Sramana Mitra: Why don’t we take the real estate constraint off and listen to what you have to say about the movement of Big Data and the industry trend in general.
Sheridan Hitchens: This may sound controversial. I work in a technology function. The technology side of things is getting pretty good. The whole Hadoop ecosystem is a lot more mature than it was when I first started doing some of this stuff seven years ago. We’ve got lots of good real-time streaming stuff with Kafka and Spark. The technology around that is maturing.
Technology is becoming less and less of a barrier to this. Compute goes up. Storage goes up. Unless you’re Google or Facebook, you don’t face data volumes that are unbelievable large. Likewise, I feel that the data space is not just a question of collecting the data. The data is getting more and more commoditized and easier to get. It’s what you’re doing with it that’s going to count. That’s where people are going to differentiate themselves.
I gave an interview about a year ago where I said, “Data science functions are going to be predominant in all companies.” I’m less sure of that now than I was back then. You’re seeing it happen to some extent already. You’re going to see some set of core data science functions, but you’re going to see data science evolve to all the engineering functions.
All of those pieces are going to be very important to engineers, generally, to have, as opposed to it just being the domain and skill set of one person. We all know how difficult it is to hire data scientists. Lots and lots of people put data scientists on their LinkedIn profile but data scientists who’ve been through full implementations is a much smaller set. You’ll see people develop skills within that and you’ll start to see Data-Science-as-a-Service.
Sramana Mitra: There are several firms who are doing that actually who basically have core competency in data science. They’re like the McKinsey of data sciences.
Sheridan Hitchens: Even Google has prediction engines and the like. HP and IBM have opened up a bunch of APIs and services. One of the pros of using one of these services is that you can start to do predictions much quicker. The downside is you’re dependent upon an algorithm over which you have no control. We all know that companies deprecate stuff.
Sramana Mitra: All right. Do you have a different answer to the question about starting a company in data if I took off the real estate constraint?
Sheridan Hitchens: That’s an interesting question. Let’s me give you some high level perspectives on what I see in the marketplace and then let me tell you what it’s like at a practical level. I see lots and lots of companies doing things like Tableau in the cloud or dashboards in the cloud.
What you see in these demos is that they are based on these wonderful sets of data, but you’re using third-party data and data from legacy systems. Being able to manage data governance and control, and the merging of data, and the problems you face at a practical level in the company is a broad area where there’s opportunity.
Sramana Mitra: Any other thoughts on that topic?
Sheridan Hitchens: That was the one that immediately sprung to mind on the data side. Nothing else is jumping out at me at the moment.
Sramana Mitra: Thank you for your time.