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Pepperdata, a provider of tools that optimize IT infrastructure for computation, has seen a lot of trends come and go over the years. Now organizations are using the company’s tools to optimize infrastructure to process AI models.

VentureBeat caught up with Pepperdata CEO Ash Munshi to gain a deeper appreciation for IT issues, such as data management, that are holding back the rate at which enterprise IT departments can meaningfully implement AI. But he also pointed out that a lot of companies struggling with AI might be fighting the wrong battle for their business needs.

VentureBeat: What’s the trouble with data management in the enterprise today?

Ash Munshi: When we had just classic databases, data warehouses, and stuff like data was managed sort of centrally, people had a very well-defined view of what was going on. It was very narrow in scope. That definition has been blown to smithereens. It’s like everything is enterprise data. It’s just ballooned.

People have recognized data is important. That’s a good thing. More data is better, but what do you do with it? People don’t really know this.

VentureBeat: Do you think AI is bringing about the moment in time where there is a constitutional crisis that is getting everybody to focus on this issue?

Munshi: I think it is. Companies that leverage data are gaining a competitive advantage. The big guys have proven that’s the case. People are realizing that data for customer success is really important. That part is becoming more obvious to more people. If somebody comes to my website, and I take three days to respond to them, they’re going to be gone. But if I can respond to them in 30 seconds and say something intelligent, all of a sudden that interaction becomes much more valuable. My sales cycles become much shorter. The rest of it, concerning how to use the data to more efficiently run my business, however, is completely unclear at this point.

VentureBeat: Do you think that there’s a disconnect between what the business users think they can do today with data versus what IT knows is possible?

Munshi: You’re absolutely correct. There is the desire. I can do these amazing things. Then you go back and look at the data and say, “Do I really understand all this stuff?” They realize they don’t really know what they’re building, their costs are running amok, and they’re not getting the insights they actually need. They actually need more data than just what’s in their silo. We’re still at the early part of it because there’s a lot of disillusionment. I think we’re getting to the trough of disillusionment right now, where people are saying only all that stuff is great, but what the hell is it doing? I’m spending more money, but I’m not understanding anything better.

VentureBeat: Do business executives really trust the data in the first place?

Munshi: That is a very valuable observation. Most data is noisy. You’ve got lots of data you can manage. But what does that mean? The discipline for being able to do that is probably missing in most organizations. They need to be able to understand the data. Quality has many different dimensions to it. That requires a business-driven view combined with an architectural view.

VentureBeat: Organizations are investing a lot of money into data operations. They are hiring data engineers and data science teams, but have they put the cart before the horse?

Munshi: I probably shouldn’t say this out loud, but I think the answer is yes. The CEO comes down and says, “We need to be a data-driven organization. We need to use AI.” Everybody then talks about AI and how revolutionary is going to be, so you sort of find excuses to use it when in reality you don’t even have the right data to be able to use it. Deep learning is a perfect example. I see lots of people who say, “I’m doing deep learning.” Great. How big is your data set? “100,000 points.” 100,000 points is totally minuscule. When you start doing 10 million, it gets interesting to be able to go do that. The reality is at 100,000 points, you could use old-fashioned statistics and probably get a better answer.

It’s being used for essentially gratuitous reasons.

VentureBeat: How did we get into this mess?

Munshi: It happens in computing over and over again. Every time we do a new technology and all of a sudden people invest a ton in it, then you find your finance people are writing it off. This is no different. The data wave has been hyped so much that people are putting more and more money into it. They got to be like Google. They have to be like Facebook. Do you really need to be like them? Is that really the business model that actually makes sense? Can I take some of my manufacturing workflow and make it more efficient? Absolutely. But you need to know what you want to apply and where you want to apply it.

There’s still plenty of room for old-fashioned people with instincts that matter. At the end of the day, data provides you insights. Those insights give you the ability to create a gut instinct, and that gut instinct is the fundamental thing that you use to make decisions.

VentureBeat: Will there be a backlash against all this?

Munshi: Right now, the number of people drinking the Kool-Aid is so massive that I don’t think the collective psyche can say we made a big mistake. We’ll figure out a way to rationalize and say it was all part of learning.

VentureBeat: What’s the solution to this mess?

Munshi: Every organization needs to hire people who actually understand how to use data. They need to know what data they have and what kinds of questions that data is capable of answering. Don’t boil the ocean. Pick vertical pieces that are high value and then create velocity around that.

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