Nevin Raj – Building a Data Software Company at Grata

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(3:12) – What are your thoughts on data vs. workflow as business models?

(7:30) – Do you see data businesses developing into more of a ‘wisdom’ category of companies?

(13:42) – Can you walk us through the different types of data businesses?

(22:53) – How would you describe a workflow company?

(24:37) – Within your business, can you walk through the different ways you can get data from private companies?

(27:52) – What are some of the data sets that correlate most with revenue size?

(30:04) – How do the leading investors you’ve studied or worked with historically estimate the size of a company?

(32:14) – How much do you balance measurable data and intuition?

(34:51) – How do you integrate the data you have with your client’s software and workflows?

(38:11) – What were the early days of Grata like and what did growth look like?

(41:27) – What second-level data sets do you want to add to Grata over time to build a moat?

(44:13) – Do you think eventually most data businesses will become workflow businesses?

(46:49) – What’s a strongly held belief you’ve changed your mind on?

(49:18) – What’s the best business you’ve ever seen?

(50:56) – Are there any insights you’ve gleaned from ZoomInfo you want to apply to Grata?

Alex Bridgeman: It’s good to see you, Nevin. Thanks for joining us on the podcast. There’s tons of stuff within data businesses I would love to talk about. And obviously you’ve done a ton of thinking around data businesses, since you run one. I would love to hear kind of your thoughts on this concept that you brought up a few times, which is kind of data verse workflow and how the two relate to each other and key differences you’ve noticed between the two businesses and which are kind of most interesting to you.

Nevin Raj: Yeah, Alex, thanks for having me on today. Excited to talk about this, because this is definitely a topic that’s near and dear to my heart having built a company that actually straddles both of these different concepts. I think it’s actually fairly common for data and software businesses to have a mix of both data and workflow. So, we’ll talk a little about Grata. I will talk a lot about other case studies, examples of companies, that have done this or are doing this and doing it well. But I want to start off by talking about just in general what makes data so special to create a business around because data businesses have not been around as long as other industries. It’s actually a fairly new, nascent industry. And there’s a reason for this, and part of it has to do with the Internet. But what we’ve seen most recently, because of digitalization, is access to data has increased exponentially. And data is special in the world in that there’s a third law of thermodynamics, that entropy is always increasing in the world. And this is almost the same with data. As time goes on, more and more information, more data is created. And that’s actually the opposite of, let’s say, natural resources, which are constantly being depleted, or manufacturing industries where you’re building something based on some finite or scarce resource. So, with a lot of those industries, you have to be concerned about your supply and your supply chain, and the prices of commodities. And actually, your cost structure going up as time goes on. What’s interesting with data businesses is there’s just more information coming into the world. And I’ve heard this phrase of data is the new oil. And I think that is right in a sense where data is valuable like oil is valuable. But it’s different in that oil is a very scarce resource. And we are moving in a world towards renewable and renewables and different sources of energy. Data is only going to become more and more important as we go on and as time goes on. And there’s a famous graphic that I love that shows a bunch of dots on a page, and it says this is data. And then in the second frame, it has these colors in the dots, it’s blue, it’s purple, and green. And it says this is information. And then in the third frame, it connects the dots, and it says this is knowledge. And then in the fourth frame, a few of the dots are lit up. And this is insight that you find from the knowledge. And then in the fifth frame, it connects those two lit up dots and says this is wisdom. I love that graphic because I think that is really telling of how this industry is maturing and where things are going. Now you have the data, which I think of a data business that collects information, and their outputs are data feeds. They’re essentially Excel spreadsheets. They are databases. It’s very raw. It’s unprocessed. It’s one level actually below information. I think about information as that next level above data, where you don’t just have raw collected pieces of data, but you enrich it with some other knowledge to essentially create some type of information. I think of the third tier, this knowledge and insight component, going back to that famous graphic, this is where we’re going to go, we’re going to talk about where we get into a workflow and really the convergence of SaaS businesses with data businesses. And then finally, that area of wisdom, that’s where human judgment comes in. That’s where consultants and data analysts and the people layer comes on all this. And we’re not going to talk about wisdom so much. But we’re going to talk about how data and insight are really flowing together from a business perspective.

Alex Bridgeman: It seems with some of these frames that you’ve mentioned with this graphic, as they get closer to wisdom, currently, there’s consultants and people doing a lot of the wisdom pieces, but it seems like pretty quickly data businesses are catching up and data businesses initially perhaps were only doing information and then there’s knowledge, and then as they get smarter and there’s more development with those data products, suddenly insight and wisdom are becoming more apparent in some of these. Do you see that development path with data businesses, like they get closer to that wisdom category?

