Mr. Elprin co-founded Domino Data Lab with Chris Yang and Matthew Granade in 2013, after nearly a decade building tools and technology for quantitative investors at Bridgewater Associates. The young company has offices in Chicago, San Francisco, Bengaluru, New York and London, and its clients include Tesla, Dell, Allstate and Gap. After a recent $40 million round of Series D funding led by venture capital titan Sequoia and the tech-focused hedge fund Coatue Management, the privately held Domino has been valued at more than $250 million.
At Domino’s San Francisco office, Brunswick’s Shahed Larson spoke with Mr. Elprin about the threat that predictive models pose to companies that fail to embed them, and he explained why managing these models is an “organizational capability,” not simply a technical skill.
Another topic that arose: how often Domino Data Lab is confused with the American pizza chain Domino’s. “It doesn’t happen as often as you’d expect,” Mr. Elprin said. “Maybe once every few months. Though we did have one support ticket asking how long it would take us to prepare a large order.”
Just how difficult is it to convey the importance of predictive models to leaders who don’t fully understand the technology?
With executives, I focus on the existential threat they’re facing if they don’t find a way to become model-driven; by “model-driven” I mean getting predictive models running the core parts of their business, automating more of the decisions being made.
You look at Netflix, which has this amazing recommendation model that its executives have said is worth more than $1 billion a year. Or Amazon, where Jeff Bezos wrote in his 2016 shareholder letter that machine learning has been powering thousands of processes and decisions they make across their business, everything from fraud to product recommendations to inventory management.
What about leaders who say, “That’s great for them, but we’re not a tech company?”
Most of our customers aren’t actually tech companies. Allstate is a customer, for instance. The whole insurance industry is based around predicting the probability that someone you’re insuring is going to file claims; if you can use models to better do that, you’re going to become more profitable.
Some insurance firms are also using models to improve customer experience. If you get into a car accident, for example, you can take a picture of the damage on your phone, submit it and have a model predict the damage instead of waiting for a claims adjustor to come out into the field. That obviously saves money for the insurance company and saves time for the customer.
Monsanto is another customer of ours and they’re using models to better predict which new seed types and crop strains will be most effective. They’re also building field-plotting models for farmers, which recommend the best places to plant crops, looking at factors like soil composition, field topology and weather patterns.
To give you just one other example: A lead scientist at Bristol-Myers Squibb, another client, talked recently at our annual event, Rev, about applying data science for immunotherapy research they’re doing for cancer treatment, and how they discover new correlates between immune cells and cancer genetics to drive immunotherapy breakthroughs.
So it’s happening everywhere. It’s not just tech companies. We have entered the next era of computing. If you aren’t figuring out how to put models at the heart of your business, you’re going to get surpassed by competitors who are.
And if you want to turn yourself into a model-driven business, you need to think about building that as an organizational capability.
An organizational capability?
Absolutely. Changes across people, process, and technology. A big misconception is you can hire data scientists, give them access to data and then all of a sudden you’ll build great models to run your business.
During the e-commerce wave, it wasn’t enough for a company to hire people with the title of webmaster and think, “Now we’re an e-commerce business.” You had to fundamentally rewire and reorganize how the business worked. Webmasters were a necessary ingredient, but by themselves they were nowhere near enough.
That’s the same kind of thinking needed for predictive models: Finding ways to get different parts of the business working together and aligned on how to put predictive models at the heart of the work that is being done.