In the age of Big Data, figuring out the best ways to store, move, analyze and make sense of data can be everything for a company. Or, all the data at a company’s disposal could amount to nothing if the business doesn’t know what to do with it.
“People can ask about things like supply chains and sales pipelines and very specific areas,” said Frank Bien, chief executive of Big Data analytics company Looker. “But when they want to ask more broad, ad hoc questions in every department in a company, it’s very difficult. And what we’re trying to do is make that less difficult.”
Bien says that in helping Looker’s clients find the value in all of their data, his 400-person company has “a focus on boring stuff that works, as opposed to being flashy and getting headlines while doing little to transform a business.” Based in Santa Cruz, Looker has so far received the backing of $180 million in funding from CapitalG (formerly called Google Capital), Redpoint, First Round Capital and Kleiner Perkins.
In an interview at Looker’s San Francisco office, Bien talked about the state of Big Data and how Looker is helping its customers solve their Big Data management issues. His comments have been edited for length and clarity.
Q: Companies have always dealt with data. What’s the difference with data today as opposed to recent years?
A: We help companies better understand all the data they have and use to driver their business, and I think this has been sort of a holy grail for a long time. Companies want to be data-driven. They want their employees to be able to ask questions and get factual answers. But, surprisingly, it hasn’t been solved, yet, in a very broad sense.
Q: When you say “data-driven,” what does that mean in terms of the data we’re talking about?
A: When we started out, we wanted to have a very business-centric focus. But we really felt that companies didn’t really understand their core business model all the time. Companies didn’t understand the lifetime value of their customers completely, and there was this chaos going on where people would think they were agreeing to numbers like the value of their customer. But, you would see that they didn’t have that value defined the same way, and while they thought they were making business decisions based on that data, it, maybe, wasn’t accurate. And it was really a mess underneath. What we wanted to do was bring it into the reach of any company so it could use data more effectively.