Jan 3, 2024 | 4 min read

Jonas Hellgren

Podcast #222 Data Quality

 
 
 
 

Revolutionizing Data Quality: APERIO's Journey with Jonas Hellgren

 

Welcome to episode 222 of the Momenta Digital Thread podcast series! In this episode, we are honored to host Jonas Hellgren, the CEO of APERIO, a company revolutionizing data quality for industrial applications. Momenta recently led the Series A investment round for APERIO, underscoring the industry's recognition of their progress.

Jonas Hellgren's Expertise: Jonas is a seasoned executive with a deep-seated passion and a proven strategy for building and scaling high-tech companies that redefine their respective industries. Boasting over two decades of experience in operations and product development, his journey spans diverse sectors, including mobile and digital advertising, managed security services, consulting, and enterprise IT.

 

Unique Insights: Throughout the episode, Jonas shares unique insights drawn from his wealth of experience, shedding light on the intricate landscape of data quality solutions. As a leader with a track record of success, Jonas delves into the strategies that have driven the growth and evolution of APERIO, making it a frontrunner in the data quality arena.

 

Stay tuned for a captivating conversation that unravels the complexities of data quality and unveils the future of APERIO in the ever-evolving digital thread.

 

 Discussion Points:

  • Your Digital Thread: What would you consider your digital thread (the one or more thematic threads that define your digital industry journey)?
  • Mastering Entrepreneurship: We often talk about serial entrepreneurs, but few have your track record of exits: Guardant to Verisign in 2004 ($145m), JumpTap to Millennial Media in 2013 ($225m), and Vaultive to CyberArk in 2018. What is your secret for finding and developing winners?
  • Navigating Sectors: The vector of your sector focus is also intriguing - from information security to mobile advertising to cloud data protection. How did this ultimately lead you to APERIO?
  • APERIO's Mission: You describe APERIO as "ensuring Sensor Data Quality for critical infrastructures and large-scale industrial facilities." What does that mean?
  • Addressing Data Quality Challenges: According to McKinsey, poor data quality is a consistent roadblock for the highest-value AI use cases in industrial customers with asset-intensive operations. How do you help address this?
  • Notable Achievements: What have been some of your notable wins?
  • GenAI and Industrial Impact: One can't discuss AI these days without touching on GenAI. To what degree do you see GenAI impacting industrial systems and APERIO?
  • Adoption Readiness: How do you know when an organization is ready to adopt your solution, and what best practices have you seen in realizing that potential value?
  • The Road Ahead: You've just closed the $9m Series A round, led by Momenta and featuring notable industry stalwarts such as Chevron and National Grid Partners. What can we expect to see from APERIO over this next year?
  • Inspiration Source: In closing, where do you find your inspiration? (i.e., book recommendations, articles, podcasts, people, etc.)


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TRANSCRIPT

 

Ken: Good day and welcome to Episode 222 of our Momenta Digital Thread podcast series. Today, I'm delighted to host Jonas Hellgren, CEO of Aperio, the industry leader in data quality solutions. Momenta recently spearheaded the Series A investment round in Aperio. Jonas is a seasoned executive passionate about building and growing high-tech companies that revolutionize their respective industries. Jonas brings a wealth of expertise with over 20 years of experience in operations and products, spanning mobile and digital advertising, managed security services, consulting, and enterprise IT. His deep experience includes developing partnerships and seamlessly integrating companies after successful M&A transactions. Jonas, a warm welcome to our Digital Thread podcast today.

 

 

[00:01:27]

Jonas: Thanks, Ken. It's good to be here. I look forward to this interview and, of course, showing a little about my personal professional story with you, talking more about Aperio.

 

[00:01:38]

Ken: Excellent, looking forward to it. I know it'll be a great conversation, as you and I have had plenty of chances to discuss some good topics here. First and foremost, we call this the Digital Thread podcast. Of course, this refers to one's digital thread. What would you consider to be your digital thread?

 

[00:01:58]

Jonas: I thought a little bit about that, and interestingly enough, it made me think through something to prepare for this podcast that maybe I haven't delved into deeply before. A few things have accompanied me throughout my career, shaping how I think about things and make career decisions, so I'll share a little about that and how I see it. I guess the bottom line for me is that my digital thread is that I'm attracted to building new things and learning challenging things, regardless of what those things are. This has shaped how I've made my career decisions. I started my career as a software engineer after graduating in 1990 with a master's degree in computer science. As a kid, I was drawn to computers and the amazing things you could do with them. Of course, back in the early 1980s when I was a kid, nobody had a computer. But in 1984, I came across a unique feature in the wireless world about how to build a microcomputer. It was based on the Zilog Z80 chip, which I don't think is around anymore. It had 64k of RAM, an operating system, and a floppy drive. I was 16 then and had no clue what I was doing, but I was determined to figure it out; it was new. I decided I would try to learn how to build this computer. This whole project consumed me for about a year. I got the blueprints for the circuit boards, patched the drill to the circuit boards, and went to build this computer. I had to run around delivering newspapers to earn money to buy hardware. I taught myself assembly language coding while making this machine. Eventually, I got to the point where I turned this thing on that I spent a year building, and it turned out it didn't work.

