Deploying and Scaling Data Solutions With Mark Hookey of Demyst

Mark Hookey is the Founder and CEO of Demyst, a company that deploys data across the globe to solve risk, automation, and growth objectives. Before creating Demyst, Mark was the Managing Director for LexisNexis, a Partner at Optimal Decisions Group, and Consultant for Corporate Value Associates. 

Mark graduated from the University of Melbourne with a bachelor’s in engineering and commerce. He earned his MBA in business administration and management from Columbia Business School.

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Deploying and Scaling Data Solutions With Mark Hookey of Demyst


In this episode…

Are you looking to accelerate and improve the way your business defines external data? How can you utilize and harness data to scale your brand? 

Mark Hookey and his team at Demyst have the recipe for your data solutions. He is helping enterprises solve and improve processes so you can begin using data to scale your brand as fast as possible. If you’re looking to expand your data capabilities, you do not want to miss this episode. 

In this episode of the Key Insights Show, Scott Johnson sits down with Mark Hookey, Founder and  CEO of Demyst, to talk about the value of external and use case data analytics and technology. Mark discusses how the data analytic space meets consumer needs, the flexible options available for businesses, and creating a secure and compliant environment.

 Here’s a glimpse of what you’ll learn: 

  • Mark Hookey explains the controversy around creating value with clean data 
  • How external data technology can meet customer expectations
  • Mark discusses streamlining the enrollment process with Demyst for scalability and customer access 
  • What are the advantages of restricting your focus to one category when growing your brand?
  • Mark explains the responsibility of effective security across data environments 

Resources mentioned in this episode:

Sponsor for this episode…

This episode is brought to you by Key Marketing Group.


At Key Marketing Group, we specialize in getting more inbound leads for B2B companies. Our proven process works time and time again to drive more potential customers to your website — and get them to contact you. 


More leads in as few as 3 months. Get started today

Episode Transcript

Intro  0:02  

Welcome to the Key Insights Show where we feature CEOs and how they’re thriving in today’s market. Now, let’s get started with the show.

Scott Johnson  0:13  

Scott Johnson here, I’m the host of the show, and we are starting a new series talking with CEOs of SAS and tech companies about challenges they’ve overcome, and how they are thriving in today’s market. This episode is brought to you by me, I’m the owner of Key Marketing Group, and we specialize in helping b2b companies get to the top of search engines. Our proven process works time and time again, to drive more customers to your website and get them to contact you rankings in as little as three months. Get started today by going to key That’s Today’s guest is Mark Hookey. He’s the founder and CEO of Demyst, a company that deploys data for the world’s leading financial institutions. Prior to that Mark helped build an insurance analytics business, which was acquired by LexisNexis. Basically, Mark is the guy to talk to you when it comes to data tech analytics, and much more. Mark, welcome to the show. 

Mark Hookey  1:08  

Thanks, Scott. Great to be here.

Scott Johnson  1:10  

Thanks. I know you were the big data guy, you’ve been doing this for about 20 years, going back to when data was barely even digital. So what were kind of your early days in this industry like?

Mark Hookey  1:22  

Well, it wasn’t called big data back then. And, and a lot of the buzzwords of today, we’ll call different things back then, but But you know, I worked with some folks way back when that were involved in building some great predictive modeling technologies in the insurance space. And we were building solutions that were deployed into personal loans, insurers to help with things like claims estimation and price pricing. That was all compiled desktop software, multi year, multi million dollar bills with large enterprises. Working with insurers in particular, I still to this day, think they’re some of the most sophisticated consumers of analytics and technology, and they were back then. So it was a great, a great domain to, to cut my teeth in the, in the interesting space of the intersection of data and analytics.

Scott Johnson  2:18  

Yeah, and data has kind of become this four letter word over the years. And but it’s funny, it’s used by so many companies, but it’s such you know, a hot topic, you know, in the news and things like that, but it’s so useful. So many industries, I mean, insurance, you know, they have to, you know, their balance sheet relies on the quality of their data. What is your take on kind of this hot topic of big data, you know, good versus evil type of thing right now?

