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I think that when most people think about big data, they think infrastructure.
They think of enabling technologies. We've done a bunch of investments in
that area. We may or may not do a lot more of that, at least in this next wave
of opportunities. If you look at what we've done lately, they're much more
applications. Whether it's reshaping how quality inspection is done in manu-
facturing processes, or infrastructure-as-a-service for the developer commu-
nity. We've also made three investments in the healthcare space.
As the market has evolved, we've evolved. We've gotten very clear about
what we're really good at and how we can help the most. That's naturally
caused us to gravitate toward certain kinds of founders and certain use cases.
If we were to sit down and have this discussion in three years, I'd be fascinated
to hear what I was saying, since we've evolved and sharpened our focus and
investment methodology since the early days of 2010.
Gutierrez:
A term that has come into prevalence is “data scientist.” Do you
think it's a similar thing where right now in 2014 it means something, but it's
generally going to lose its meaning, or do you think that it eventually becomes
a much more well-defined role?
Ehrenberg:
I think it's something now that's distinct because we need to
make more of them: there aren't enough skilled people to manipulate and
extract meaning from data. One way the market is trying to solve this shortage is
through new technologies and applications that give the power of data science
to non-data scientists. We back a company called Data Robot that essentially
places the power of a data scientist in the hands of a non-data scientist. So I
think we're going to see many more tools and technologies to democratize
the power of the data scientist.
At the same time, I think we'll see the value of people who can go beyond
this continue to rise, because while these tools are powerful and are sufficient
for a wide range of use cases, they are not a panacea. It's kind of like machine
learning and AI. These are areas where tremendous progress has been made,
and yet you still need human input to get the most value out of these systems.
I think data science is much the same.
Gutierrez:
IA Ventures is one of the few investment funds with a data
scientist serving as a scientist-in-residence. How will that role evolve?
Ehrenberg:
Having somebody like Drew, our scientist-in-residence, on the
team is valuable. Whether or not it's going to be somebody who's more on
the investment team that has that set of knowledge or somebody fulfilling a
similar role to Drew today—I don't know. It may be that one of our invest-
ment people will look a lot like Drew in makeup but still be an investor and
not a data scientist. I think it will evolve. As data science becomes built into
everything, it's not clear to me that we will need someone of his stature.