 

Nothing happened, so I went to borrow some equipment. I had to learn how to use an oscilloscope; I taught myself how to use that and troubleshoot this computer of mine. A few weeks later, I successfully made this computer run, which was defining for me. I used this computer for the next couple of years for almost everything. I taught myself to program in C, Modular to Fortran, Pascal, and other procedural languages. I remember the last time I used this computer to write my job application for my first job out of college, a software developer position at Andersen Consulting, which eventually became Accenture. That became my first job in 1990. That was the last time I used this computer; I still have it in my office here. I often think about how building that computer at that age, when nobody else did that, had such a profound impact on the rest of my career.

 

That was the beginning of the two thematic threads that have guided my career in media with Samsung, and that is what I said up front: I love to build new things and learn new, challenging things. If it's not about building new things and if it's not hard, then it's not that interesting to me. I didn't think about that then, but when I look back on my career, it's pretty clear how I've made my decisions. Whenever I look at a new professional challenge or jump into a new project, I always have the same two ingredients in mind: build something new and learn something hard I didn't know how to do before. That's been it for me, and it was never, and still never is, about title, money, career, or anything like that. It's just about learning and building, and I think for me, the rest follows. That ultimately led me toward the career I chose working in high-tech startups. That's what startups do: build things and learn things. The specific industry that I worked in wasn't that important to me; that's why I've been involved in all sorts of different things, from web development in the late '90s to cybersecurity to mobile advertising technology, and most recently, building Aperio to chart a new path in data quality for industrial data. That's my digital thread, Ken, and the key themes that have been there for me throughout my career.

 

[00:06:20]

Ken: What a great story. I think that pattern represents the things that you've done since then and the success you've had. It's very interesting. I was quickly typing up. I thought it might have been the Altair, but then I realized that it was an 8080, your processor- you even did something previous to that if it was a Z80 processor.

 

[00:06:42]

Jonas: I did. Yeah, a Z80.

 

[00:06:44]

Ken: Whatever you do, hang on to it because those early PCs are worth quite a bit.

 

[00:06:50]

Jonas: It's right in front of me; I don't think it works anymore. Sometimes, I'm trying to see if I can turn it on, but-

 

[00:06:55]

Ken: There you go. Well, perhaps that's your retirement sitting right in front of you. It's interesting. We often hear about serial entrepreneurs, but few boast the track record of exits that you do. From Guardant to VeriSign in 2004, a $145 million deal, to JumpTap to Millennial Media in 2013, a $225 million deal, and more recently, Valuative to CyberArk in 2018. What's your secret for finding and developing such winners?

 

 

[00:07:22]

Jonas: Well, I'm sure there are many people with much greater exits than I had, and indeed, there are many. I don't know if there's a secret recipe for that. I'm sure there's a lot of luck and timing involved in all of that, too, but the way I think about it is—things aren't necessarily winners when you find them. They could become winners, though. You look for projects that have the ingredients for becoming winners and then, provided that you can execute and make them winners.

 

For me, that's been about two or three different things. Firstly, I look for things that are easy to understand and have a clear use case. If I can explain it to my children, it's easy to understand. Secondly, unique solutions. I mean, differentiate it from other things out there. It's not just a 'me too' in a sea of competitors that do the same thing, and you're just trying to do a little bit better. Thirdly, it targets a potentially big industry opportunity. Often, when you start, there isn't a market out there at all yet, but you're expecting there to be one. I've tried to think about how big that opportunity can become.

 

If I apply that to JumpTap, for example—when I joined JumpTap, it was a mobile advertising company in 2006. iPhones didn't exist then; there were flip phones. We were 15 people, and eventually, we grew to over 150 when we exited. We were the first company to put ads on mobile phones. It is easy to understand what that is; we had a unique solution that allowed us to build an automated ad platform that provided very accurate targeting to the owners or users of those phones so we could publish relevant ads. Of course, everybody knows them in Denmark; it's now huge, so there was a big opportunity there.