Mark Hookey  2:40  

Yeah, well, through the development of DMS, and also my career, we’ve seen this evolution down the value chain, it was that analysts a point of differentiation, and people had their people had sort of basic internal data, like, if you’re a bank, you’d know what product somebody had, or if you’re an insurer, you’d know whether somebody filed a claim or didn’t file a claim and, and all of the basis of competition was around who could build some fancier mousetrap algorithms sitting on top of the data. But what’s happened as the analytic space is mature, and great platforms and companies have both standalone companies as well as the large cloud players have created a much, much more scalable commodity infrastructure for how to build amazing analytical models, everybody’s starting to realize that it’s the picks and shovels that that, that getting clean data in is the hard problem, you can’t find a data scientist on the planet who doesn’t complain about it. That Oh, you know, I want to do the interesting modeling part. But if only I can get clean data, but we don’t want to complain about it, we see it as the opportunity not The annoying thing you have to get done. Getting clean data is where, where a lot of the value is created. Now, as you and our space, that’s external data, but it’s the same is just as true of internal data that’s inside the enterprise. Now, you mentioned rightly, that, it’s, it’s a it’s a polarizing domain that and really, that’s all about the use case. And you see different companies tackling this in different ways. There are certain uses of data which passed the pub test, and you know, you feel comfortable with it, I’m using data because somebody provides consent for me to use data and it gives them a better experience. And companies like Uber have taught us that, you know, you press one button, you get your product, and that’s good. But then there’s other use cases where it’s not as clear to the consumer and to the man on the street that that they that they want data being used to target advertise to them or do something like that. So it’s all about it’s all about the use case.

Scott Johnson  4:50  

Yeah. And I think the public kind of talks out of both sides of its mouth on this like we love the convenience like you mentioned, you know, the single clicks and we want search results that are tailored to us even We don’t realize that but we want the search results we want. And they simply can’t do that without data. But we don’t like the thought of that. So it’s kind of a catch 22?

Mark Hookey  5:08  

Well, it is it isn’t. It isn’t. I mean, there are some use cases where there’s a very strong alignment between the consumer, the enterprise and actually the regulator, like, I’m applying for a credit card, I’m trying to get a renter’s insurance policy, I’m, I’m in transaction, I want to be in that transaction, I started the process, I’m giving you the enterprise consent. And then the enterprise goes and finds out everything about me with my consent and with my control and, and then make a decision and they don’t store the data and keep it forever just for the sake of storing it and keeping it because I don’t want that. And so I’m happy, they’re happy. And actually the regulator’s a happy as well. The other thing about data use cases is there’s many use cases people don’t talk about as frequently, which are really all about protecting people, but regulators imply apply for responsibility to enterprises, like banks and insurers to make sure they’re not doing business with with bad guys. And and not supporting the development of any kind of fraud ecosystems that are out there.

Scott Johnson  6:13  

Yeah, interesting. And so we talked about insurance. I know that was your previous business and Demyst is involved in not just insurance, but other financial enterprises as well. Right? What do

Mark Hookey  6:24  

you guys do? Yeah, so we actually help insurers, banks, fintechs, retailers, many, many other types of companies with external data, operationalization, what we find is that many of them are quite aware of the power of external data, they’ve spoken to lots of vendors, big and small. There’s lots of bureaus, there’s lots of niche players, but, but the friction associated with operationalizing data inside their four walls is very, very high, they have to go through all sorts of processes to to harness the data, it would be like if, you know, if a person was hungry and wanted to make some food, but supermarkets didn’t exist, they have to go to the farm. And they have to figure out where the farms are in the first place. And that the farms exist, that’s a bit of a bit of an analogy as to how a lot of people tap into external data today, so we’re creating a one stop shop platform for enterprises to to operationalize data to solve their business problems.

Scott Johnson  7:23  

Yeah. And you mentioned clean data a number of times, so I imagine the onus would fall on you guys to ensure the data is clean, but those institutions have some sort of mechanism. Just how does that work? Man, it just sounds like a bear of a test to ensure you know, data is clean, you know, for both parties, because the consequences are massive. If it’s bad data,

Mark Hookey  7:42  

well, in it some look clean matters, compliant matters, the data was has appropriate provenance, and the company that’s providing it has the rights to sell it, and the company that’s providing it is legitimate and ethical and, and the data is fit for purpose, not just clean, but it solves certain business problems, these are all really important things to solve for that, that we we help solve for with our, with our customers and with our platform. And, yes, there’s a lot to it. It’s not it’s not straightforward, it’s slow and painful. But once you get it right, once you understand the supply, then you can reuse it across customers, you can, you know, without you can start to make recommendations that speed up the adoption of compliant useful data. And the net effect is our customers tend to consume 10 times consume, sometimes an order of magnitude more data than they otherwise would consume. So the whole issue in the data marketplace is because of all of this friction of figuring these things out. Most people just don’t bother. And they stick with what they have. Because there’s too much cost and risk of going beyond it. And the whole data marketplace is artificially constrained as a result, that’s what we’re solving for is making the, you know, helping the market get unlocked.

