Nick Derewlany: From Data Asset to Data Business

In this episode of Selling Signals, we’re joined by Nick Derewlany, a data monetisation leader who has worked across GlobalData and Trustpilot building new data products inside larger organisations.

Nick brings a practical view on how corporates should think about commercialising existing data assets. We talk about why “we have lots of data” is not the same as “we have a data business”, and what companies should prove before hiring a team or putting large revenue targets in front of the board.

The conversation also covers the path from 0 to 1, the importance of repeatable demand, and why $1m ARR across several customers is a very different signal from one large bespoke deal.

This episode is for anyone building, buying or selling data products, particularly inside companies where data monetisation sits alongside a much larger core business.

Transcript

SPEAKER_01 0:02

Welcome to Telling Signals, the podcast focused on how businesses actually monetize and sell data. Each episode, we interview an industry insider to get their experiences and lessons learned.

SPEAKER_02 0:12

The spirit is powered by Valtors, the company that transforms your data into investment-ready intelligent products.

SPEAKER_01 0:17

If you enjoyed the episode, please subscribe from wherever you get your podcast.

SPEAKER_02 0:22

In this episode, we're joined by Nick Derolani, a data monetization leader who has spent years building and commercializing data businesses inside organizations, including Global Data and TrustBot, where you also had the privilege of managing yours truly. Having worked across both businesses launching new data products, Nick brings a practical perspective on what it actually takes to turn data into a scalable revenue stream. Nick, welcome to the podcast.

SPEAKER_00 0:45

Thanks, Eric. Excited to be you guys. Looking forward to the conversation.

SPEAKER_02 0:49

Yeah, welcome. Yeah, it's a bit strange interviewing my former boss.

SPEAKER_00 0:55

Yeah, likewise.

SPEAKER_02 0:59

Why don't we uh start with global data and trust pilot? Obviously, me and you are fairly familiar with Trustpilot, but uh to the listeners who are Global Data and Trustpilot and what were you trying to achieve in your roles there?

SPEAKER_00 1:12

Yeah, so uh I actually I mean I came into Global Data through uh its acquisition of a company called TS Lombard. So I actually spent uh almost almost seven years at TS Lombard and then Global Data um originally as a as a seller and and then uh was in Europe for a couple of years, helped uh build out our APAC business, and then eventually led the team there. And the first year of leadership was really uh to get this uh get the business back to growth and set it up for acquisition, which we did. And so I came into global data through that. I was head of sales at TS Lombard, and then when we were acquired by Global Data, um Global Data had historically sold a lot of uh its kind of market intelligence and alternative data products into corporates. Um, but they really wanted to build out an institutional investor division. And so I was head of global sales at TS Lombard, effectively it was financial services sales, and then we were required to, and I led the build-up of uh Global Data's buy-side division um to help them scale their data offering into the buy side. Uh when I joined Trustpilot last year, slightly different stage, obviously, much earlier as we know. Um, but Trustpilot, it was really um, you know, as you know, Trustpilot has a very successful uh SaaS platform that it sells into brands. Uh, you know, $300 million ARR. And the view was how do we take all of the data that Trustpilot has collected and monetize that into a number of different audiences? And obviously we started with investors. And so that was very much a kind of pure um, you know, zero-to-one uh build. And yeah, it's been um it's been a fun ride over both.

SPEAKER_02 2:51

And and you're you're obviously originally from Australia, moved to London and then are now currently in New York. Where across those roles did did those moves take place?

SPEAKER_00 3:01

So I actually started my career in financial services recruitment. So um spent when I left Australia, spent a couple of years in London when I first moved there, recruiting systematic traders and portfolio managers, and so got a lot of exposure to you know different trading strategies, especially on the quant side, um, kind of quite quite early doors. But um TS Lombard started a couple of years into my London career, and so um you know spent uh well seven years in London with them. I was supposed to move to Hong Kong when uh I was promoted to help build out our APAC business, but um COVID happened. I got stuck in London, uh, started doing Zoom calls at 3 a.m. on my uh flatmates iron board, um, which was fun. So um yeah, did that uh and obviously finished up in London, you know, with with uh global data and then moved to New York last summer, which is when I when I joined TrustBalance.

SPEAKER_02 3:59

Well, you're using the the iron board as a standing desk.

SPEAKER_00 4:02

Correct. Yeah.

SPEAKER_01 4:03

Good times. COVID DIY.

SPEAKER_02 4:06

Yeah, yeah. Awesome. Um essentially for this episode, what I want to do is pick your brains of the learnings across those times of working in essentially public companies and building new data businesses within those. Um I think uh it'd be good to put this across sort of two main phases. One is sort of how exact teams should be thinking about building those businesses and what they should be thinking about before. Um and then let's say someone is tied into that role like like we were, um what are what are some of the things that someone could do to set themselves up to success, lessons learned those sort of things. So if we start with the executive team, um sort of what they're thinking about before building, from from your experience, what should these teams be discussing before they embark on a data monetization journey?

