If you have massive amounts of data, Devo wants to help. The Madrid-based company specializes in the handling and processing of massive amounts of data.
In this DevOps Chat we spoke with Devo CEO Walter Scott about the use cases for Devo in a data-crazy world. Walter is a seasoned CEO in the tech sector. He knows a problem when he sees it and he thinks Devo is the answer. Have a listen to why and check out Devo at devo.com.
As usual, the streaming audio is immediately below, followed by the transcript of our conversation. Enjoy!
Transcript
Alan Shimel: Hey everyone, this is Alan Shimel, devopsy.kinsta.cloud. You’re listening to another DevOps Chat. Really happy to have a new company, a new friend, joining us on DevOps Chat today. I have Walter Scott, CEO of Devo. Walter, welcome to DevOps Chat.
Walter Scott: Thanks, Alan.
Shimel: So, Walter, you and I met recently in Iceland, of all places, at a Insight IGNITE event that really kinda focused on DevOps, cloud, cybersecurity, if you will, the topics, but it was my first exposure to Devo. I don’t know if our audience is familiar with Devo. Why don’t you–give us the foundation, man. “We are Devo.” What are you guys? What do you do?
Scott: Well, Devo is a data analytics platform that’s the next gen of technology, that provides a data fabric to allow our customers to take all of the noise that exists in the machines and petabytes, and petabytes of data coming in, for them to really start getting signals so that they can start their automation or their AI or their machine learning process, to make their companies more efficient; to be able to have them act more quickly; as well as to be able to get the people who use our platform, to get them the time back that they spend in managing the underlying data platform and allowing them to add the value to drive IT, to drive that value back to the business.
Shimel: Excellent. And, you know, Walter, one of the things that impressed me with Devo was this wasn’t your sorta typical Silicon Valley story—a couple of wiz kids come up with a PowerPoint business plan and get funded via VCs, pre-revenue, pre-product even. Devo—the underlying technology here, the company’s been around a while and it’s not a Silicon Valley company, right?
Scott: That is correct.
Shimel: Give us a little background on that.
Scott: Well, you know, over my six stints of being a CEO, I’ve never really run an American-based company. There’s great IT being built all over the world. I’ve done them from Malta to Scotland, Germany to Armenia to now with us here in Madrid, is where the company started. And we had a not Silicon Valley startup, but it was five guys—or four guys and a girl or woman—and started off the product in a research key, to really build something special and change the market because the bank that they worked for had to completely change the way they do business.
And over the course of three years in research and we finally launched a product in 2014 and have been three-figure type growth every single year, since the date of the company being founded, ’cause it’s such a large market. And it’s really accelerating because of what we’re seeing happening with businesses and trying to get ahead of everything from getting arbitrage out of supply chain to solving their security problems, just to start with basic log management and monitoring to consolidate tools and increase agility.
Shimel: Sure. So, Walter, you were—as you alluded to, this is your sixth or seventh startup where you’ve come in as CEO and had some tremendous success. And, look, not everyone is a success, right? That’s the mantra in business, but you keep plugging. When we look at Devo—and you and I spoke a little bit about this in Iceland, but so much of what we see as, quote unquote, “AI” today is really machine learning. And so much of machine learning is based, really, on data, right? You’re only as good as your data. In order to find patterns, you have to have enough data to analyze it and make actionable kinda calls out of it.
But this is not a new problem and there’ve been other solutions. What makes Devo’s solution—I don’t know if you use the term “next-gen” or “new-gen” or something like that, but what makes Devo’s solution different?
Scott: Well, you know, this has been going around a long, long time and you’ve heard me say, in the absence of data, the machine can’t learn. And I go back, all the way back, to the Walmart days in the ’80s, when they built—I would argue … them as the first person to really take the competitive advantage, coming from their data warehouse, right? And —
Shimel: I agree.
Scott: —They actually had the line, they could tell your daughter’s pregnant before you could by looking at the data. Well, today, that’s become necessary, but, over the last 10 years, it’s really—what’s happened with the cloud is being able to get that data manageable. I don’t like the term “AI.” I like more “augmented intelligence” because we’re not at the place—or even narrow AI ’cause I think it can solve very specific business problems that people might have. We have one customer that needed to actually build some ML and machine learning to detect what a bot was, were hitting their site, because, when they launched a new product, the bots would come in and sell it all in the secondary market. They wanted that brand loyalty to go to the end users.
