Two of the dirtiest words in the Valley: "Big data”
I vividly remember being in a conference room at a prominent Silicon Valley VC firm (that I will leave unnamed) back in 2006 trying to raise seed funding.
One of the partners found his Blackberry much more interesting than our boring, data-focused startup, only looking up as he walked out the door saying, “Right now I am looking for Web 2.0 for the enterprise companies.” It was the era of Web 2.0.
Fast-forward a couple years. We’ve gotten pretty far bootstrapping the company and set out again to seek funding to scale. Now it’s the era of “big data.” And we do “Big Data for sales,” our tagline at the time.
This time it was a dramatically different experience. We received an astonishing amount of interest and signed our first term sheet in a matter of weeks.
The big data backlash
There’s been a lot of backlash at “big data” as a buzz term (recently The New York Times, AllThingsD), with Gartner adding fuel to the fire saying it’s headed into its “trough of disillusionment.” The backlash is also directed at companies that have adopted the term to describe what they do, and some deservedly so.
While data analytics continue to drive many of the most successful tech startups such as Cloudera, GoodData, and Splunk, countless other startups have fallen victim to the overhyped, overinflated and nondescript “big data.”
We picked up the term to better describe what we do (or so we thought), and to align with a hot trend.
Two years later, did we make the right call? Well, the answer is yes and no.
Riding the “big data” wave
As much as we all love to hate them, these buzz words in the technology industry serve an important purpose. Whether Web 2.0, cloud computing, mobile, social, Internet of Things, big data or any other, they provide a short cut to simplify a technological breakthrough and bring it to mainstream understanding.
It gives the media something to write about, a place for VCs to place their bets ($1.2 billion to big data startups, according to CrunchAnalytics), and innovative early adopter customers something to wonder about.
Entrepreneurs find opportunities to build new companies around them and industry leaders shift to incorporate them into their product portfolios (at least some of them). Salesforce.com is a great example of this. The company finds a way to capitalize on nearly every tech trend from both a marketing and, eventually, a product standpoint.
I’ll admit I cringed when we first started using “big data,” but it is what we do and it worked for us.
Where “Big Data” failed us
It was a different story with our prospects and customers.
The term "big data,” casts such a wide net, from infrastructure and analytics to applications, that it created confusion for companies and the term became meaningless.
Sure, we got a ton of interest. But on more than one occasion, we walked into companies to pitch our “big data” technology and there would be a room full of IT people wanting to know what their “big data” strategy should be. These weren’t the right meetings for us. The right people were not in the room. The curiosity didn’t translate into anything.
What I learned is that it’s important to remember that the Valley can be an echo chamber, only talking to itself. Companies outside of the Valley have a different perspective.
You need to listen to your customers. You need to create value for them. Period.
Today we’ve shifted how we talk about what we do to focus on the end result we deliver to our customers. We still talk about “big data,” but as a great enabler. We don’t have it perfect yet, but I can tell you that the interest we receive is spot on and continually accelerating.
In hindsight, would I do it again? Absolutely. It’s how the Valley works, as much as we like to complain about it. Without our beloved buzzwords we wouldn’t have ideas to rally around and create momentum.
But it’s important to know when it stops working. Trends should be picked up and discarded and not something to build a house on. And that you learn by listening to your customers.
So we’ve officially jumped off the “big data” train, but it definitely took us for a great ride and brought us to the right place.
[Image courtesy JD Hancock]