Ask the Experts: Is Big Data Hype or Opportunity?

Welcome to Ask the Experts, brought to you by In this video, Intelisys’ SVP Cloud Transformation Andrew Pryfogle discusses how Sales Partners can find opportunities around big data with ServerCentral’s Eric Dominguez. Visit Cloud Services University to learn more about big data:

Andrew: Okay. Time for another Ask The Experts session, guys. We’ve been talking about applications, right? We’ve been talking about this idea of big data and where the opportunities lie around big data. Kind of defining that and figuring out how do we all make money in this space.
I thought I’d bring in one of the smartest guys I know about this topic. The Global Director of Sales Engineering for ServerCentral, Eric Dominguez. Mr. Dominguez, welcome back, my friend.
Eric: Thanks for having me.
Andrew: All right. Eric, talk to us about big data. Lots of buzz around this. The term is thrown around all over the place. Is this more buzz, more hype than reality? Or is this something that our partners really should get smarter about? I’m mostly interested to get your take on how does our partner community make money in the world of big data. Speak to that.
Eric: Sure. Big data is a buzz word, like any other buzz word in the industry right now. There’s some element of truth, and some amount of hype that goes along with it. Ultimately, big data is just a fancy way of saying we have a bunch of unstructured data that we’d like to filter through a program and take a look at in new and creative ways.
It’s something that we’ve been doing for a long time, but now we’ve got a buzz word to associate with that and create a little additional hype, as it were, around that type of data.
Andrew: Got it. The analytics around this and crunching this data can be really, really insightful to a business. Give them insights that they’ve never had before, for sure. Where are you seeing ServerCentral be a player in big data? What do those solutions look like?
Eric: Well, big data is pretty interesting, because the infrastructure that needs to be in place to run a larger scale big data farm is highly specialized. You have to have the information and knowledge on how to put these system together to really run an optimized big data set of infrastructure. We’ve been doing that for a very long time now. We’ve got the resources, the infrastructure, and the know-how on how to put these systems together.
Andrew: Got it. Fantastic. That’s an important one. Because a lot of times the customers are left up to their own devices of how do they go out and build this platform that can run these big data analytics that are important to them. Am I correct? I mean, running solo on that can be dangerous for them.
Eric: Yeah, it can be dangerous. They can ultimately end up spending a lot more money than they would if there were to reach out to somebody who’s already done the work. It can save them a lot of time as well. We put these things together every day. When the company is putting something like this together for the first time, there was a lot of research that has to go into the ultimate solution. Whereas we can solve that with a few simple phone calls and some interviews on what the data set looks like.
Andrew: A lot of customers would think that the big public providers like Google, Azure, obviously, Amazon Web Services would be the natural places for big data analytics. Where are finding that you’re winning against those types of providers for big data applications?
Eric: Yeah. Believe it or not, those large hyperscalers are really bad place to put big data applications. The inherent bottlenecks, especially in I/O subsystems of the hyperscalers, make them a poor environment for big data and larger scale data analytics. These systems need to be highly specialized. They need a local storage in a lot of cases, just because they’re so thirsty for I/O throughput. The hyperscalers can’t achieve it–or if they can, the costs are astronomical.
Andrew: Got it. Really interesting insight, and probably dispels a myth out there that big data belongs in pure big public cloud environments. It sounds like if you’re interested in performance, and actually having these big data applications working and being efficient and cost-effective, places like ServerCentral may be a smarter place to go.
Eric: Absolutely correct.
Andrew: Awesome. Hey, Eric, great stuff as usual. Thanks for diving in. Guys, that’s Eric Dominguez. He is the Global Director of Sales Engineering for ServerCentral out of Chicago. Good friend and one of the smartest guys I know in this space. Make sure you check out the learning center for ServerCentral here at the University. Go deep with them, guys. They know these applications, they can have the in-depth technical conversations with your customers to build the credibility that you need in that selling process. We’re big, big fans of them. You should be too. Good selling, everyone.