Viewing Data as a Liquid Asset

Finance innovation relies on looking at data through a different lens.

SVB Financial Group has $40 billion in assets and offices in the U.S., U.K., Israel, and China, and is the holding company of Silicon Valley Bank. Its analytics unit, SVB Analytics, supports SVB and its clients, which include half the venture-backed technology and life science companies in the U.S. It offers 409A compliance services to help private companies to value employee stock options, as well as provides strategic advisory services to help companies and their investors to benchmark key operational and valuation statistics.

Steve Allan joined the company in 2008 to run SVB Analytics. He spoke with MIT Sloan Management Review contributing editor Michael Fitzgerald.

What has changed about analytics since you started at SVB?

Analytics used to be a competitive advantage, and now it’s becoming table stakes. It’s something you just need to have to execute on the business competitively. We’ve gone from experimenting with some analytics tools to deploying one visualization tool across the entire enterprise so every person has access to data reports and the ability to look at the data from the exact viewpoint they would like. If you had told me two years ago I was going to shift that tool out from a small group of people to all 1,400 customer-facing workers, I would have said, “I highly doubt it.”

Take me back to expanding the tool beyond the small group of people to the whole company.

As we started to deploy those insights across the organization, invaluable qualitative interpretation started occurring at the front line; the folks who are operating with the VCs, operating with the angels, operating with the entrepreneurs on a day-to-day basis. We had a lot of “aha moments” — better understanding of anomalies. The frontline team were sometimes able to give context as to why an anomaly occurred, which then allowed for further testing.

For example, we did an analysis on molecular diagnostics, basically genome sequencing for prediction of disease, and found that it cost almost two times what was projected to actually get through a product development cycle — a difference of tens of millions of dollars.