SPEAKER_00 4:53

Yeah, so I mean, I think like a couple of things really come to mind. And I I'm gonna approach this from a um, you know, we have a bunch of data, we haven't commercialized it yet. Kind of, you know, how do we do it? You know, I I'd say the global data role was a little bit uh different in the sense that it, you know, I think there's a different difference between data businesses who are looking to go into different verticals, right? And then, you know, non-data businesses who have a bunch of data, whether it's through their core product or something else, and they're thinking about like, right, we have all of this data, how do we think about this, you know, as a as an additional revenue stream? Um, so I'll focus a little bit on the latter. Um uh but but but happy to dive into the former if if helpful. I mean, I think like a couple of things that are really important um, you know, for me in in my experience. Firstly, it's um like how committed are we to this? I think it is very easy when you're thinking about data, especially if you have a kind of successful kind of core product, you have a bunch of data, it's very easy to look at, I would say, the um the metrics of data businesses and and and some of the kind of sales mechanics of of what you can do with the data business and and just start to think that this is an obvious choice. You know, if you if you look at a couple of things that make data businesses um really attractive, they have high margin generally. Um they uh the net retention is usually very strong. Um, you know, so it's very common to see you know 110, 120 uh percent net dollar retention. Often the gross retention is is you know kind of high 90s, um, you'll you know, in the 90 to 100 range. And so I think it's and and and and thirdly, I think it's very easy, and this is something that we did at Trustpilot, you know, it's it's very easy to look at um just do a simple TAM-sam mapping exercise. So, you know, target adjustable market and um like weighted adjustable market and and really look at um, right, what are the avenues that we could sell into and how big are those? And if we capture X percent of that, you know, that's gonna lead to Y revenue. And so I think it's really easy to look at all of that and say that hey, this is a really obvious choice, and we should absolutely do this. And this is um not gonna be easy, you know, nothing's easy, but it's like you know, it's just gonna be a question of you know turning the data that we have into an API or a set of products or whatever that looks like, and then we're gonna capture that market. Um, you know, from my experience, uh unfortunately it's not that easy. Um, and there are a lot of other considerations. And so, you know, for me, the first question is really like, how committed are we to this? You know, if if I'm an exec in that business, like how committed are we? Um why are we actually doing this? You know, what's the goal? Is it a diversified revenue stream? Um uh, you know, are we looking to go into different industries? Are we trying to add something back into the full product? Is this an experiment? I think you need to you know have really hard and honest conversations around you know answering some of those questions, um, you know, before you think about uh kind of embarking on that journey. I think I think uh a second critical thing in all of that is from my experience building things within public companies, and we saw this at Global Data and also at Trustpilot, um one of the challenges that you have building something within a much larger org is that there is a core business running alongside you. Uh and that can create, if not handled thoughtfully across the business, some incentive misalignment. And so one of the questions that you should also be discussing as an exec team is like, are we prepared to divert resources away from the core business in the short term to uh stack this up? Um, you know, are we prepared to run this effectively as a separate business within the broader business? Are we prepared to treat it um from a unit economics point of view, from a kind of resource to output point of view, effectively is a different business? Um, because from my experience, you know, across both uh companies, um, if you're not prepared to do those kind of things, and if if you're really you know trying to uh almost just treat it as a separate sales team and run it kind of within the uh the the infrastructure and the the setup of your existing business, um, I think that can create a lot of challenges in terms of you know some of the the alignment and and and and incentives and and and ultimately kind of mischaracterizes you know some of the fundamental differences between like building something brand new within a much bigger org and obviously um you know continuing to plow money into something that's that that's already working. And so I think I'd say those are probably um a couple of things that that I think are really important to think about.

SPEAKER_01 9:45

You you mentioned um that that's your first point, you know, figuring out just how committed the the businesses are to going down this data monetization route. Um and I I wanted to sort of ask to dig in a bit deeper on like how you go about like what what are the the metrics, uh and obviously it'll be different business to business, but like what in your mind are the metrics of finding um getting that level of um comfort in the drive to do it? Because we we were talking, I think it was a few months ago now, but we were talking to the CEO of a I'd I want to say they're either public or they're a large private, you know, a few hundred million um dollars in revenue, and we were discussing the prospect of them monetizing their data. And basically he he turned to us and he said, Look, the fact is that we do have the rights to monetize our users' data. Um and I know because we've had this long conversation that institutional investors uh would generally want the data to be like anonymized, they don't want to have to worry about PII or anything like that. And so to a degree, they're one of the best places to be monetizing this stuff. However, I can tell you now, if there's any prospect that this would target the main business in any way, um, I would need to be able to tell my board that the kind of the the I'm taking this risk because there is a meaningful path to like, I think you said like $10 million, $15 million in revenue in the next 12 months from this business unit. Um I looked at him and I was like, yeah, well, you you're not realistic, you're not gonna be able to say that. That's just not gonna happen. He was like, Yeah, I I kind of figured you'd say that. And so that's why to me that this this angle isn't isn't as attractive. So yeah, sorry, it was a good um, I just thought there was a um that gives you a metric that that I've heard, but like what in your mind are uh is the best way for an executive team to get that level of confidence and indeed perhaps convince the board that this is the right way to go.

