Database Reference
In-Depth Information
Gutierrez:
In interviews, you talk about investing in companies that trans-
form passive data into active data assets. How do you find these companies?
Ehrenberg:
There are two principal approaches to opportunity identifica-
tion. One is curating our significant inbound deal flow that either comes to us
“warm” through trusted connections or “cold” through direct outreach from
someone not in our network. The other results from hypotheses we have
about a particular market opportunity or emerging space. In this case, we're
actively looking for people trying to solve problems where we see a market
gap and where we're passionate about finding solutions.
The issue of helping to transform passive data into active data happens later.
We invest in a team that is building a business. We do not invest in a team
building a data set. The data set is exhaust that emerges from interacting with
customers in many cases. Once a company is interacting with customers, we
can then think about the exhaust and how the company can improve the
product or customer experience by gaining insights from the data.
Gutierrez:
Who or what helps shape your views on the data space?
Ehrenberg:
Aside from reading current literature, I would say just watch-
ing how society grapples with rapidly changing conditions and the wave of
new technologies. As our portfolio grows and as we have greater longitudinal
experience with our companies, we are able to learn more and more about
what's working, what's not working, and what are some big problems that
simply haven't been solved yet. We're always collecting data, whether it's inter-
nally generated data from our own companies or things we're observing in
the outside world.
Gutierrez:
How did you form your first views on data-centric companies?
Ehrenberg:
A great deal of it was through osmosis from my experience on
Wall Street. The last five years of my career entailed living in the world of
high-speed data and feeds. This gave me an understanding that while the infra-
structure that existed in the late '90s and early 2000s was good, there was a
huge gap relative to what I saw as the inexorable increase in the velocity and
volume of data. I understood that new technologies needed to emerge to
handle the changing world.
The other experience that helped shape my views was how predictive analyt-
ics and semantic intelligence could be practically applied. Again, these were
things we were dabbling with in some of our strategies at DB Advisors. So,
even though I had left Wall Street, I found both areas very compelling. What
was interesting was that most of the conventional quantitative approaches
were already reasonably well-known. The literature was out there for every-
body to see, so there was going to need to be innovation either in the kind of
data that was being parsed to generate insight or in the technologies to parse
existing data to generate better, faster insights.