Nevin Raj: Actually, wisdom is another way of saying machine learning. And it’s taking what we learn as humans and turning that into an algorithm and institutionalizing that. But what’s happening is that type of wisdom that you build with ML or AI actually goes back and fuels insight, because then you have people always interpreting that, staying one step ahead of the machines. So, I still think that while there is this push towards wisdom, people are always one step ahead, people are always going to interpret and make decisions off the latest information they get, that may or may not align with what the system or what the advanced data provider in this case tells them.

Alex Bridgeman: Yeah, in many ways, it kind of relates to some of our earlier discussions on Bloomberg, where these companies that blend media and data together, they have this data, these knowledge and insights, and then their reporting and editorial team is the one pulling wisdom out of those insights and writing them up in the form of articles and content and podcasts and what have you. That’s a pretty interesting way to see, like okay, media data is like some form of blending people and wisdom and data together. But data on its own seems to be quickly approaching that wisdom category too, like you said with machine learning.

Nevin Raj: Yeah, Bloomberg is a great case study of a business that got really good with pricing data for equities and commodities and all these different securities, and brought it into a real time system. But then what they did was they really facilitated collaboration. And if you talk to most users of Bloomberg, they’ll rave about Bloomberg chat, of how they at a hedge fund can talk to a banker or talk to another fund and actually make a deal. And Bloomberg is not set up to be a clearing house, but they are helping transactions clear. Because you can take the information and the knowledge and the insight you get, and then you can make decisions. And that actually forks from wisdom because there’s some element of decision making that a customer makes. But Bloomberg as a business, as you mentioned, has gotten into this wisdom pillar through media. Bloomberg.com, their landing page, is all about the media and all about the insight that they draw from their data and are sharing and interpreting with the world. We’ve actually seen this happen, even outside of Bloomberg and other businesses, CB Insights, where they started off initially collecting funding data and getting better information on VC funding. And then they actually started marketing this really well with these marketing maps and daily newsletter. People would read the newsletter, see the market maps, and say it was really interesting. And they were using that to draw users in to subscribe to their software. What they found out was that the market maps and the research alone are also valuable. And they created a pricing tier where companies are paying six figures and above for that research, for the wisdom that their team can provide that sits on top of the data, the information, the insight and the knowledge that they provide. I think that’s a great example of a more modern, it happened in the last 5 to 10 years, of a company that’s gone through that transformation.

Alex Bridgeman: Yeah, it kind of reminds me of Politico Pro. I don’t know if you’ve followed that company or that team, but they have a similar content, in depth reporting, and data product for politics. So they’ll have data on different races or progress of different bills. And they have all this data or like funding data as well, but then they have a really in depth reporting team that covers it all and sends out daily newsletters or there’s even alerts. So, if you want any alert for any news for one particular person or bill or what have you, you can sign up for those as well. And so it’s kind of a- it’s a similarly priced product too as something like Bloomberg, which is pretty fascinating to me, where it’s not just data or it’s not just raw numbers anymore, but there is a strong insight element to Politico.

Nevin Raj: Yeah, and I’m not familiar with the specific business of Politico, but a couple of my classmates started a company called Quorum based in DC that sounds pretty similar. Basically, what they do is they collect data about legislations, about what’s happening in Washington, and they sell that information to investor relations departments and lobbying departments of large companies. What they’ve really created, again, back to data versus workflow, they’ve created a way to manage your political communication. So, it’s not so much- the value they’re not providing is the raw data, it is I get all this information about what bills are passing in DC, how this relates to my business. So, let’s say you have a climate bill passed, it’s related to emissions; you’re GM or you’re Ford, you need to know about this, how that’s going to affect your production of cars and your stance on electric vehicles. That’s really important for you. That’s the value that I’ve seen that Quorum brings to the market, again, transcending data and moving to helping someone get a job done.

Alex Bridgeman: Would you walk us through the different types of data businesses? You’ve talked about there’s obviously like public or non public data businesses. Would you say those are the two main types of data businesses? Or are there perhaps one or two that I’m missing as well?