SPEAKER_00 11:32

So I mean, I think it's a bit of a balance, right? I think um on the one hand, again, as I said, it's very easy to look at kind of some of the uh historically very successful data businesses, some of the core kind of unit economics of what a great data business looks like, and paint a really optimistic vision of like, yeah, hey, we're just gonna turn the taps on and we're gonna generate you know 50 to 100 million dollars in the next three years. Yeah, maybe you get to 10, 15 in a year. Um uh, you know, I think it's really easy, you know, to do that. Um, and obviously that kind of stuff is gonna get the board excited, you know, particularly if the business is at a at a stage where that stuff to become really meaningful for the overall revenue. Um, I do think you need to balance that. And if you're an exact um, you know, I do think you need to balance that with a little bit of kind of realism. And and again, kind of coming back to some of the points that I made around um, you know, like what are we actually trying to do with this, you know, based on on what we think we have and a really conservative estimate of what that might look like, what is a realistic pathway? And and I think your job as a as an exec, you know, certainly to the board is to really thread that narrative. And it's a tricky balance, right? You know, the board is not going to buy in if you're not um uh you know painting a picture of something that could be really meaningful in the same way. You don't want to completely overpromise um something that's not grounded in any in any sense of reality and uh and fail to deliver that. And so it's a bit of a thread, I think, from my experience, and and we can come on to this, um the important work to do uh when you're thinking about like how committed you need to be and what that actually looks like is actually doing a lot of the work beforehand. And so um, you know, it's it's trying to do as much of the um the actual research as possible, whether that's like early research in a product team, whether you can do like early white papers to try and figure out like what do we have, what do we have here in financial services, the more you can do ahead of time to actually get feedback from the market or actually test your own data in a way that gives you confidence that um there is something there, I think can really start to help you to put some numbers behind what that can actually look like, but also start to you know build some credibility on, you know, like I think we have this opportunity, and these are the reasons why. And we've actually done XYZ you know testing. We've we we've done a bunch of white papers, we've maybe spoken to a couple of customers who've said, hey, if yeah, if you had that, we would pay this. Um, I think the more you can do ahead of time to almost build that business case before you know hire a bunch of sellers, invest you know, a bunch of money, um, I think is really important to that, uh, you know, to that question and really something that that probably people should do more often.

SPEAKER_02 14:22

Not to challenge that view, but obviously I worked with you at Trustpilot, and I've done a few of these monetization journeys from the from the seat when I was at New Data. And I I kind of agree with that approach, right? You're gonna pull uh people into an organization where uh things might not work out and there's some job security risk, et cetera, et cetera. And you want to be a little bit more confident on on the longevity of this business before you you sort of change someone's career path, right? The the difficulty I found over the last sort of three or four years in industry working with these types of businesses is that uh the data worlds are really unique uh and people end up going down a pathway of building things that are just someone coming into the state has to unpick. So I how how do you feel like you can sort of get that blend? Is it hiring a PM to come in and sort of just talk to customers and think about how to build build build projects or cut bringing in someone who's more of a strategic thinker rather than a BD? What's that balance there?

