When Big Data Runs
Into a Little Reality
If you don’t build a capability
early in your startup, you’re
unlikely to get to it later
tation, security, agriculture, and fnance. Angel List
tracks nearly 5,000 big-data startups in its database.
The trouble is that many of these companies got
distracted, you know, starting up a company, so the
data and analytics projects were back-burnered, and
then allowed to go cold. “Big-data confessional”
is one of my favorite startup parlor games—getting
other founders to admit they never got around to
creating the intelligence engines they had once
dreamed of. Time and again, short-term demands
superseded long-term data dreams.
I’ve come to believe that the only way to do big
data right is to do it from day one. At Iodine, we
had a long list of data-driven ambitions, many of
which never saw the light of day. But we did,
thankfully, build out some analytics capacity in our
early days, and since then we have often gone back
and improved or fxed our data warehouse and
infrastructure. It helps that my co-founder’s pedigree is indeed exceptional. He spent time building
data tools at Google. But our success here owes
as much to just doing the work when there have
been so many competing priorities.
And there’s the rub: As easy as it may be to talk
big about big data, the actual doing is a slog—
plumbing and janitorial tasks that take a lot of efort
and yield marginal benefts. For many companies,
that work seems less rewarding than doing the stuf
that actually might drive hockey stick growth.
Which is pretty rational, when you think about it.
These days, big data doesn’t quite have the buzz it once did, but its progeny—machine
learning and deep learning and artifcial intelligence—are sown into pitch decks and business
plans like magic beans. The same story applies: If you’re planning on using one of them
someday, then it’s probably never going to happen.
And this goes beyond data and analytics. Any startup with some sliver of change-the-world ambition has a two-step strategy. Today we’re doing X, but tomorrow we’ll be doing
Y—that’s what we’re really building, they whisper. Trouble is, tomorrow is a long way
of. And if X starts to drive revenue and growth, it’s almost impossible to shift to Y without
jeopardizing any current success.
If you really want to be driven by data or A.I. or deep learning or whatever, the best time
to create that capacity is on day one. Figure out how it aligns with your business plan and
revenue strategy. And if it doesn’t, either change the business or stop kidding yourself that
you’ll get around to it. The best time to build the startup of your dreams is before you’ve
started building anything else.
IN 2013, WHEN M Y CO-FOUNDER AND I started Iodine, we—like pretty much any startup—sufered from certain delusions of grandeur. Our slick slide deck touted our esteemed pedigrees, our unfair advantages, and our uniquely brilliant business idea. We were talented, experienced, and absolutely original. In truth, we were riding in on a pretty crowded band- wagon—one called big data. We, like a lot of others, saw an opportunity to gather large amounts of information (in our case, about people’s real-world experiences with medications)
and put computation to work. Data would go into our black box,
analytics would happen, and out would pop insights and predictions
that correlated to better outcomes. Lives would be saved. Money
would be made. Particularly for health care, data is widely seen as an
elixir that will revolutionize an ossifed industry, rooting out waste and
failure and bringing intelligence to the marketplace. But the same
big-data demos were being done in many sectors, including transpor-
Thomas Goetz is a co-founder
of Iodine, a digital health startup
based in San Francisco. Follow him
on Twitter: @tgoetz.
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