 

The bottom line was an easy-to-understand unique solution addressing a big market opportunity. If you have that, then at least you have the ingredients for building something that can become a winner as long as you can execute.

We applied that to Aperio. What do we do? We validate and clean industrial data, which is easy to understand. What we do here at Aperio, fully nobody else. There's no other solution in the industrial and manufacturing space that can do what we do at scale, and obviously, the market opportunity is big. Data quality is, without a doubt, the number one issue holding back industrial companies from realizing the full potential of analytics and AI. We have that in place. It's mainly a matter of execution to create the winner, and I think that challenge is mainly a matter of building the right team and the right team culture.

 

Once we have the ingredients, then it's down to execution, and execution is about finding the right people and building the right team. That's how I spend most of my time. Of all the projects I've been involved in startups throughout my career, I believe Aperio probably has the biggest potential because the data quality problem we solve is such a massive and pervasive problem in the industry. We have a clear category leadership with a unique solution. Yeah, it's got all the right ingredients, and we're also getting close to having the right team. Hopefully, this will be another winner.

 

[00:10:46]

Ken: Well, we are counting on it being so. You warmed my heart when you said this would be your biggest win because that comes off of a pretty high hurdle based on your earlier wins. Let's jump on into Aperio. You describe Aperio as ensuring sensor data quality for critical infrastructures and large-scale industrial facilities. What does that mean?

 

[00:11:08]

Jonas: Several places can fail when you start from the field sensors and follow the path through the analytics engines. What Aperio does, and what that means, is that we help identify these issues in the data flow throughout all the systems the data passes through. It's about the data itself by identifying gaps or anomalies in the data stream. It's not only about having data; it's about trust. Aperio ensures that you have data that you can trust. That is, in essence, what we do and what Aperio means.

 

[00:11:50]

Ken: Now, no small achievement in there or no small challenge, I should say, that you guys work towards. According to McKinsey, poor data quality is a consistent roadblock for the highest-value AI use cases in industrial customers with asset-intensive operations, i.e., where Momenta likes to invest. How do you help address this?

 

[00:12:09]

Jonas: McKinsey published an article in early 2023 that was very beneficial for us; it validated what we do. They concluded that poor data quality is a consistent roadblock to realizing the value of industrial AI. It's a great validation for Aperio, but there's much more in the industry. For example, it's estimated that 80% of industrial data goes unused due to poor data quality. Most of our customers tell us they spend a significant portion of their data analytics budget on validation and cleansing, rather than analyzing data and making business decisions based on it. There's undoubtedly a lot of industry attention to the issue, with significant funds allocated to address it. So, how can Aperio help? We provide unique assistance by automatically and at scale solving data quality problems, and no other solutions can do that. Data volumes are growing exponentially yearly, creating more data than in previous years. This data serves as the basis for making critical business decisions. The volumes are now so large that manual efforts or hiring consultants, as suggested by McKinsey, are no longer feasible. This approach only works when dealing with thousands of data streams. Still, most industrial companies now struggle to manage millions of data streams, and the negative consequences of making decisions based on poor data are much more significant. That's where Aperio comes in to help and addresses this problem by automatically solving data quality issues at scale—this is our differentiator and how we stand out.

 

[00:14:13]

Ken: To put a point on this, can you tell us about some of your notable wins with clients?

 

[00:14:19]

Jonas: Both in terms of who we've been able to work with and the kinds of issues we've addressed, right? We were fortunate enough to work with some real industry leaders early on in our journey. I'll say we punched above our weight a little bit in the early days before we were funded and before we had a complete product because we started off targeting these Global 500, Fortune 500-type customers, which led us to the opportunity to work with some of the largest industrial organizations in the world, such as (inaudible - 00:14:56), Chevron, Air Liquide (the French gas company), National Grid, and others that we can't mention. We're now working with some of these real industry leaders who are willing to partner with a small startup and go through the patience of working with a company that wasn't quite there yet in terms of maturity. Now we are, but back then, we weren't, and that speaks volumes to the size and importance of the problem we're trying to help solve. We work with interesting industrial companies like that. When it comes to the types of issues, we help companies address, that ranges from the tactical stuff. For instance, we worked with a customer and started loading all their data streams into our system. As soon as we did that, they discovered major configuration mistakes that they had made in their databases, allowing them to act and solve significant data analytics problems. We have a fairly short time to value where we deploy and focus on a specific use case, helping somebody make better data analytics decisions or find faulty sensors or broken systems quickly. These are the everyday wins we encounter, but by and large, the fact that we've been able to earn the confidence of some of these large industrial companies is noteworthy.