SPEAKER_00 15:28

Yeah, I mean, I think I think if you're selling specifically into financial services, uh you need to have some, you know, have somebody with experience selling into that space. And so um, you know, whether that's from a commercial point of view or a product point of view, um I think when you start the exercise, it's almost like don't start it without somebody who understands what that world and what are the things that you need to look for. And so again, I look back at some of my experiences certainly at Global Data. Um, you know, Global Data had some of that expertise in the broader product team who were looking after the corporates. And so what we were able to do quite effectively is using that expertise um to leverage that person to start building some white papers and looking at, okay, this is the data that we have, you know, what do we think is actually gonna be relevant and why? And so I think we were able to do a lot of that kind of groundwork before it was like, great, this is where we should put the resources, this is kind of how we're gonna think about the growth and investment plan. And so um again, I think if you look at some of the common you know, pitfalls or challenges to your point, um, you know, a bunch of stuff is built and then a team is hired in, which you then you know might need to spend a bunch of time kind of undoing a lot of the stuff that's been done. I think the really important thing is like if you figure out um, hey, this is something that we want to do, great. Is there you know institutional uh knowledge in the organization that can help you think about this the right way, you know, uh from a product point of view? So um, yeah, you know, data history, you know, all of the stuff on time, uh, you know, what are the insights that we're actually providing with the data that we have? It's not just, you know, hey, we have data, great, let's go monetize it. Um, you know, if you can find somebody internally, you can help you do that, great. If not, how can you try and get that externally? Can you bring on a consultant? You know, can you uh talk to an industry expert? Um, you know, there's all sorts of kind of like outsourced, you know, monetization teams that probably cost a fraction of like a, you know, like a full-time hire. Can you do as much of that as possible to help you shape that early build the right way in terms of like how should we think about product build? Um, how should we think about TAM and Sam? You know, it's not just like, hey, this is the TAM for public market investors. If we capture you know 3% of that or 10% of that, that's X in revenue. Great, let's go do that. Um, so if you can do as much of that as possible and we be quite tactical and thoughtful in doing that before you add a bunch of resources on, I think that's how I would typically approach it. And certainly from experience, I think when you don't do a lot of that to the point that you've alluded to, um, you can go off in a uh a direction that feels very reasonable at the time, um, but often it requires you to spend a bunch of time, you know, on picking that.

SPEAKER_01 18:26

And you you've um described this kind of initial, almost like scrappy startup style approach of like it sounds like maybe even just having one person who's fully in charge of that initiative and is trying to find essentially product market fit for your data in whatever manner manner you're now seeking to monetize it, but by also being scrappy with the resources that are available to them already from within the enterprise. From your perspective, at what point does it start to become uh something where you do go actually now is the time to put the real resources behind this, bring in a sales team, etc. Is there like an inflection point that the uh and at which yeah, that feels like the right thing to do? Is there even even a you know an ARR figure in your mind?

SPEAKER_00 19:09

Yeah, I think that's a really important question. I think if you look typically at, and this does very business by business, right? So like I don't think there's one answer to this. This is just my view. I think if you look typically at where founders who are building standalone companies start to think about bringing on salespeople. Um, and again, I think like the AI has shifted a lot of this, you know. Um I I've seen kind of early stage companies getting to four or five million ARR in the in the in the AI space before, thinking about external sales ties. For me, a good benchmark is a million dollars in AR. Um, so that's one thing. I think the second thing is that it it ideally you have that distributed across a number of clients. And again, every ACV is a little bit different. Um uh but you know, like something that we looked at at Global Data, we had a product that you know we thought was really good uh in the kind of medical SU transaction space. Um, you know, we got to about a million dollars in ARRM and decided, okay, great, this is somewhere that we can really kind of invest in and divert sales resources to. And again, it it it wasn't a pure kind of let's you know start from zero and uh and do that, but I think the principle is the same. And so I think if you have distributed or diversified clients where there is some repeatability um across the different style of use cases, um, you know, I think that that is a really important signal. And I would generally be looking for a million dollars ARR before I would be thinking about great, let's actually start to pour some fuel on the fire. Um, because I think that you know the challenge is if you do that too early, um, you're adding a bunch of uh, you know, people that are supposed to convert uh you know, pipeline and convert um, you know, existing demand and interest into revenue and help scale that up. If you're having all of those people try to find product market fit, um still I think that's you know, it's it's not a good dynamic necessarily for those people. And it's also, you know, certainly not a good a good dynamic for the business. And so that's kind of how I think about it in terms of a specific number.

SPEAKER_02 21:10

And you mentioned there that you'd want that to be across a number of accounts. Are you thinking that as you want that to be repeatable use cases that are clear rather than just one big buyer that found it really, really useful and there may or may not be others out there? Correct.

SPEAKER_00 21:26

Yeah. I think you know, when you think about making bets, it's about diversification of risk. And so as an exec or as a sales leader, if I'm looking at um where should we uh, you know, where is there evidence that we have signaled that something is working? And should I devert resources to that? Um, you know, if we have a million dollars in revenue and it's one customer and it was a custom deal and they got a lot of value from it, that's great. Um, but I would definitely be questioning like, is that repeatable? Um Maybe they had a particular uh, you know, type of setup that made that one thing really interesting. And again, like we we saw this at Global Data, we had a particular uh quantitative product. Again, it was it was in the medical transaction space. Um, really only had one customer uh on the systematic space for that particular product. Um they paid us a lot of money for it, it was incredibly valuable to them. Um, but nobody else really bought it. And so I think that's an interesting example of like one strong signal from one customer doesn't necessarily mean you have something that's repeatable and ready to scale. Whereas if I'm looking at something that's a million dollars in ARR, and again, depending on your ACV, whether that's four customers or 10 customers, whatever that is, I think if you can start to see multiple examples of somebody using it in a similar way, um uh, you know, your risk is diversified and you have a clearer signal of like, hey, this is repeatable, it is more likely that we're going to be able to repeat this again over kind of you know a larger number of companies than just one or two. Right, there is something here. Let's spend time and resources uh exploring that further. And there's no guarantee, of course, but you know, every decision is risk reward, right? That's the that's the name of the game.