Scott Johnson  9:02  

Yeah, interesting. You know, I’m always fascinated by businesses like Demyst were in your early days, you have to start off with these, you know, complex sales to large institutions. I mean, it’s not easy to get your foot in the door, for example, for me, SEO, I mean, I could start off with a 2000 a month retainer and work our way up to you know, bigger companies but but if you could go back to your your early days, I mean, what what were your your sales like and not dollar fee wise, but what was your approach to landing those initial customers for Demyst?

Mark Hookey  9:30  

Yeah, we were definitely sticklers for punishment. We started with some of the biggest, most complicated most conservative, slowest moving large banks and large insurers and, and that they’re some of the, you know, people don’t often sort of dismiss them, but they are also some of the earliest adopters to to technology and analytics and data, they just, they just have their own way of working. That is what it is and won’t change. So you have to be able to build platforms that work within the context of how those institutions work. And so that’s what we did. And, you know, once you if you start by climbing Mount Everest, then then everything else gets easier. Now, look, had we done it more of the SAS way and started with pre packaged solutions to mid tier customers, we, you know, we also would have that will also would have been a legitimate strategy. And we did try and fail to do that in a couple of different ways. It would have then been harder to scale into the enterprise segment later on, we just found that our earlier attraction demand was coming from the larger, more innovative institutions versus the

Scott Johnson  10:34  

smaller players. Yeah, and I mean, to be honest, you kind of gotta follow the money sometimes when you’re starting off. But you know, with the number of our SAS clients, they started off more complicated and got simpler and simpler and more productized as they went on. Not all of them, though. So for you guys, what are sales like now? Are they still that that similar style, or have you kind of streamlined and simplified are

Mark Hookey  10:55  

not known at all, because we have streamlined and simplified. And one of the one of the other things that happened as we went through our journey is we, we were partially bootstrapped. We raised we raised venture money and, and we had some great backers, but we never raised any one large round. And we never followed that traditional playbook of raise money, build a product, launch it, scale it, it was much messier than that it was more, follow the money, do some custom stuff, do some more custom stuff. And then finding Product Market Fit has been a continuum, not a binary state. And so it’s evolved over time. And, and part of that was in the pricing, we had higher price points, fixed minimum commitments, big chunky complicated projects. And by contrast, today, no for people to look at our website is consumption based pay as you go pricing, it’s much, much faster to get started, we have these things called recipes on our website that are templates of data that we know works, just like a lot of other totally unrelated Sass companies, like, you know, productivity tools that will have templates, we have recipes, there are templates. And, and so smaller companies and bigger companies can come to us now. Sign up, try recipe, pay $20 get started, we don’t need to have. We don’t need to have very, very large drawn out enterprise conversations. And what’s really encouraging. And I don’t think this is just us, I think this is a lot of enterprise b2b tech at the moment is big companies do that, too, that you end up finding these small groups within very large enterprises, we had a $60 billion hedge fund sign up recently that’s doing I don’t know what the the monthly billings are, but they’re very, very small, but potentials, obviously, massive. But this was just you know, somebody who we’ve known for a while, and they’ve known us and sign up on the website, sign up to an account, they get started, they self serve, they self educate, they still want to talk to us. But it’s not the same as that, you know, 20 years ago, like, I’m going to take you to golf, you know, I’ll fly to see you. We have a six month sales cycle, the world has moved on from that. And large companies and small companies expect to be able to put their fingers on keyboards, and get started in a more scalable way.

Scott Johnson  13:15  

Yeah, you know, you may have already answered this. But if you can give advice to a SaaS company owner, that’s maybe earlier on in their journey, first few years, maybe they’re under a million? What would you say?

Mark Hookey  13:27  

Well, I think look, with the benefit of 2020 hindsight, what what we should have done, what, what I think I would do, again, is focusing on very, very specific, very, very specific business application and use case, Amazon sell books before they sold everything, you know, if we had only solved for, you know, reducing fraud in Malaysia, with two datasets and nothing else. And we got five customers doing that. And only that then later on, evolved to being a far more general platform that you can do lots of different things with, that probably would have would have moved faster. So my advice to other founders is don’t don’t sort of get too far ahead of yourself. And don’t, you know, obviously, the VCs need to hear their pitch they need to hear, you know, this is how we’re going to become a galactic platform that solves lots of different things. But the customers don’t care about that the customers just want to hear that you’ve done that thing before and that your product has done that thing before, and that there’s low risk and adoption. So that’s the that’s the advice. I’d give. Don’t be afraid to pigeonhole yourself temporarily into just one problem in order to get get traction in the market.