 

[00:16:24]

Ken: If this conversation had been a year ago, all those wins would have been valuable to their industries. But I think everything related to AI- industrial AI, in particular, has taken a huge step up because of the interest in generative AI- gen AI as it's called or ChatGPT. To what degree do you see Gen AI impacting industrial systems, and probably more importantly, Aperio?

 

[00:16:51]

Jonas: Well, I think my answer is not much different from most others. Gen AI is going to be disruptive to everything. It will disrupt the space we're in and everything that we touch across the board. It's a matter of how and when. The way I look at it is there will be many new winners that are created due to generative AI—those that pay attention to Gen AI. There will also be a lot of losers—those that don't. We have to pay attention to that. That said, there's no reason to jump headfirst into gen AI and latch onto that without a plan. So, we continue executing our path. However, we've been proactive with our system and the architecture we're building to ensure its flexible, ready to integrate a generative AI layer when needed. The interesting thing about Aperio is that generative AI craves quality data like AI. You can see how what Aperio does becomes even more important because generative AI, by and large, can drive the consumption of even larger amounts of data and make bigger decisions based on this data. If we use generative AI directly here at Aperio, I think it can drastically improve our opportunity and product. Yeah, we see generative AI as a driver for more demand for quality data but also as a potential tool that we can use to improve our product. We're certainly thinking about that and setting up the architecture to be ready for that. Still, we're stopping short of jumping straight into it without a solid plan. That's how we think about it.

 

[00:18:37]

Ken: Well said. Our investment thesis for Aperio is interesting. I think one of our partners put it best when he said that in a gold rush, sell axes, picks, and jeans. In some sense, data quality becomes that infrastructure that enables, if you will, the rest of the gold rush, and gen AI is simply the hype that's driving interest from all angles of the industry but also exposing the big gaps in data quality or data accessibility or to your point earlier, data usage, the 80%. We see many upside opportunities, which was one of the strong points for our investment in Aperio. Let me ask: how do you know when an organization is ready to adopt Aperio, and what have you seen in certain terms of some of the best practices clients might use to realize the value of your systems?

 

[00:19:30]

Jonas: First of all, regarding interest in adoption, we are at a point right now where we see a lot of excitement in the industry. We feel like the product-market fit has been fairly well-validated. There's almost a universal interest in data quality as a topic, as a problem that needs solutions. That's good; that's there. Then it comes to adoption in a particular organization, and here, what we look for is the actual end users. We find that when the data engineers at, for example, an upstream oil company that we're working with, get frustrated with the time it takes for them to manually mine data and ensure that the data is fit for purpose for the AI or the machine learning models they're trying to build, we then get the end users or the potential end users in front of our products so they can see how efficient they become using our product, Aperio's solution. Once we get these end users excited about Aperio, we know we're in the best position to get adoption for our product in that organization. So, that is our best practice. As a best practice, we focus on getting the end users committed first; we get them excited, build on that, and make them the champions of Aperio inside their organization. Then, we leverage them to get adoption. When that happens, when we have these data, champions building those across the organization— I mean, we sell into fairly large companies. Running up and down, raving about Aperio and carrying the Aperio champion torch, we know there is a readiness to achieve adoption. That's essentially how we also structure our sales process.

 

[00:21:25]

Ken: Well, excellent, given that you end the note on sales. This is the Disneyland question. You've just closed the $9 million Series A round led by Momenta, featuring some notable industry stalwarts, such as Chevron and National Grid partners. What can we expect to see from Aperio over this next year?

 

[00:21:45]

Jonas: Execution. I mean, it's very simple. But first of all, it has to be said that we're proud and excited about the funding that we just closed in August of this year. It was a bit of a challenging year to fundraise across the board, and we ended up with an oversubscribed round with tier-one investors like Momenta. To have confidence and support from Momenta, we look forward to working together to reach the next milestone. It's great for us. Also, to be backed by such a strong syndicate of strategic investors, National Grid, Chevron, EDP, Delek, and others. Most startups at our stage are not fortunate enough to have the backing of investors of this caliber, especially not in a year like this.

