of this first discussion topic.

Finn: Where do you think transaction data is going to go in the next one to two years? Very, very broad question, but take it how you want.

Lorn: For sure. So, I think the good news is…. I think all this consolidation is an indication of market maturity, right? So I think the transaction data market is mature. Where I see the future. Is going back to that reach and accessibility thing. There’s so many funds that still cannot integrate this data into their day-to-day research work, that we see that as one of the larger opportunities, and we see, again, going back to some of the platforms that we work with, there’s some that we don’t work with, becoming quite successful in helping integrate these data sets into the workflows of these funds and these analysts individually. So I think we’re gonna see a lot more of that, and I think the second part is we’re gonna see different approaches to pricing the data as well. So going back to the price part, you gotta match pricing models with the delivery and distribution models. So, as you start looking at broadening the aperture of how this data is getting consumed and delivered, I think you’re going to see a change in how the data itself is priced. I can say from my point of view, we’re certainly going to be moving further in the area of pricing, not just at a panel level, but maybe at an industry sector, or even, like, ticker package level because we see the opportunity to broaden the reach through those efforts.

Finn: Interesting, interesting. And I guess leaving, maybe, the card data behind, do you think, have you seen expansion into things such as, you know, point of sale data, different regions? Do you think that’s going to be another expansion, kind of firms or funds kind of deepening their use of transaction data?

Lorn: For sure, and we were active there today, and it’s exciting because funds are finding this data quite valuable. I think going back to the question, which it’s really hard for a provider to answer around, like, overcrowding and alpha decay, given that, obviously, we’re not trading, but what we’re hearing from our clients is the more expansion we can provide of data covering more tickers or different KPIs on the income statement of an existing ticker covered by car data. That’s becoming more and more of interest. So, on that side we’ve put an enormous amount of effort over the past couple of years around sourcing data that’s coming more from, you know, a product level, so that you’re getting visibility into manufacturers that wouldn’t show up on the card side, or for manufacturers that do show up on a card side because they have direct retail presence. Giving visibility into, like, wholesale KPIs. And that’s where we see a lot of the movement for us over the next couple of months, is pushing more on that side.

In thinking about it from the point of view of how a fund thinks about it, which is, like, how much of the universe do you cover, and how much of the KPIs within that universe can you help me with?

Finn: Interesting.

Finn: I might hand the floor to you for a few minutes to talk about a development at Facteus?

Lorn: Yeah, so real quick, I mean, this slide just covers, kind of, the overall panel that we bring to market. Very large panel of debit and credit cards, over 185 million active cards, 6 years of history, so very useful when you’re obviously doing backtesting. But one of the things that’s been very helpful for us, especially with certain, you know, names that are driven by events, or also by strategies that are focused on time, is our T plus 1 lag. So that’s… that’s been a big kind of calling card for us. But if we can move to the next slide, the thing that really wanted to talk to is exactly what I was mentioning before, is our expansion outside of the tickers that are traditionally viewed either in a card panel, or even in, like, CPG side, we’ve built this product, Onyx, which is covering categories that otherwise aren’t covered in most traditional product-level panels. So, it includes home improvements, sporting goods, apparel, um, big box names, as well, and this is exciting for us and our clients because it’s bringing very large set of data, at a reasonable, ingestible format.

With ticker coverage that otherwise is pretty difficult to get, and we see that in a lot of, like, the backtests that funds have done, or as some of our platform partners have done, where the performance is quite impressive for some of these names. Um, and they’re names that just really don’t have coverage otherwise. So I think going back to your point, Finn, like, we think this is more what the future looks like.

And it’s also about making it accessible to a wider array of users.

Finn: Cool, really cool. This brings a close to our first part of the webinar, the kind of the discussion.

Finn: I can see a few questions coming in on the chat box, but I’ve really kind of recommend and welcome any questions, um, that you have for Lorne and myself, just to post them in that chat box.

Finn: I do have one question, to kind of kick it off. Um, this kind of Onyx product, we’re talking about different tickers here, like, how much is this going to expand the universe of names that an investor PM could expect to track.

Lorn: Yeah, so if you think about on our card data side, roughly, it’s just to use round numbers, about 500 tickers that are covered. The Onyx product is closer to about $150, probably expanding upwards to 200 as we continue building it out over time.

So it gives you a pretty, pretty sizable bump, and again, what’s nice about those names is that there’s not as much data out there, so it’s really about bringing coverage into an area that’s kind of net new, as well as giving coverage for existing tickers, one example that we use a lot is Nike.

Where you get a lot of the direct retail sales from card data, but then we’re getting an enormous amount of visibility into the wholesale side through Onyx. So bringing those two together gives a much better view for an analyst or for a fund that’s interested in Nike, as an example.

Finn: Interesting, interesting. And of those, kind of, 500 and 150, kind of, tickers on those two respective products that you mentioned, are all 500 of those, kind of, names that people are trading, or is it, kind of, maybe half of those which are kind of actively used to generate a signal.

Lorn: That’s a good question. So, I think it really depends on the fund and their strategy, because obviously the systematic folks are going to be more interested in seeing, like, that fully broad view of as many tickers as possible. But then when I talk to clients that are more interested in having, kind of, eyes on trends, they’re probably not looking across those 500, but they may be sub-selecting areas that they focus on.

I think, yeah, that 250 to 300 mark is probably in the right realm of total tickers that overlap, I think, in those two. It says.

Finn: Hmm. Interesting, interesting. Um, so I’ve got quite a funny question coming through here, I think there’s concern that, you know, Facteus is gonna sweep up a few more clients in this disruption, and then bump up prices in Year 2 and Year 3. What can you say to alleviate any concerns there?