Nevin Raj: Yeah, I think that’s the general structure, and that’s right. The first thing I want to talk about is when talking about data, the buzzword that people use is proprietary. I want proprietary data. It’s data that no one has. It’s super secret. All data, to some extent, is proprietary, whether it comes from the public or the private domain, it’s proprietary. The more proprietary you are really just depends on how much you synthesize that data. Again, coming back to our framework of data, information, knowledge and insight, the more you can bring that data into information and knowledge and insight, the more value you add. So just caveat data, what is proprietary? Proprietary can still mean public data. So therefore, I’d break this down versus where does the data come from? There’s not public or private, and then there’s publicly available data. And let’s start with that first bucket of not public data. I think there are a couple of, actually three sub segments within this. The first one that we’ve seen is data that comes from a network. And this is where you make sense of someone else’s data. The best example of a business in this space is Nielsen. Nielsen has multiple businesses, but let’s take their consumer retail business. They buy in store retail data, and they buy this from all the major retailers. I think it’s like Target and Walmart and you name it, Macy’s, JC Penney, whoever. They buy this data. And what they do is they look at the flow and the sale of products in stores. And IRI actually does this as well. And the way that business works is they actually get flash drives and Excel files. And they used to get mail to them from the retailers. They compile it. They look at the data. And then they send back to their contributors kind of anonymized data of how they’re performing relative to their peers. They pay them, they provide them with anonymized data for being part of the network. And they take all the data from all the contributors of the network, and they sell it back to people, like investors and other people and brands who want to know how their products are performing on the shelves relative to other products and relative to other retailers. And that’s a great example of a network-based business where that data is not public. Its proprietary. But really what is proprietary about Nielsen is the relationships they’ve made and how they make sense of all the information they get. Another business that has done this is Verisk Analytics. They are a contributory network for insurance data. And they sell to insurers to help them underwrite risk and help them with their actuarial models. And again, they’ve built a really great contributory network, just in a different space. So that’s data business type one, contributory network based. The second one is a license or partnership model. This is where you don’t own the data. But you go to someone who has the data, and you buy it from them. Or you say, I’m going to give you a rev share, I’m going to give you 10%, 20% of my sales. And this is a business that sits again in the value chain in that second level. And one example would be Second Measure. They go to credit card authorizers and banks, and they buy credit card transaction data, and they’re actually owned by Bloomberg now. But they buy that data. They have to clean it. There’s a ton of cleaning involved. It’s very messy. They have to match it to vendors. It’s a very, very hard problem. And what they do is they ultimately provide insights into where consumers are spending – the products, the categories. And this is beyond Nielsen. It’s just in store, credit cards, it can be in store, it can be restaurant, it can be ecommerce, online, DTC, and that’s really powerful. But they’ve taken a different model. It’s not so much a network where they come in and anonymize it and sell reports back. It is really purely we buy the data, we license it, and then we make sense of it. The third type of data business that sits in this not public category, where information isn’t accessible to the public, is I call it organic, where they’re actually the creators of the data. And they use that to provide insights across the value chain and workflows across the value chain. A great example of this type of business is Facebook. Facebook collects tons of data on what people like, what people interact with, what they’re talking about in posts and in messages. They are a data business at the heart because what 99% of people don’t see, we all see a Facebook profile and an Instagram feed, what we don’t see is the admin portal behind it, the advertiser portal, where they help marketers go in and place ads and target the people and the demographics that they want and really the behavioral segments they want to get in front of. And it’s a huge part of Facebook that most people don’t see, which I find really interesting. But what they do is they collect, they organize, and they distill this information to help you, again, get something done, in this case help the marketer reach an audience. That’s actually the same thing for Yelp. So Yelp has a public facing platform. You go onto Yelp, read about a restaurant, write a review. The way they actually make money is they take that information and they sell it back to the businesses. A business can claim their profile. And what they can do is they can advertise on profiles that people are viewing similar to theirs, meaning if a person goes on profile one and then jumps to profile two, Yelp tracks that and they say, your profile, these are your competitors, here are the alternatives to you. So, it may not be an Italian restaurant competing with another Italian restaurant, maybe an Italian restaurant competing with a sushi restaurant because they’re close by and they’re in the same price range of people who are looking at them similarly. And Yelp says here are the businesses and alternatives to you. You can advertise on their pages. And you can pay for insights for how many views you’re getting, how many impressions you’re getting. And they’ve, again, turned what looks like to us a consumer facing business into an advertising business and a workflow for marketers who are trying to reach an audience. So just to recap, we have there’s public versus private data. Within private data, we have network based, license based, and organic. Now the second segment, this is where the data is publicly available. And again, I repeat this point of it doesn’t mean this isn’t proprietary. A lot of this actually, and most of this is proprietary. It just comes from a different source. It may come from the internet. It may come from government sources. This information is still often hard to get. It’s hard to collect. It’s hard to organize. It’s hard to distill. One of the biggest examples of public data is S&P. S&P has many, many different products. But their Capital IQ product is really taking and organizing SEC filings, 10Ks, 10Qs, annual reports, and helping spread comps which used to be done manually. You used to take the tables from a PDF of a 10K and put it into a spreadsheet and spread all the financials. This was banking 10 years ago. They totally automated that. And there have been other new age companies like Alphasense and Sentio and Bev Sec, a couple of them have been acquired since, that have actually done this in the modern way and found new insights and used machine learning and AI to not only extract data but make these filings searchable and make unstructured information found more easily. But again, it’s public information. Anyone can go to Edgar and find it. It’s just really hard to make sense of it. And again, when I come back to another example of Grata and my company, we make sense of company websites. And yeah, can anyone go to a website? Sure, they are publicly available. But can you make sense of 100 million web pages and distill that down into 10 million companies and what they do and how they do it and who they do it for? That’s really hard. And that’s where data businesses have a leg up and where their proprietary nature comes in. It’s how they interpret what is available to then, ultimately, power a job to be done and a workflow, which is the second part of what I want to talk about today.