Or we have another customer that has a bunch of IoT devices integrated into their sporting equipment, where they’re trying to figure out if somebody’s gonna get hurt and using machine learning to be able to define what is that case, in terms of repetitive hits, but they need that data over long scales. And just the data science is actually making that possible. It could be done in the past; the problem was it just wasn’t cost-efficient enough to do it.
We have customers that are taking literally over a trillion events a day to try to—and building machine learning models to understand which customers may leave their environment, based upon dropped calls or poor support integration. And it’s not just having that one set of data; it’s about having hundreds of different sets of data to help prove those hypotheses, to get those more and more efficient. And the data scientists are spending 70% to 80% of their time getting their arms around the data. We take that problem away from them and allow them to have—to solve that other 80% now, they can put in training their models and getting data to make them more and more efficient.
Shimel: Excellent. Great answer. Hey, you know, Walter, you and I, this is neither one of our first rodeos. And, when I look at your job, your mission over there, at Devo, one of the things that I guess it’s a double-edged sword ’cause it presents so many opportunities, but, at the same time, there’s an axiom in business—“Put all your wood behind one arrow,” right? There are so many use cases for what you can do here, right? There’s the cybersecurity case, right? Where, “Look, man, we need this data. We need to be logging everything. We need to find if we’re under attack, where the attack’s coming from, what’s happening?,” right? So there’s that. And one can spin up an entire company around just using this kind of technology in cyber.
You alluded to a few other use cases, right? That had nothing to do with cybersecurity. Better business intelligence. Understanding your customers better. Understanding your product better. There’s only so many targets, though, that you can go after, right? Without a tremendous—you know, you wanna build an IBM kind of force or something or a Dell force, that’s a whole different story. So how do you—Walter, as the CEO, how do you prioritize? What are the best use cases for this technology? What are the right markets for us to pursue?
Scott: Well, what you say no to is just as important as what you say yes to, in a business.
Shimel: Yep.
Scott: And, right now, we are very, very focused in the Americas, as well as in western Europe, right? So we’ve been able to say no to Asia for another year or two. Our value proposition is really about the biggest of the big—the telcos, the banks, the large retailers, people who’re looking at real scale that they’re trying to solve problems with. But the fact is, is where I see the market going over the next three years is more about consolidating the DevOps, the ITOps, the Network Ops, the SecOps. All of those are coming into one. And I’ve heard a couple of people start using the term “fusion center,” which is really helping them drive the business and stop pointing fingers and reducing that mean time to response.
Well, every security product starts with the logs, right? And every IT monitoring product starts with the logs and the data. And the advantage of us being able to load in all of that relational data, which is about 20% of our data that’s in our systems, across our customer base, is things that is coming out of Xdata or data warehouses or relational or customer application type or weather data or threat hunting data that comes in, and being able to combine all of that. So, for us, it’s the larger customers or companies that have data at scales that we’d see in the global 1,000 and really looking at the people who understand that what Google, and Apple, and Facebook, and Netflix have done is taken a data-first approach to their business.
And, when we look at customers that are forward seeing—that see forward into the market, they realize there are a whole new set of competitors and being able to ask what their real competitive advantage is in their business and the data that it takes for them to be able to do that, whether that’s a telco to figure out how to launch a 5G network because they have all the 4G, 3G and 2G data, whether it’s in their customer retention rates or net retention of “Can they get more dollars out of their customers each year?” to whether it be their manufacturing process and figuring out that arbitrage in the supply chain.
So I think we are—because of the ease of use of our product, because it’s so easy to get data in, because we keep data in raw format. So you can have one set of data that’s very easy to get in and use it 100 times, not having to index it or reduce that data because it just makes it more efficient.
But I see us, in the next couple of years, really focused on the security side of the equation. Looking at people who’re doing the transformation and the weight that it’s gonna take on their DevOps and CloudOps teams and being able to work closely with the network and the DevOps teams to be able to grow their business because the cost of becoming so great on having so many different tools, at different layers of your network, I say that the best and the brightest companies will take control of their own data, consolidate those tools so they have a better competitive advantage.