 

There is no doubt that this is going to materially impact our future success. First of all, thank you for the confidence you put in us. But also, it's great to be in this position and have backing from this type of syndicate. That said, next year for us, it's going to be all about continuing to focus on team building, and I think I said before, building the right team and getting the right team culture—it's a never-ending project that has to be worked on all the time as the market evolves and as the company evolves. Next year, we will strongly focus on customer needs at the center. I've always said that the customer comes first, and we're more successful if we always consider putting the customer first and center. We have a unique solution already, and we have a big market opportunity. We're convinced we have the best investors, so next year will be pretty simple. We'll try to build a great product, keep signing up customers, and provide the best customer support possible. I think it's as simple as that. It's not simple but conceptually straightforward and keeps us focused. Several companies ago, by the way, I worked with somebody—Maria Cirino was an investor in one of my companies, and way back when she was also the boss and the CEO of the company I worked for, Guardant. She always was a bit of a mentor to me. She said many things; some things have stuck more than others. She said to me many years ago, "Jonas, don't think too much about strategy and this and that. Just focus on getting another purchase order to maybe get another purchase order, and then another one, and the rest is going to work itself out." That's how I think about next year: signing up customers and getting more purchase orders—that will drive the company to step up and deliver for these customers, and success will follow that. That's what you'll see from Aperio next year.

 

[00:24:34]

Ken: Formulaic, and that only works when you've got the track record to endear the trust of people around you that your formula is the right one, and clearly, you've got the track record. It's interesting. As you said in this funding, it's a very challenging funding year. We have constantly found companies we've been either leading the investment or participating in, where we have had to almost beat off a strategic investor sometimes because the rounds fully subscribed at that point. In this case, you probably had the most stellar investors who wanted to come in at the last minute. As they say, when the herd started to move.

 

[00:25:16]

Jonas: Yeah, and then they're all—yes. It was good.

 

 

[00:25:18]

Ken: Yeah, yeah. It was great to see.

 

[00:25:20]

Jonas: I mean, it's exciting. It's inspiring, for sure.

 

[00:25:24]

Ken: In closing, I always like to ask: where do you find your inspiration?

 

[00:25:30]

Jonas: Many people find inspiration in various sources, such as books, podcasts, and admired figures. In my case, I draw inspiration from a more personal aspect of my life—food. Our discussion touched on this when we last met. My passion for food and cooking emerged from a significant challenge: one of my children faced severe, life-threatening food allergies. Determined to provide him with the joy of diverse and delicious meals, I immersed myself in the world of cooking. It became a full-circle experience, reflecting my innate attraction to building new things and tackling challenging endeavors.

 

When it comes to reading, my preference leans towards cookbooks. This interest has become a family affair, with weekends dedicated to culinary exploration and food projects. My entire family, including my kids and wife, share a love for food, making these moments a cherished part of our lives. In the demanding world of startups, where work is intensive and stress levels can be high, cooking is my sanctuary. It offers a welcome escape and a chance to relax, bringing balance to my life.

 

 

[00:27:09]

Ken: You've done very well on our bookend questions. I always like open-ended questions because you learn so much more about a person based on their answers. All of you fans of the podcast know that our opening and closing questions are truly open-ended because I love the surprise of never knowing where somebody will come back with an answer. You hit it on both ends there, Jonas. Nice job.

 

[00:27:30]

Jonas: Glad it helped.

 

[00:27:32]

Ken: Thank you for sharing this time and these wonderful insights with us today, Jonas.

 

[00:27:37]

Jonas: Thanks for having me, Ken. It was a pleasure. I look forward to working with you more.       

 

[00:27:43]

Ken: As well. I love the capital 'E' on execution, so we look forward to being part of that. This has been Jonas Hellgren, CEO of Aperio, the industry leader in data quality solutions. Thank you for listening, and please join us for the next episode of our Digital Thread podcast series. We wish you a momentous day. You've been listening to the Momenta Digital Thread podcast series. We hope you've enjoyed the discussion, and as always, we welcome your comments and suggestions. Please check our website at momenta.one for archived versions of podcasts, as well as resources to help with your digital industry journey. Thank you for listening. 

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What inspires Jonas?

Jonas Hellgren finds inspiration in an unconventional source—food. Unlike traditional outlets like books or podcasts, his passion for cooking emerged from a significant challenge: his child's severe food allergies. This culinary journey, driven by a desire to create diverse and delicious meals, reflects Jonas's innate attraction to building new things and tackling challenges.

Instead of traditional reading materials, Jonas prefers cookbooks, turning this interest into a shared family affair during weekend culinary explorations. In the demanding startup world, cooking serves as Jonas's sanctuary, providing a relaxing escape and bringing balance to his life.

About APERIO:

APERIO is the leader in industrial data integrity solutions. We help customers drive profitability and sustainability goals while mitigating risk in their industrial operations. Powered by AI machine learning, APERIO automatically validates operational data at scale to improve data accuracy, security, and value, allowing for smarter business decisions based on real-time, trusted, superior data. For more information about APERIO, visit http://www.aperio.ai.