Alex Bridgeman: Yeah, so would it be fair to describe Grata as a workflow product? And then diving into that, how would you describe your workflow company?

Nevin Raj: Yeah, actually, that’s what talk to our customers and prospects about. I tell them yep, there’s data in Grata that’s underlying what we do. But we are a workflow software. We help you get a job done. And that job for Grata is we help investors, investment bankers, corporate development teams, source their next deal. We help them find companies that they want to engage with, whether that’s for an acquisition or a capital raise or helping you find good companies. And to find good companies, there’s data, you need to have companies of a certain size and growth and value and ownership structure. And there’s all this information you need to know about the company, what it does, what industry it’s in. But ultimately, you’re searching for companies, you’re collecting and processing information, and then you’re running a process. You need to reach out to a CEO. You need to take notes on the company, label the company. You need to manage a funnel and a pipeline. And that is more than just data. It’s process. And if you just have data, what happens is that you lose your power in the value chain. So businesses that have stayed data and just done data, and I gave you big examples. I told you Bloomberg and S&P and Facebook and Yelp. You’ve heard of all these businesses because they’ve transcended just the data. And they’ve gone to helping people get a job done or helping people and businesses get some type of value, which is why our belief at Grata is the data is great, but the data is nothing if you can’t use it to get something done.

Alex Bridgeman: I like that framework. So, within the Grata business, so can you walk through the different ways that Grata gets information on private companies? We’ve talked about stuff like you mentioned their website and perhaps LinkedIn and some other maybe public sources or PPP data that’s available. But where are you pulling from for gathering this private data?

Nevin Raj: Yeah, for us at Grata, we start with a company’s website. We look at the universe of all websites. It’s actually very similar to Google. When a website is registered, it comes online, comes through Grata’s system, and then we crawl it. And we have machine learning that reads it and interprets it, and tries to figure out, is this a business? If so, what type of business is it? What’s this sector? What’s the business model? Who did they sell to? And then we do something called indexing, which is making it discoverable. Because our goal, our vision, is to make companies more discoverable to the investment and financial community to allow for better flow of capital. There’s a $14 trillion of Boomer capital that is going to turn over the next 10 to 15 years. There’s 4 trillion of dry powder in private capital that’s trying to invest in these businesses. So, there’s clearly a market here to be made. And our goal is to make these companies more discoverable. So, what we do is we take this public data, these websites, process them, index it, make companies discoverable. And that’s the core. But there’s a lot of other ways that we get information and data. And when I explained to you the different types of data, I talked about private versus public, network, license, organic. Usually, it’s not cut and dry. So, what happens is the data business will start in one spot, and then they’ll expand to different types of data and actually have a more diversified strategy. So, while we started Grata with all publicly available data from a company’s website, we then moved into licensed and partnered data, where we built relationships. An example is we have funding data and startup data that comes from CrunchBase, who we have a partnership and a license with. We work with Similar Web to get web traffic data. There are a lot of other companies that have these similar partnerships. So those are just two examples of ways that we found insight from an existing data vendor and existing peer. And we’re using that data in a different way to power our workflows. But it is a mix between what’s publicly available and what’s not. We’re getting into other sources of data, for example, organic, from all the data that we have, and that our users- for example, we have a revenue estimate feature on Grata. And we have a button you can click to say it’s too high or too low or it looks about right. And that is information that we use to collect to train our models to get smarter. And that’s getting into contributory and data that the user provides us for us to get smarter and for them to get a better experience. So even we at Grata are evolving our model of data as we think about diversifying our strategy but also ultimately providing more value to our users.