SPEAKER_02 23:14

Yeah, understood. So let's take this on to the next phase then. And I don't want us to assume that all that work's been done because quite quite typically it's it's not. So you you're sort of hired in in this sort of data monetization lead type role. Let's talk about how someone can be successful in that role and sort of pick your brains on the lessons learned. So you're hired in, how should someone think about their first 30, 60, 90 days uh at a a big corporate that's thinking about monetizing uh data assets?

SPEAKER_00 23:46

Are we assuming not like uh are we assuming none of that work has been done? It's really like complete fresh start, you know, we don't know what we're doing, help us figure it out.

SPEAKER_02 23:59

Yeah, let's do a bit of that and a bit of column A, a bit of column B. Okay.

SPEAKER_00 24:04

So I think if you're coming in completely fresh and it's like we have all this data, we want you to help us figure out how to monetize it. Um, and again, there are some differences here between something that maybe is a little bit kind of slightly more mature. I think early on, you know, in in that environment, it's it's really about um uh trying to think about like product and marketing position and really getting a good sense of like what what the current assets are and and how you could start thinking about building product on top of that. So, like what is the data? Um, where is it stored? What's the history? Um, you know, trying to get as much information as possible on kind of what that looks like to start forming opinions on um, hey, how could we think about, you know, maybe a product build? Um, you know, how could we think about you know market positioning? I think another important thing is something that I've learned from my experiences, uh, my experience doing this is that selling data really is a cross-functional effort. Um, you know, it's it's not um, you know, it involves sales, commercial, you know, whatever you want to call it. Um, but there is a big, you know, obviously a big product component, there is a big data component, um, uh, there's a big legal component. You know, often, you know, when you start at a at a you know, if you're coming into an org that is sells a different kind of product that might be a SaaS or whatever product um contracts will be worded a certain way. Um and so it's really about like how do we build a little and shape a little cross-functional team where again, like you're not pulling people away full-time from their jobs, but how do you start to work on these problems cross-functionally so that you're not just doing stuff in isolation. Um, and you can actually start like, you know, building consensus with what you're doing, which I think is is is really important. And so that that first 30 days for me is is really about you know trying to get um your head around all of that and and really trying to start building um you know a universe internally that's gonna allow you to succeed moving forward. I think 60 days um you know is really I think you will have a good sense of that. And so secondly, it's really that kind of mini validation phase, I would say, uh, that I talked about earlier. And so um, what can be done to help you understand uh if you have a data product that is valuable? I think one of the misconceptions is that always uh is that you know, data equals revenue. Uh, and I think I always like to say that that's not true, insights equals revenue. And so I think it's really important to start like, how do you start to prove that? Um, can you speak to people in your network to help you think about this the right way at funds that you've sold into? Um, can you engage external consultants scrappily, you know, kind of on the fly, um, you know, for an hour call again, depending on your budget? Like whatever you can do, um, you know, can you tap people in your network or even can you do internally? Can you start to um try and build a mini thesis as as to how you think this data is going to be valuable specifically for investors to use to make better investment decisions? Um, and then how do you test that? Uh, you know, how do you start to um prove that out a little bit? And then 90 days is really, again, assuming, uh, and I would almost think about it, this isn't a step of actions to take. Um, it's really a step of like things that you need to prove out and be comfortable with. And so assuming, you know, the first 30 days worked in the way that you anticipated, assuming the next, assuming the 60 days you started to validate and and you receive some positive signals of validation. 90 days is really about great, um, how do we think about product build in the right way that's gonna capture and enable for some of that? Um, and how do we how do we start thinking about like a an MVP kind of viable commercial setup um where you start, you know, what does the motion look like? Um, you know, are there terms that we could change so that you're really in a position to, even during that nine during that uh kind of last bit of the 90 days, but even beyond there, so that you can actually start putting this in front of people for um you know to evaluate uh effectively. And so that's kind of how I think about it. Um, you know, certainly when you were starting from scratch.

SPEAKER_02 28:21

Yeah, I uh your insights data doesn't equal revenue, insights equals revenue sort of uh resonates with me, especially as I think about any sort of product that's being sold, it's gotta solve one of three things, or do one of three things. Create more money for a business or more opportunity for a business, save a business money or reduce its risk. And like data can be one of those three. Um depending on yeah, yeah, you can have a data that can solve all three. But the the rule of data doesn't necessarily achieve that. There's a lot of productization that needs to go into that to build something that that does do that's called overload.

