Summary:

Orthopedics and agriculture — the hot new fields in big data may be less glamorous than two years ago, but that’s where venture capitalists are looking.

Shivon Zilis, VC, Bloomberg Beta; Sven Strohband, Partner and CTO, Khosla Ventures; Hilary Mason, Data Scientist in Residence, Accel Partners; Jalak Jobanputra, Managing Partner, FuturePerfect Ventures. Structure Data 2014

Was it only 2012 when venture capitalists would rain money on anyone who said “big data”? Those days seem to be over: Venture capitalists say they don’t want to hear that phrase anymore, but are instead looking for startups that are deeply immersed in niche verticals.

“We’re focused on vertical opportunities and unsexier industries,” said Shivon Zilis, a VC at Bloomberg Beta who pointed to decidedly unglamorous fields like orthopedics and trash as attractive targets for big data investment.

Speaking at Gigaom’s Structure Data event in New York Wednesday, Zilis and other investors warned startups not to try and dazzle them with buzzwords and Airbnb analogies.

Instead, they said the most attractive startups consist of founder duos where one person is deeply immersed in a certain industry and the other is an expert technologist. This combination will be able to identify and execute on new data opportunities.

“I don’t like me-too companies,” said Jalak Jopanputra of FuturePerfect Ventures, who says she is interested in companies creating application layer financial services involving the unbanked.

Another speaker, Sven Strohband of Khosla Ventures, emphasized the emergence of data products in unexpected industries. He cited robots crawling through farms to classify lettuce leaves and experts on inner ear infections. In building companies, he says that he spends considerable time recruiting data engineers as part of a process of filling a pipeline of experts.

One surprise from the panel is that many self-identified data professionals can’t actually do anything useful with the information they hold. According to Hilary Mason, a data scientist at Accel Partners, some companies are forgetting to employ the scientific method.

“Companies say they’re data driven, but they use it poorly…They lack an experimental process,” said Mason, adding that if a startup appears unable to extract value from its own data set, it’s unlikely anyone else is going to want to invest in that data.

As an example of a successful data company Mason gave a shout-out to Dark Sky, a weather app that uses GPS data to buzz a user’s phone when it’s about to rain.


Photo by Jakub Mosur

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