Alex Bridgeman: So, I’d be curious, what are some of the most correlating data sets that you have on private companies that correlate most strongly to revenue size?

Nevin Raj: We’ve done a lot of research on this topic. And it could be its own entire hour of conversation. The leading indicator that’s used right now in the market is headcount. And you can go back and you can distill almost any business, there are a couple of exceptions, but you can distill almost any business down to how many people they have, which is relevant to their scale. And the reason for that, and there’s different scale factors. And we’ve tried looking at locations, we’ve looked at web traffic, we’ve looked at funding. They all play a role in how you calibrate this. But by far, the strongest one everything is correlated with is headcount. And let’s take like a manufacturing business that doesn’t need as many people; what matters is the size of their facility. That’s the driver of their revenue. If you have a bigger facility, you can produce more, you can get more revenue. Versus let’s say, a professional services business where like a law firm, you’re billing by the hours for lawyers. So your revenue can only be proportional to how many lawyers you have. If you look at the headcount- So that one’s obvious. If you look at the headcount of a manufacturing business, the bigger the facility, you’re going to need more staff. You need more maintenance people, you need a site manager, you need all this different staff to operate and make sure the facility is working well. And that scales. It scales differently than the professional services business, which is more one to one with revenue. But it’s still going to scale. And so therefore, if you can get the scale factors right, if you can break down the economy into the thousand different sub sectors that each have a different scaling factor, then scale your revenue based on the sub sectors, then what you get is a really nuanced and usually highly accurate way of assessing a company outside in.

Alex Bridgeman: So it sounds like that’s- obviously Grata is taking in a whole bunch of different data sets. How did the leading investors that you’ve studied or worked with, how have they historically or currently estimated the size of a company?

Nevin Raj: Yeah, well, it’s not rocket science. I think everyone does it pretty similarly. They look at the headcount, and they apply a multiple. It’s the way this industry has been trained to work on multiples. It’s how we value businesses. We look at revenue, we look at EBITDA, and we say, this is a 4x, 8x, 10x, 20x, depending on the scale of the business, the growth, the margins, the customer concentration, the defensibility. Valuation is an art. But because everyone’s used to valuation multiples, that’s the same way they size a business outside in. A typical investor will take the headcount and say, hey, we know this type of company roughly has $150,000 per employee or makes $250,000 per employee, and they just apply a flat multiple. The other way they do it is they’ll say, and this only works for venture or PE backed companies, they’ll say this company has taken in $100 million dollars of funding, let’s just say, and we know that in this round, they give up X percent. So it’s a series B, they’re going to give up 10% of the company, so it’s worth roughly a billion dollars. We know to be worth a billion dollars, you need to have 50 to 100 million of revenue. Or they will look at the growth rate and say, okay, based on this like fast growing company, maybe they could get a billion dollar valuation with 20 to 50 million of revenue. Or, hey, it’s not growing as fast, they look more stable, maybe they have 100 to 200 million of revenue. Or maybe it’s a consumer business, so they’re lower margins, we think they have 100 million of gross margins, which is going to trade at like a 10x. And that’s the math they do. So they kind of take what they can observe, whether its employees on like a LinkedIn, which is probably the most observable for an investor, and funding, which is going to be in a TechCrunch article or The Wall Street Journal or on a Pitch Book profile, and they use that to back into it.

Alex Bridgeman: It sounds like some of that estimating is based on past experience of those investors or intuition. How much of that do you use at Grata? Or do you try to stick to measurable data as much as possible and kind of try to set aside intuition as much as possible? How do you kind of balance the two?

Nevin Raj: You kind of have to take intuition as the input to machine learning. When you’re training a model, you’re getting it to behave like a human would. Take, for example, a really challenging problem that we try to solve is how you classify businesses, specifically software businesses, which are really complex. Is data software? Is data a service? Is blockchain software? There are these nebulous fields that transcend different traditional industries. And our view on this is you get the models to believe and to act like the intuition of the people and the people who are your customers. Customers are going to believe and act in a certain way. And especially if you have a concentrated market and you have product market fit in a particular use case, it’s easier to train the models to work in a way that’s relatable to your end customers. Because our end customers will generally say, data businesses and blockchain businesses fit in the software bucket. But you could have other industries that say, no, that’s actually not the way, not what we believe. So, we’ve trained our machine learning models to match the expectations of our customers. Now, is that a self-fulfilling prophecy in some way? Do you limit insight? You can argue around that. I’ve seen both sides of the coin with that. But ultimately, it comes back to this theme here of data versus workflow. And our users are trying to get a job done, they’re trying to find companies to invest in or acquire. And if they have a certain way of believing and a certain way of assessing companies, if we lead them down a different path, unless that path is more right, and we can prove that with evidence, sources, and methodology to build trust, they’re going to just go back to their old way. And if they have to unwind what we’ve done to go back to their old way, it adds time. So a lot of this is meeting your user where they are, and then inching them along that journey to get them to a more right or more sophisticated answer. And we think a lot about that when we think about intuition and machine learning and how those relate to the products that we produce.

