Humble Beginnings to Global Success: The DataArt Story with Alexei Miller

Hi, Deborah. Thanks for having me.

Either that or I couldn't figure out anything better to do in all this time.

Well, I was part of the group that started the company, so I guess if we want to start with the beginning. The beginning for me starts before Data Art was founded. And it's sort of how I came to be part of this group all these years ago informs how Data Art evolved. So if interesting, I could give you a few comments about where I come from and why I ended up here. To keep it short, I grew up and lived until I was 16 in the very north of Russia in a very inhospitable place beyond the polar circle. Long nights, long winters, not a nice place. It was very industrial. Education, engineering education was actually pretty good, I thought, because the Soviet Union needed engineers. My parents were in the engineering field. I had an affinity for math and physics. Growing up, I thought I was really good at math in my little town, but at age 16 I was invited to a specialized math and physics boarding school in St. Petersburg. I thought I was the best, but I showed up and was one of the worst. It was a good lesson in humility. I graduated, got into St. Petersburg University math department. Four years into a five-year degree, I realized there's no money in math and I'm not a genius, so I switched to computer science and started looking for a job. I wasn't in love with programming; I preferred books, girls, and beer. I went into computer science to make money. I found a contact trying to pull together a team for an internet project in 1997. I got hired as a programmer, but I was terrible. They found out in two weeks. But I was useful for ideas and speaking with clients. That was the beginning of Data Art, a group of about five or six people. I was the only one who liked speaking with humans. I lived in Russia until 2003, then moved to New York. That was the second beginning of Data Art, when we realized we were a real business providing technology development services. So a little less than the total duration of the company.

Interesting? You asked that because in the very late nineties and early 2000s, it was easy to feel like a real business because money was flooding the internet. Even nobodies like us got some. Then it all crashed overnight. No clients, no money, no real business. We had a team of 40, money for two months payroll, and a choice: split the money and go our ways, or try to make something of it. That was the moment we decided we could be a business. It wasn't a strategy; it was an animal-level decision to stay with people we liked and trusted.

It's a fair point. But you only suffer if you know there's better. When you live in it all your life, it's normal. Now in New York, a few snowflakes and I'm like a regular New Yorker: 'terrible winter.' Back then that was summertime. It's only in comparison.

It was crushing, but not for long because it was so interesting. I enjoyed studying with very smart people. 99% of time studying, 1% comparing. So I was a loser 1% of the time, but 99% I was just one of them. The system was organized around excitement for learning. I think that Eastern Central European education system is valuable for tech today.

I think so. I don't know why it works. The original founder, Eugene Goland, had the ability to place bets on people and trust them. He trusted me for reasons I don't understand. All credit to him.

Can you hear me okay? Debra? I apologize, I lost you for one second. If you could repeat the last portion of the question.

Yes. What's fueling beyond employee longevity and loyalty, but their willingness to refer others? I think it goes to the way the company was organized. We were just doing what was natural. Looking back, there are two ways: system-based and culture-based. We built a culture-based organization. Culture was there first. People who joined early felt it and passed it on. It's harder now with 700 hires per year. You have to live it for a couple years to feel it. Our loyalty metrics remain very high but naturally decline a bit. We're proud they're high for our industry. We're in the people business; our assets leave every night and can exit any day. So there's not much choice.

I compare organizations to athletes. You can take a substance for a temporary boost, but it's not a long-term fix. To run a marathon, you have to practice running. Reorgs are often adrenaline boosts. They can be useful for short-term motivation, but long-term you need to practice culture, customer service, and making every person live your mission. Easier said than done.

I am a runner. I run quite a few marathons. I wish there was a pill to make training easier, but sadly not.

Data Art is a professional services company helping clients build custom technology. We believe using tech in a smart, unique way is key to business performance. We don't sell existing tech; we build custom solutions. We do everything from redoing old systems to building mobile apps. We're almost 6,000 people. We take almost any business, small or large. We have industry-specific strategies for five sectors. We're not a household name, but CIOs in those sectors know us.

Two sides: clients' business and our business. On clients' side, every client is talking about AI but few have firm plans. It's a lot of experimentation. It will come in waves, automating routine operations. The shift will be fundamental but slower than the hype. On our side, generative AI can write code. Our internal measurements show a 30% productivity boost for some tasks. That means projects can be 30% cheaper. For us, that could mean less revenue per project, but lower costs open more possibilities for clients. So we think it's market-expanding. You'll need fewer engineers for large projects but will run more projects in parallel.

Sure. The other side of AI is how it impacts our business. Generative AI can write code. We've measured a 30% time savings for some tasks. Projects become 30% cheaper. That's great for clients but not for me if I just sell hours. But lower costs mean clients can do more. So those massive projects with armies of engineers will shrink, but those engineers will work on multiple projects instead of one.

Exactly. Otherwise they walk out. It's very practical, not charitable.

It goes counter to short-term efficiency. The easy path is to find the lowest cost resource. But that's destructive long-term. We have mechanisms to let people change jobs inside Data Art. It's expensive but helps long-term sustainability.

All of it is true. But clients are complex organizations with different interests. Everyone wants the best team but not always willing to pay for it or accept changes. Finding balance never stops. Many clients and we ourselves search for a fixed formula, but methods go stale quickly. Best results come from accepting constant change and engaging in it. AI will force even faster change. A CIO recently told me to forget our plans from four months ago; he needed to move 10 times faster. That's the new world.

Thank you so much, Deborah. I don't think we're doing anything extraordinary, but it's important to do what we enjoy and help others do the same. Thank you for having me; it's been a blast.

Thanks a lot.