Shimel: Good. Good. Good. Walter, I’m gonna throw a term at you and I want your opinion on it, “AIOps.” What do you—this is a term that we, here at devopsy.kinsta.cloud anyway, we see it rising with a bullet. And, like you—to me, there’s AIOps with a capital “A” and a capital “I” and it’s like Robby the Robot stuff that’s gonna make us live like The Jetsons, finally. And then there’s AI with a small “a” and a small “i,” like the real, functional, kinda down in the dirt–maybe it’s not as sexy, but it provides real change for us. AIOps is—man, everyone—what’s your story on AIOps? It reminds me of when cloud came out in 2005. What do you think?
Scott: So let’s be very clear. AIOps is a term that’s been coined by Gartner. Out of the last 100 conversations I’ve had with CIOs, I’ve only heard it referenced once or twice. I think it’s more the vendors that are getting on it. And, when you look at it, log management is a big part of AIOps. You know, user behavior analytics is part of AIOps. The anomaly detection is part of AIOps, right? There’s seven or eight different components to that. I don’t think any vendor right now, in the world, really has defined themselves as an AIOps vendor. I think we’ve probably got three years, through help of M&A and deciding what that is, ’cause incident response or the service management is also there, so I think it’s gotta evolve.
But, of the seven or eight different components, the monitoring, the dealing with events, the user behavior, the anomaly detection and the log management is there, which is where we focus. And I think the enterprise log management, I thought, five, six years ago, was solved, when we solved who all those vendors are, but now, with the complexity of the cloud, it isn’t solved at the scale customers need to do with it. We had solved router management; now, with SD-WAN, it’s a whole new market.
Shimel: Yeah. You’re right. I mean, and it’s funny that things that–it’s kinda like the measles, right? We thought we cured measles and here we are back with measles. Walter, we’re both CEOs of companies and I don’t know how many people listening out there are CEOs—many of our listeners are kinda getting their fingernails dirty, practitioners as well as managers at large enterprise—what keeps you up at night about Devo? What do you worry most about, Walter?
Scott: Well, I think part is we have to manage tens or hundreds of petabytes of data for our customers, right? And I think we have a very large team internally for security, to make sure that the data that we have is protected and we work very closely with Amazon and Google and Azure to make sure that we address that. And that’s something that you’re never gonna sleep well ’cause you have to be passionate about making sure that you protect your customers.
We were on the phone literally an hour ago with a manufacturer that makes toys for children, and that was the question that’s keeping him up–he says, “I have 10-year-old boys and girls logging into my website. I have to protect that data, making sure in the things that we do with being able to obfuscate data at the row, in the column level, without having to duplicate it and our algorithms that are in there.”
But my job, as a CEO, is to help people build careers. I’ve been very fortunate in my career and have had some very large exits in part of the business, but it’s making sure that the team around us continues to make sure that we handle all of the things, in dealing with our business and our main offices in Madrid. We also have a big office in Cambridge, Massachusetts, because I love the guys and women coming out of MIT, where we do a lot of hiring. But it’s mostly about the team and then making sure that we continue to have what I call a industry-abnormal or an anomaly in terms of net retention. And our goal is to help our customers continue to grow because it benefits us and wanting to make sure that we make it easier for them to be able to get more value because our mission is about making IT so that they can provide data to the business that is actionable.
And my belief is is the CISO or the CIO that has control of that data, if they push it out to the business and say, “Hey, did you know that, on this particular case or when this happens, we’re losing customers or we’re gaining customers?” That data is 80% more likely to be leveraged than it is if the customer asks the business. So—
Shimel: Nice.
Scott:–I think it’s empowering the IT to accelerate value to the business and most of my nights are, well, we see this new customer is bringing in this type of a data, bringing in our data science teams to help think about use cases that we can drive more value from.
Shimel: Excellent. So, Walter, as I mentioned to you before we started recording today, the good news and bad news about this audio format is it goes so darn quickly. And we blew past our time. So what we’ll do, though, is we’ll—secret’s out: you live down in Florida, too. We’re gonna need to get you down here and into our studio and let’s do a whole video on this. We’ll continue our conversation in a video format. But, for now, we’re gonna call a—have to pull the plug on this one. Walter Scott, CEO of Devo, thanks for being our guest today on DevOps Chat.
Scott: Great, Alan. Thanks.
Shimel: All right. Hey, this is Alan Shimel, devopsy.kinsta.cloud, and you’ve just listened to another DevOps Chat.
— Alan Shimel
During the meeting, Liu invited experts from JFrog shared their understanding of DevOps and expectations for the future.