Scott Johnson  14:49  

Yeah, the riches are in the niches as they say. Yep, that’s right.

Mark Hookey  14:53  

So I was talking to a VC about that the other a while back and we’re talking about 100 million ARR company and he was like, oh, you know that that’s not 100 million ARR company that’s 25 million ARR companies and we love it. You know, it was like they had a factory for manufacturing 5 million ARR companies effectively. And they operated them, like almost independent businesses. And yeah, the riches are in the niches.

Scott Johnson  15:18  

Yeah, awesome. So I mean, clearly, you know, Demyst is doing great, you know, but just to put you on the spot, what’s something that you’re trying to work on improving it diminished or kind of what’s next as far as where you’re trying to evolve or improve

Mark Hookey  15:31  

at where, look where I’m barely scratching the surface, even with our current customers in terms of the the opportunity that can come from tapping into the data that’s already on our platform, and helping enterprises solve problems that they’re already solving, but they’re solving them in a siloed way. So we’re tripling down on on working with our clients to centralize how they manage data, were tripling down on, on scaling that consumption based Adoption Model and making it far easier for not only our clients to self serve, but some of our partners in the ecosystem that want to build proprietary capabilities on top of this, that’s, that’s a big focus for us. And, look, the other big focus area, and the work has never done here is is around data protection, and compliance and privacy. And we pride ourselves on how good we are at that. But you can never be too good at that. Like every, every extra control, we add in every extra piece of technology we add to protect our clients trust us with with sensitive data and so to our suppliers and and, and it’s all within the right guardrails. But, you know, we were always investing in being as safe and secure as we as we can be in as everybody expects us to be.

Scott Johnson  16:52  

Yeah, and those guardrails are constantly shifting, like they’re on quicksand, you know, what secure now won’t be the same next month kind of thing.

Mark Hookey  16:59  

So yeah, and look, we feel like we have a not just a duty and a responsibility, but actually an opportunity. Because what rather than, you know, each enterprise and each data source and each data supplier kind of reinventing the wheel and creating their own processes, we have an opportunity and a responsibility as a central market participant to go above and beyond the standards that that any individual participant would would place on themselves. Yeah, love it.

Scott Johnson  17:31  

Okay. Well, just last question for you. I mean, clearly, you’ve come a long way in this industry. And there’s usually some strong mentors along the way that help you get there are people in the industry you admire? Who would that be for you?

Mark Hookey  17:44  

Interesting question. mentors, people along the way? Well, look, I’d call out some of the people I’ve worked with in, in the Information Services business in places like bureaus in data companies. And one of my prior bosses, Peter Rollei, who was at LexisNexis, for a long time. And our, one of our board members, Michael Heller, who founded a business called August information advisory services, they, they really were pioneers in building emerging information companies, in terms of you know, and are still very close with them. And they’re doing great things. The I also think there’s, there’s a lot of great people, and I’m learning a lot from in the, in the emerging tech space. That is, as I mentioned earlier, down the value chain and some of those underlying unsolved infrastructure problems. There’s a there’s a company called a normal low, the co founder, Jeremy Stanley, is building a great platform for data quality management. So automated processes to detect any anomalies in the in the data and and make it what’s the Circuit City easy button for things like clean data, I have a lot of respect for people that are solving those, those picks and shovels problems, those, you know, less sexy problems that when you’re inside big enterprises that work with big data and big numbers and big impact. That’s the thing that fills their day. It’s not, you know, some advanced algorithm and some Nishi edge case. It’s some how I get the data claim and say, Yeah, and so I call up well, but

Scott Johnson  19:23  

All right, well, we’ve been talking with Mark Hookey of Demyst Mark, where can people find you and Demyst to learn more?

Mark Hookey  19:30 If anybody wants to ping me on LinkedIn, they’re welcome to and if anyone is looking to accelerate and improve the way they operationalize external data, and then solve customer problems, like fraud and pre filling applications and and targeting the right workflows for the right customers conducting credit checks, doing background checks, Next, those sorts of use cases, they’re looking to do that and want to move faster and don’t want to build capabilities in house then they should reach out to us and maybe we can help. Love it.

Scott Johnson  20:09  

Alright Mark, thank you so much.

Mark Hookey  20:11  

Thank you, Scott. Great to connect.

Outro  20:17  

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