SPEAKER_01 29:05

And it's insights for who as well, right? Like if we're talking on the investment fund side, you mentioned like tan and time earlier. I think people uh particularly like sometimes they provide quite naively, assume that like an investor is an investor. And then the more you dig into it, the more you're like, oh, like this type of investor needs this type of insights. Like we're talking to quant. We therefore need massive coverage. Or we're talking to a customer investor, therefore, actually we need like really deep insights because they're going really deep into the businesses. And so like the investment industry is really is really tricky from that perspective. Not only you have to figure out what the insights might be, but you have to figure out who they're most appropriate to, then find those people because they don't tend to put what their strategies are, etc., on LinkedIn, and then figure out how to get in front of them.

SPEAKER_00 29:54

Absolutely.

SPEAKER_02 29:55

I I even think about I think a lot of people think selling to corporate is is not that big of an opportunity. And I think we spoke to Ad Labor about this really early on when we launched the podcast, but like successful data providers, but people that sell content uh contact information and intent data to sales organizations they're they're enabling sales reps to be way more efficient with their time, create more opportunities and hopefully close win more business. Like it's they are solving a very obvious problem, which is salespeople need contact information. Um and they're targeting sales leaders. But it it it that's the the sort of problem they're solving. So I feel like that's always overlooked, not always, but quite often overlooked in organizations that think about doing this is that ah, we have this rare asset, let's just go and flog it in and someone else will figure it out.

SPEAKER_00 30:42

Yeah, absolutely. I mean, I think that point on on you know, like selling intent data to sales leaders and the impact that that has on revenue, I think is really important, right? Like I think, you know, investors buy data because it gives them alpha. Um, you know, it gives them additional signal that they can't really get anywhere else. And it gives them information that they can use to make better investment decisions that they're currently doing. And to your point, James, like that will vary depending on the type of investor. So, you know, quants want different things relative to fundamentals. If you're selling into private markets, the value proposition of, you know, you can't run a back test um against a public company and correlate it to earnings, right? You're looking at um, you know, can you give them a better signal or an insight into that business as to what's happening and the future likelihood of it being successful or not based on the data you have? And then can you build a product around that? And so it does vary a little bit depending on on the types of organizations that you're selling of the type of financial services firms that you're selling into. But ultimately, it's really like um you know, it's all about alpha. And it's like, can you help them make a better investment decision based on their strategy um relative to other things that they're currently doing? And if you don't have, and again, like you're not gonna have you might not have that straight away, but if you don't have a really clear idea of how you think you can do that, and if you're not building products that specifically um uh try and address and and you know solve for that gap, um it's very easy to look at great, we have all this data. Um, for Tam and Sam is you know, financial services are huge buyers of data. Great, we're gonna unlock that. It's very easy to do all of that um and fail miserably because you just haven't done that work. And so I, you know, I do think that work is really important. And you know, we even saw that again out of global data. Global data has a lot of data assets. Um, you know, uh it has, I don't know, it covers 20 different sectors. Each each sector data set is incredibly rich, um, incredibly deep. You know, I would say it's probably one of the most comprehensive providers of data out there. Um, you know, it covers something like, I mean, I can't remember how many companies, but you know, like hundreds of thousands of companies very, very deeply. Um, but in that 20 sectors, we only really focused on on three or four. And that was because when we started to actually look into the data set and what would be valuable selling into financial services, you do the two quality tests, right? It's like, is the data um does the data have these specific characteristics that generally make it valuable for investors? You know, as we know, that's rich history. Um, it's updated frequently so that the signal is not delayed or decayed, um, you know, point in time if you're uh if you're selling into systematic firms. And then there's the kind of is it valuable, does it give alpha, does it provide insight? And what we started to see was that that actually is we went through all of these data assets, there's only really three or four that kind of met that criteria. And that really changed how we thought about go to market. And it also really changed a lot of the conversations internally around okay, we have 20 products, if we can do, you know, X million revenue for each of these products because the TAM for consumer is X, and we have a consumer data set, this is the opportunity. And it takes a lot of, you know, you need to be able to tell that narrative as to, you know, that is offline because of these reasons. Um, and again, I think if you're not doing that and you're not thinking about things like that, you end up in a position where you think your TAM is 100 million in in four years, and it's actually six, and obviously, um, you know, that leads to a lot of complications.

SPEAKER_02 34:22

You've touched on a few things, some some common pitfalls there, but what would be the other pitfalls you'd you'd expect to see in in a business in in this stage?

