842: Realizing the Potential of Data at Scale | Ross Muken, CFO, Sophia Genetics

“It was through this process that I saw what needs to happen when you integrate two businesses and need to drive cost synergies and margin expansion,” recalls Muken, who at that point was helping to spearhead the firm’s healthcare and life sciences realm, an area of research that was enjoying some added luster due to a recent boom in biotech.  

Along the way, Muken says, it became apparent that the 20-plus-percent operating margins that management was targeting for the newly merged entity would be a bigger challenge than expected.

“It couldn’t just be the cost side of the equation—what was going to get us there was new revenue streams,” remarks Muken, who reports that the firm began evaluating possibilities in a number of untapped “adjacent markets” before formulating a strategic investment inside the equity capital markets business.  

“We had committed to the Street that we’d meet these margin targets, so putting in additional costs didn’t feel great, but our view was to be tactical and take some cost out but then reinvest those dollars to achieve higher margins,” comments Muken, who doesn’t hesitate to share the outcome.    

“This paid back tenfold, and we were able to build a very large revenue base with better margins in this new business, which allowed us to get to our margin targets without shrinking headcount,” says Muken, who today credits the tactical move with more than margin expansion.

He explains: “We had to take a strategy that made sense on paper and then have it make sense to shareholders from a numbers standpoint—and it was because of this experience that I decided to move to the operations side of things.” –Jack Sweeney

Historically, for a disease like cancer, much of what has been used to treat it has not been specifically fit for the purpose. It’s hypothesis-driven medicine. Everyone with the same cancer is treated the same. The reality is that this is just not how things play out. Our view is, How do we use data at scale to really inform so that the patients of tomorrow can actually benefit from data that we are learning from the patients of today? We’re growing at a very rapid pace and hopefully doing some social good along the way.

Someone diagnosed with cancer usually has started in the radiology department, so there’s the image of the tumor. Eventually, a pathologist looks at a slice of the tumor under a microscope. Normally today, there’s next-gen sequencing, so they’re sequencing the tumor and learning about it and your genetic makeup. There’s all of your family history and what we would call phenotypic data. In the world today, there are tons of unique characteristics about an individual and lots of data points, but they’re not utilized optimally, unfortunately, or at least certainly not all together and correctly. Having a common platform that can ingest all of this raw data and then design help based on all of the previous patients with similar characteristics can help us to start to inform on decisions about what to do and what the response will likely be.

The worst thing that medicine can do is to say, “All right, we’re going to give this cancer patient a drug,” and then have them become a non-responder. Now we’ve given someone this incredibly toxic cocktail and then they don’t actually live or they don’t actually see an extension of life. Sophia is trying to help by matching the right patient to the right therapy not only to begin with but also if things change in the treatment path and unfortunately the tumor grows faster than what was expected. What do you do? what does the data history say that you should do as the clinician?

The last piece of it is comes from a diversity perspective. For example, if you’re in the U.S. and are of Turkish descent, it’s of great benefit to you that we do business in Turkey. This means that with regard to genetics, we’re able to gather and analyze data from a huge population right there that’s more genetically similar to you.

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