Alex Bridgeman: And how do you support that workflow? So how do you take the data that Grata has and integrate it with other tools and software and workflows that your clients have? Is that through like API’s or some software connection, something else that’s going on there?

Nevin Raj: The core of what we do is we have a SaaS application. It’s a cloud-based web-based application you can log into. And that application has different entry points and different connectors, so to speak. And the common user will come in and just run queries and find companies and research companies. But we’ve created a suite of other ways to enter the Grata universe, so to speak. And one way, and what’s really common, is we have a Chrome extension. And this is getting in your browser. So you see Grata, when you’re on a website, you can see all the information that Grata has on that website. When you click a button, you can jump right into Grata. And a lot of other companies have done this and do this, have Chrome extensions. So, it’s quite a common way to get someone back into your platform and to add value at the point of their workflow. The other thing we do is we realize that there are a lot of other tools that people use, and you have to play well and integrate with other ones in the ecosystem. So, we built integrations with some of the key players in our space like Salesforce, Deal Cloud, HubSpot, coming up is Affinity. These are the CRMs that investors use. And they are storing different information. While they’re using Grata for their top of funnel company searching and deal sourcing, they’re using these CRMs to manage relationships, manage middle office, back office functions, multiple sources of deal flow. So, to make their lives easier, we have to be integrated with the CRM. The other ways that we see people consume our information and consume our product is through APIs where we release our capabilities. We have a search API, we have a similar company search API, where if you’re building an application in house, most of our users who do this are building a proprietary in house tool, they may not be able to use their UI if they’re building their own UI that’s accustomed to their organization. And so, they want to use our capabilities, and they’ll use our API and call our system and provide back not necessarily the data but the search functionality that we provide in our UI. That’s another, again, route to market that data businesses have taken, it’s SaaS, it’s API. And then the other route, and this is in the raw sense, is a data feed or what we call a flat file, where we just provide- it’s almost like if you hit Export in Grata every day, you’d get your flat file back with all your data fields. It’s rows and columns. It’s not a multi dimensional structure. It’s a two dimensional structure. We provide that too for the ones who really want to come back and consume data. Going back to our framework of data, information, knowledge, insight, wisdom, our flat file is our data, our API is our knowledge, and the platform and the UI gets you up to insight. And we let people engage in different parts of the value chain or different parts of the data stack.

Alex Bridgeman: There’s this article written by Abraham Thomas that talks about the economics of data businesses. And one of the things he talks about is that data businesses typically have slow beginnings, where it kind of takes a while to get your data and products set up and various other things. But over time, they tend to accelerate in terms of growth and adoption. I would love to hear kind of what were the early days of Grata like, and how was that growth path been for Grata since founding?

Nevin Raj: Yeah, I think I’ve gone back and forth about the need for critical mass and how things get started. And the slower path is you go out and you collect a lot of data, as I mentioned, you build partnerships and licenses, or organically, you collect a lot of information, or you make sense, you build a network, it’s got to convince a bunch of people to come in and contribute data. All that takes time. So a lot of the big data businesses you’ve heard of, like Verisk and Nielsen and Bloomberg and S&P and others have all taken- they’ve gone through, they’re huge now, but they’ve taken a long path to get there. The way the modern data business has short circuited this, and same thing with Grata, is because there’s so much available out there, the amount of information, we talked about information entropy is increased so much, is you can pick a starting point with more than enough public information. And that’s where we started. We said, there are hundreds of- there are billions of websites. If we just figure out the 100 million sites that matter and the 10 million that are companies, we can pretty quickly, within a year, ramp up, build to scale, and build the automation to make value and make sense of this. And that’s where we started. So, we picked an untapped potential, it’s sitting right in front of our eyes, of information, to then build a business on and then have expanded to other components of licensed data, organic data that we create, contributory data that we receive. We actually broke what I call the chicken egg problem with that public data and grew the business quite rapidly. Andrew and I ran a separate, like a bootstrap data business that we called Grata Data for four years. But Grata as it is today is a SaaS business. It took us less than a year, in late December 2019 to about November 2019, to really- or no November 2020, sorry, to build up the critical mass that we need. And we were actually selling subscriptions as early as January, February of 2020 without even having crawled every website. I think we had like a million companies at the time or a couple hundred thousand companies. But people still found that valuable because it was more than nothing. And it was allowing them to discover a universe that was bounded but still a universe they didn’t have access to. So, for us, it happened very rapidly because we took a source that was out there and made sense of it. And then other parts, other different data collection mechanisms are going to take us longer because they just come like network just comes with more usage and more people in your community.