SPEAKER_00 34:34

Um I think for me, like I I touched on a little bit earlier. So, you know, I I've talked a little bit about kind of you know doing the work, um making sure you have a a kind of an ambitious but realistic expectation of what you can do and and and how you're gonna get there. Um I think another pitfall is that again, when you're building in uh again, my experience has been building in public companies within kind of startup divisions within that. Um, I think it's trying to apply uh trying to apply the unit economics and the um, you know, I guess the operating model of the core business to the startup division. Uh, you know, most or almost all startups are profitable. Um, and obviously you'll be uh a lot of the time, you know, if you're doing this in a public company, they will have certain uh you know, EBITDA goals, um, they will have certain operating margin uh you know goals. And so I think it's a really important, and this is not just the person coming in, this is you know, holistically as a company, it's really important to, and this goes back to like how committed are we to this and and what are we actually trying to do there here? You need to have an honest conversation around this is what the revenue trajectory could look like based on some of the points that I mentioned earlier, but this is also roughly what a pathway to some kind of profitability looks like. And are we actually comfortable having a division that is going to be losing money in it you know across a period of time, potentially for a long period of time, to help capture this revenue opportunity? And I think you know, if you're coming in as a head of data monetization and having a conversation with whether it's you know a CRO or a CEO, whoever it is, you need to have those um hard conversations first, um, so that you can actually figure out like, um, can you operate um in the way that all early companies operate within a bigger company to help get this off the ground? Because you know, if you're expected to um uh be profitable in year two, or if you're expected to um operate to a a margin constraint that may might be applicable for a 400 million ARR business, but certainly wouldn't be for a you know one million dollar ARR startup, um, it's going to be very difficult for you to execute in the way that you need to to actually make it successful. And again, that's um, you know, nobody wins in that scenario.

SPEAKER_02 37:10

I mean, all of this conversation does raise the question of why do it at all. And I know we touch on the fact that there's uh decent margins, the retention rate can be quite strong, but uh there's not many databases out there that are worth enough money for a public company to really invest part the money in and and allow it to run a be a lot enough for a period of time for it to sort of revert to being profitable. Like is it the the the the thinking should be the the belief around data is going to become more and more important? It's gonna be one of the inputs to AI, and um more and more businesses are gonna use data, and therefore you're possessing yourself for a a future opportunity that isn't here today. Or should businesses be thinking about actually, well, a five, ten, fifteen, fifty million dollar business that has really strong margins is a good thing to build nonetheless?

SPEAKER_00 38:08

Yeah, I mean that's that's that's an interesting question. Um I think I think about it a couple of things and uh a couple of ways. And so, yeah, like I I do think if the world is moving away from platforms to an agentic world where you know agents are just kind of using data and interacting with the world through data kind of platform agnostic, then I think if you have uh data that powers that and is incredibly valuable for that, I think uh you you find a way to make yourself um future-proof and relevant um for an agentic world. And and so I think if you're a public company, again, you know, we even saw this with with Trustpilot. Um, I think Trustpilot did has done a really good job at communicating to the market um why it wins in the world of AI. And I think if you look at its share price, and again, I I haven't checked in the last week, so maybe it's sold off, but but if you look at its share price, um, you know, certainly a couple of months ago um during the large SaaS sell-offs, it did sell off a bit, but then actually rebounded quite strongly, um, as a lot of the other kind of SaaS and typical platform companies, you know, Monday.com, you know, whoever else didn't. And I think largely that is because it it um did a really good job of convincing the street that, you know, Crosspilot has a lot of data that is going to be incredibly valuable uh in the Gentech world, um, and also a product that's gonna be incredibly valued to help people understand how they're being perceived in AI. And so, you know, I do think that is definitely a big element of that. Um, I think the second thing is that uh and diving into the the kind of margin and I think a little bit more. Um and by the way, like you don't need to be a company to build out like a database, right? And uh and you know, I think I've even seen this with some private companies. Um, you know, that there's a bunch of non, you know, like well-established non-private companies. We think about like Snorkel AI, um, there's probably a bunch that I'm gonna forget, but you know, that there's a bunch of like very successful private companies that might be the 50 to 100 million to 150 million dollar error kind of range, um, you know, maybe more, maybe less, that if you can build a data business that is 30 to 50 million or even 10 to 50 million with really good margin, um, with really strong dollar retention, which is really important if you're a public company or kind of moving towards that journey, um, it's almost like, why would you not do it? And so, you know, yes, a lot of the stuff that we've talked about is, you know, you need to do it the right way and you need to be really thoughtful about how you do it. Um, but like, I think that's just good execution. And so I think if if you do think about execution in the right way, um building a data business again, if you have data that is valuable that you can convert into potentially an additional revenue stream, can bring you a lot of benefits. And in an age of um, you know, uh typical tech or SaaS companies constantly coming under pressure from AI and skepticism from investors, both public and private, around um, you know, is this viable long term? Um in an age where uh you know companies are sick of paying more money every year to software that isn't really getting any better. But you know, it's the only way for those companies selling into those companies to actually, you know, uh earn more from existing customers. I think if you can offer additional products, additional services to your customer base, um, and you do it the right way, like that should be incredibly attractive um for any business that has the opportunity to do that.