Lorn: Yeah, I think it’s similar to the way that I was talking about our partnerships with our data suppliers. It’s the same way we treat our clients who are, you know, our customers. We’re in this for the long haul. I like to think of us in the race here as kind of the tortoise.

We’re slow and steady, but we make material incremental improvements every single year, and we’re here to stay, and I think, frankly, the response from the market has recognized and really appreciated that. So, large pricing increases is not the way we typically want to be doing business, because it hurts us long-term.

And again, our business is really predicated on bringing new data to market. We consistently have brought more card data to market, we’re bringing new additional providers around this product-level data, and it’s with the intent of having a relationship with a fund that we can come back to them and say, hey, would you be interested in this? Would you take a look at it? And if we just sent them a, you know, 50% pricing increase or something absurd, that’s a much harder conversation to have, frankly.

Finn: Mm, mm. And… Getting a crystal ball out. Is there anything that people should be getting excited for, kind of going forward?

Lorn:  Uh, I… I think there’s… there are some things in the works, but not ready to talk about them yet.

Finn: Interesting, interesting. Okay.

Lorn: But we are excited. Yes, we have…. Probably by the end of the year, some announcements we’ll talk about.

Finn: Interesting. Um, I have got another question, um…. It’s really talking about if we are we gonna see more innovation in a consolidated market?

Lorn: Innovation, I guess, in terms of panels, in terms of delivery.  I don’t know how much more you can squeeze from the panels that are out there.

Um, I really do believe, both personally and professionally, that delivery is the new frontier. We’ve got to expand how data gets in the workflows of our clients. And that goes from data feeds, which is, you know, the bread and butter, to APIs. To, you know, Claude announcing this financial services module, it’s like, well, how do you now start integrating into. The LLM world, so how do you build an MCP? I think there’s some of these, like data delivery questions that are really interesting, and I’ve had conversations with some potential, like, technology partners who are trying to answer those questions.

That that gives me…. A real view of where the industry can go.

Finn: It sounds kind of something like a discretionary fund, you should be really excited about that. Is that something that a Quant Fund would be interested in as well?

Lorn: I think that’s a good question. I think there are smarter people than me that probably have a better answer than me.

Lorn: I’ve heard some interesting things on how different funds are utilizing AI models on transaction data and melding it with other data.

But, as you know, they tend to be a little tight-lipped about specifics.

Finn: Yes, yes, no, for sure. I’ve got another question here, uh, it says, With all the back and forth around regulation and regulation changes, do you see your model/products, or processes changing?

Lorn: No, we have a really kind of consistent approach to how we source data, and it’s worked very effectively with the way that… so, and maybe this is just good education for the audience. The way we source data is we go directly to a bank or a credit union and explain to them what we’re trying to do with the data, who we’re selling it to, why we believe it’s valuable. And the way we ultimately get the data physically is they do an implementation of our synthetic data engine behind their firewall.

So it’s… we have a very tight relationship with these banks and credit unions, and they’re synthesizing the data on their side, and we’re receiving that synthetic data. And that’s how we’ve really enabled ourselves to get check marks from all these compliance officers as well as from a regulatory point of view of ensuring that the data is as clean as possible. In fact, it’s cleaner than really all the regulations have required.

And so, moving forward, there’s no intent or reason we would change that. It’s worked really well. We don’t want to mess with it.

Finn: Mmm, interesting, interesting. And I guess, continuing on the theme of audience, that kind of word, synthetic data. Sounds slightly like a dirty word there. Um, could you talk about what that is, and maybe why you should or shouldn’t worry about synthetic data?

Lorn: Yeah, great. So, I think it’s especially becoming relevant because of the discussion around the use of synthetic data in training, like LLMs. Um, slightly different than the way that we’re talking about synthetic data. So synthetic data for us is data that is essentially perturbed data from the data source. So, certain key metadata is adjusted so that for example, a dollar amount may be adjusted by, like, 1%, or something like that. And the reason we do this has more to do to not allow anyone who has additional data sets from just joining them together. You shouldn’t be able to reverse engineer the original dataset from the synthetic data, but in terms of trends and use cases completely unaffected. Um, and that’s… that’s really key, because. Really, at the end of the day, none of our clients care about, you know, Lorn Davis buying, you know, $5 Frappuccino – but they care enormously about the 5 million people who are buying $5 Frappuccinos and being able to trade on that signal. So, we have nothing to do with PII or any of the things that are completely valueless in our world, and everything to do with, you know, really accurate data, even if it’s perturbed on a per-transaction level.

Finn: Interesting, that’s really useful. Um, so I’ve got a last question here, um, so if anybody’s got any last burning questions, put them in the chat box. It’s saying, do you think we will see new players coming to market?

Lorn: I think we always will. I think so. Um, you know, I think over the past few years, there’s been new players coming in and out. I don’t… I don’t see a reason that’s gonna change, and I think, um, some of them will bring interesting products to market, I think there’s products out there that serve particular niches and focuses. And I think that’s good. Again, my overall thesis is this consolidation is an indication of market maturity. And we’ll see over time, people continuing to… to participate in that market.

Finn: Mm, interesting, interesting. Um, so I haven’t got any more questions here, um, so we will bring an end to this webinar. First of all, I’d just like to extend my thanks to Lorne Davis for joining me on this. I thought it was a really interesting conversation, um, and I myself learned a lot. The second point is, on the screen here, a kind of a slight plug, shall we say, for the New Data Industry Survey. This is serving buyers and sellers of data to understand what trends are doing. If you answer the survey, which is going to take between 5 and 10 minutes, no longer, you get early access to the full results and entered into a prize draw. So if you’ve got the time, I’d encourage everyone to do this. But thank you very much for joining me, and we will see you next time.