Alex Bridgeman: Yeah, so what are those second level data sets that over time you hope to add to Grata to kind of intrench and build your moat around your data business then?

Nevin Raj: We think a lot about how you gauge a company size, and we spent a lot of time around more data that’ll help drive a better revenue estimate. And right now, we’re working on data around government data sources, PPP, others that are public, but they’re really hard to make sense of, and no one’s really done a good job of connecting this and making sense of this information. And we’re starting there because it gives us a point where it’s free, it’s public, it’s out there. And there are other niche industry specific datasets we’re thinking about to help us with size, like claims data for the healthcare industry, or credit card data, transaction data for the consumer industry, which is more of an industry specific approach. So that’s one bucket. It’s all about how do you gauge the size of a company? Another place that we’re thinking a lot about is how you value a company? What’s the price? Because we’re a deal sourcing platform, you need to know who much you need to pay for a company, what’s the value of that business? How much should you pay for it? What are the multiples out there? Again, this is public data that’s made sense of because you can go out and get public company multiples, but you need to know what are the right multiples to use, what’s the context, how to adjust them, and that’s all the other information we have that really provides value and sets the context on that. So, thinking a lot about that. The other place that we think a lot about, and what’s next for us, is related to user generated data for the user itself. So a user comes into Grata- We’re building a feature on labels where a user can tell Grata whether a company’s high priority, low priority, or medium or low priority. And we’re going to use that to give the user a better experience. You’re telling us you like these companies, and you don’t like these companies, we’re going to start surfacing more companies like the ones that you like, more companies that make it through your pipeline. You’ll be able to submit a status to Grata to say, this company is- we’ve done a deal with this company, or we’re in active conversations. And so having that information, it’s kind of like a dating app – you say, I like or I don’t like this person, and the app gives you recommendations. That’s kind of like the vision for Grata of let’s go up the chain, the value chain, and not just give you information, but let’s give you insight, and let’s get you closer to where it’s wisdom, where you’re telling us what you like, just like on Facebook, you like posts and you say what your preferences are, we add that or our users are starting to give that to us, especially with some of these new features. So we want to provide them with a better, more curated, more personalized experience in the application.

Alex Bridgeman: You’ve talked about data businesses and workflow businesses and how workflow businesses will take a raw data set, but then make it usable through your dashboards and integrations and other stuff. Do you think eventually most data businesses will become workflow businesses, and the two kind of basic ideas or basic concepts will merge together in some way?

Nevin Raj: I think about this as a value chain. And the analogy is in manufacturing. You have raw materials companies, you have manufacturers of building materials and building products, you have the ones that make the end consumer products, you have the distributors, the retailers. This industry is going to look very, very similar in the next decade. And there will always be specialists along the way. But the most valuable companies, where the industry is going, is towards a more integrated view. I still love the example of Yelp, I love Four Square as another example. If you get people, if you provide some value, you give people a place to find great restaurants and businesses and review them and you’re going to get interactions, you will always create data. Information entropy is always increasing in the world. So anyone that creates a great product or great service can and will collect information. And if you do that in a tech enabled way, and you have the infrastructure to capture that, then you will create data, and you will be able to monetize that data, whether that’s selling the data raw or selling around workflows, that is always happening. And the biggest and the best companies have been thinking about this. They know this is changing. And I worked at McKinsey for several years. And I worked in the tech team. And we saw a lot of our clients asking about how do we make sense of data? How do we turn this from a cost center to a revenue center? That was nine years ago, seven years ago. A lot of that’s happening. But it’s also happening on the way up the chain. A lot of the data businesses, the data startups that I see and that we talk to more and more so saying, we’re creating dashboards around our data. That’s like the first place to start, dashboards and ways to view this. Oh, now we’re creating tools to help you get whatever job done, whether that’s make a decision or place an ad or do some type of- I like the jobs to be done framework. You’re seeing data businesses move up the stack, too. So I think it’s natural. It’s the way you capture share once you do something well. I think it’s the future of this industry. And there will be some specialists. The most valuable companies will be generalist. But generally, I’m a believer in the convergence of these two.