SPEAKER_02 42:05

Interesting. I I'm conscious of time, so maybe one way to end this is and we've discussed a lot of this throughout, but if you to bucket this up as advice as someone to someone joining uh one of these initiatives, what would it be? Maybe your top three.

SPEAKER_00 42:22

Um do the work beforehand. So I think a lot of the yeah, so first you do the work beforehand. I think I think a lot of the stuff that I've talked about you can and should verify during an interview process, and you can do that directly uh in conversation about the vision, the scope of the role. You know, I think if you if you're coming in with a very narrow ownership over something, but but can't actually make decisions that are gonna move the needle cross-functionally, you know, I think you need to figure out if that's something that you want to do. And so I think you can do a lot of the work in the interview process. You can also do a lot of the work um, you know, uh speaking to existing people in the business. Again, if it's a new thing, obviously they won't have kind of a massive insight, but you can get a sense of how the business operates. Um, so I think do the work before, spend time actually fleshing out what is the scope, what could the opportunity be, and do you actually have uh the ownership to be able to execute that? Um I think secondly, uh, and again, this is probably something that's often overlooked. We've talked about um what's overlooked in terms of building, but also like what's overlooked in in terms of like somebody joining. Certain types of data are inherently more valuable than others. And you yes, there will be exceptions to this. Um, but like there is a reason there is a lot of very successful like transactional uh panels uh or data companies that have built transactional panels, and that's because it's directly correlated to revenue. And I think the further away you get away. From the revenue strain in terms of the type of data that you have and the signal that it might give to revenue, um, it just becomes harder to justify. Uh, and it's not that you can't do that, um uh, but it just becomes more challenging when you start to think about will investors pay for this. And so I think think deeply about like what is the data at the moment, what could it be in the future, and how closely is that to, you know, uh uh, you know, forecasting some kind of revenue. And I think if it's really far away, again, you probably need to do a lot more diligence, but also just think about okay, what what will be the pathway here to like kind of really interesting insight? And again, the answer is not no necessarily, but just kind of keep uh keep thinking. Um and I would say thirdly, uh like a lot of this comes back into kind of homework. Um, one of the things that was was interesting about Trustpilot is that the Trustpilot's core business model is really about an open free platform and bringing consumers on to read and leave reviews. Um, you know, it's done a really good job at being very visible uh in Google um naturally, um, and is is now one of, you know, I think one of the most quoted businesses um uh in LLMs in terms of you know information that's been retrieved when people are searching companies. And so Trust Violet has done a phenomenal job at doing that. Um but that is really inherent to its core business model and and how its core business is going to grow. Um, I think if you look at um the challenge between then trying to monetize some of that data, there's a little bit of disconnect. Um, and so I think another important thing is like, what is the core business? Um is is there a um is there going to be something to navigate internally around like, hey, we want to go in this direction, but actually the core business means we need to move in another direction. Um, and again, it's not to say that you shouldn't um dive into those challenges, um, but you just need to be mindful that if there is just an inherent kind of push and pull built into this on top of what there already is, um, you know, that can be uh you know challenging. And so those are probably three things I'd advise.

SPEAKER_02 46:19

No, that's really interesting. And to your last point, when I was assessing the trust part opportunity, I thought a lot and spoke to a lot of people in the industry about the scraping piece. I actually fared wrongly on the side that if there are a lot of people scraping it, there's clearly inherent value in that data set. And actually, if you can prevent that, then there's a an extortion route to monetizing it, I guess. Um, the difficulty that I guess US foresaw is that actually that was a very, very integral part to try SEO, a EO's sort of strategy and in that sort of core go-to-market motion. So that that that then made it very difficult to try and protect the platform in a meaningful way where you can monetize it. So yeah, there's many things you need to consider. That's a very specific example. But even if you're a publisher thinking about monetizing, like you want to have strong SEO if you're not behind a paywall. So, how are you going to protect the platform if you also want to be able to promote the platform? Like completely agree.

SPEAKER_00 47:14

And and and and and that dynamic is so different from something that doesn't run into that, where it's like, yeah, um, you know, we have this data, it doesn't really have that conflict, Red. How do we think thoughtfully about monetizing that? Um, you know, so yeah.

SPEAKER_02 47:29

Awesome. I uh always appreciate our conversations, Nick. I love the way you you reflect and view view the the experiences you had. So I appreciate coming on and uh look forward to catching up too.

SPEAKER_00 47:40

It's been a pleasure, guys. Thanks for having me.