Performance, not understanding

Updated: Oct 8, 2020


This is the first of a series of interviews I will be conducting with members of the Machine Commons supplier Collective. Subscribe to the site to be alerted about future posts.


Markus Schmitt is the CEO of Data Revenue, a machine learning consultancy based out of Berlin.


How’s business in the pandemic era? Have you felt much impact?

“The impact hasn’t been huge. New client interest is a bit down, but we’ve been extending projects with our existing client base.”



When it comes to generating new business, has anything been working for you?

“People are definitely distracted right now. There’s just so many other things to take care of, and they’re all scrambling to get everything done! So we’re planning to get in touch later, when it calms down a bit. Still, it’s vital to make the connections now, so we’re ready to pick up where we left off when things do free up.”



Let's take a step back. What exactly does Data Revenue do?

“We build machine learning solutions for other companies, which makes us an ML agency. We primarily focus on large-scale ML enterprise solutions. The challenge is making scalable end-to-end solutions – something that integrates with existing solutions and is really in use day to day.”



What’s the most challenging aspect of what you do?

“Deciding how to spend our time and what to focus on – on all fronts, including the marketing, the industry, and the product itself.”



Which strategies help you figure out what’s important?

“We ask ourselves which of our projects were the most fun. The answers help guide our decisions.”



Could you give us an example?

“For one client, we built a hugely successful ML solution to improve their marketing revenue. But it didn’t sit well with the internal engineers that we were doing the exciting projects. So they gradually built their own solution and ended up competing with us.”



What would you do differently next time?

“I think we could have avoided the problem by working more closely with them, removing the need to compete and making sure we weren’t duplicating work. It depends on the team. You have to get a feeling for the politics. It was very sad for our team because we wanted to celebrate a win – which it actually was. It’s a shame when things don’t end well. But we’ve never had a situation like that since then.“



How did you get into this line of work?

“I came to programming by accident. The first script took me a long time, but then I was addicted. Before long I was coding 16 hours a day. Then I took on my first ML project for a client. After five very intense months of learning on the job, I managed to build a first solution for them. Data Revenue grew from there.”



What do you find interesting about machine learning?

“At the end of the day, when you’ve finally got everything lined up, there’s this clear potential latent in the data. You’ve trained a machine to figure out patterns that no human would have been able to identify, or to do work that would have been utterly impractical for a human to do. So you can do things you couldn’t do otherwise. It’s like magic!

I love seeing that potential in the data and figuring out how to access it.”



What’s your best moment?

“In many cases, it’s seeing how much effort people put into a manual process. Often they’re doing really repetitive work – things they wish they didn’t have to do. Then you bring in your solution and say, ‘You don’t have to work 60 hours a week on this anymore!’”



-Any examples?

“One of our biotech clients had really smart researchers spending days and days doing very repetitive work. It wasn’t exciting at all, and they had better things to do. But this grunt work was a necessary part of the process. Our solution saved them 98% of that effort – something that used to take two weeks now takes five minutes.