Alex Bridgeman: What’s a strongly held belief you’ve changed your mind on?

Nevin Raj: When we first started Grata, I would always come up to Andrew and say, “Andrew, we need to have some assets, like we need to collect something that’s quote unquote, proprietary, no one needs to have it. That’s what we need to sell.” And the belief that I really changed my mind on was that you don’t actually need something secret or private or, quote unquote, what the industry calls proprietary to provide value. You just need to collect, organize, and distill information for people to help them get a job done. Whether that’s if you literally physically go to your government, your local government offices and collect paper that they have, filings, and make that digital and help someone solve the problem of permitting or construction, you’re adding value. And that’s the same thing in our world. There are many, many analogies of this. But I always thought oh, you need something secret, no one can have access to it, it has to be totally proprietary. And I realized very quickly that’s actually not true. So that’s probably one belief that is very relevant to the Grata but a belief that I had that has changed. I think the industry has also come to realize that as well, because it’s so hard to make sense of what’s out there. There’s a lot of information out there. So the industry has realized it’s not about the information. It’s about how you make sense of it.

Alex Bridgeman: Do you think that makes data businesses less defensible over time, if the data is not necessarily the proprietary source of value for that company? If its analysis over time, would you think that given a five year time horizon, that will be easier for a competitor to replicate versus some data set that that competitor might not ever be able to have access to?

Nevin Raj: Just as easy or hard as it is to make shoes. Anyone can go out and go to a rubber company and buy the soles and then manufacturing of the fabric and make the shoe, but why there are only a handful of really good shoe companies? It’s hard. There’s a whole value chain to make sense of the raw materials to make the end good. So, while it’s out there, and we think it’s easy because we can access it from our computer on the internet, it’s really hard to make sense of it and to create insights. So I think, yes, to some extent, it is accessible. But I guess I don’t think this industry will look any different than manufacturing, for example.

Alex Bridgeman: I didn’t think we’d be comparing data businesses to shoe businesses. But I love that analogy. What’s the best business you’ve ever seen?

Nevin Raj: One business we really admire is ZoomInfo. And they’re kind of tangential in our space. They have taken something that’s really hard to do, which is collecting contact information on people. And they did it in a multifaceted way. They got public data, they licensed data, they created a really powerful contributory network. And they did that really well. And their tagline used to be on their website, hit your number, that was their slogan or tagline. Now, it’s all about go to market. And what’s really interesting is they’ve evolved as a company realizing it’s not just about getting data on contacts to increase your sales, but it’s about how do you bring your product or service to market. And they’ve made acquisitions, they acquired Chorus AI for sales intelligence, they’ve acquired many, many different types of companies to help them expand across the value chain. And that business took a long time to build. I think they did a great job of starting off with a data source, expanding the ways they got data, and then now expanding the problems that they solve to be the salesperson or the marketers end to end solution for going to market. I think it’s a pretty cool thing that they’ve done. And of course, it’s gained a lot of value. At one point, they had the highest multiple, revenue multiple in the industry. I know markets have corrected now a little bit. But it just shows in their valuation of how the investors market values that business.

Alex Bridgeman: Are there any particular insights that you’ve gleaned from studying ZoomInfo that you’re starting to apply with Grata or want to apply at some point?

Nevin Raj: Yeah, it’s that data diversification of start off with one type of data source and move in and diversify and use that to add value to your users. And that’s what, ultimately, not just builds a moat but also creates more and more value. And we really believe in the community element of Grata. We think of our space as big but tight knit; people know each other, people switch jobs every couple of years and go to a competing or neighboring firm. And that means that there is value in that community. And I think ZoomInfo has done a great job of their building value in the sales community. And I hope we follow a similar path as we figure out ways to engage that community. And same thing with Bloomberg. They’ve had a great way of engaging their community through the Bloomberg chat, not saying that Grata will have an in app chat, per se. But the theme of community is really important to us, whether that’s with our own company and our own employees, or engaging our customers, or prospects, anyone in our universe.

Alex Bridgeman: Yeah, I’d love to see a chat feature at some point. That’d be actually a pretty cool feature. Thanks for coming on the podcast and sharing a little bit. I’m excited to see what you guys build with Grata and what it eventually becomes. So, thanks for sharing a little bit today. Appreciate it.

Nevin Raj: Alex, thanks for having me. It was a pleasure.