When we hear the phrase “big data,” we have to ask ourselves, “How big is big? What are we really talking about?”

Let’s take one of the largest retailers—Walmart. Start by visualizing one five-drawer filing cabinet. Now, think of a room filled with 60 million five-drawer file cabinets. That’s how much data comes from all of the Walmart stores every hour. And as retailers install more sensors to add advanced predictive analytics to real-time sales and customer behavior, that figure of 60 million filing cabinets worth of data every hour is going to increase. For example, retailers are beginning to use mannequins with cameras in their eyes so they can see who’s looking at them and whether they’re male or female, pregnant or not, thin or heavy, etc. And that’s just one little data point.

In the past, I’ve written about how we’re using cameras in the stores, not just for security but also to create actionable data on where people go, when they leave, where they stay, what they buy, and what they just look at and move on. All of this is creating an ever-increasing tsunami of data—so much so that we have to realize it is, indeed, big data, and getting bigger.

What’s even more amazing is that if we look back 20 years, from 1993 to a few years ago, the total amount of data that went over the Internet in a year is now how much data that goes over the Internet in one second. It’s important to note that most of that increase has been in the last few years due to the exponential, and predictable, advances in processing power, storage, and bandwidth. And, by the way, a hard trend certainty is that the amount of data is going to increase as data gets bigger and our desire to get real-time high-speed analytics increases.

So we have to ask ourselves, “What are companies doing with all this data? Is it paying off already, or do we have to wait for the payoff?” The answer is, “The payoff is starting to happen already.”

For example, I was in Canada recently and visited a couple of electronics chains, one called The Source and the other called Charlie Brown. These stores are using real-time analytics of sales to make decisions.

They noticed that in all of their stores a purchasing shift had taken place; several specific upscale electronics items that started at about $650 were selling a lot more than the lower price models, which were in the $150 price range. So they started filling the shelves with more of the higher-priced merchandise and greatly reduced the number of lower priced models. Sales in the categories that they made those changes in surged 40% in a very short amount of time.

A 40% surge is not bad. And thanks to the real-time data, they were able to know exactly which products they needed more of. There was no guessing involved. They could zero right in on a shift in purchasing and make the changes pay off immediately.

They also looked at their lower-end items and noticed which specific items were decreasing in volume, so they discontinued them completely. Again, overall sales and profitability rose, because profitability is based not only on the items that are selling, but also the merchandise that isn’t selling and taking up space in inventory.

Of course, retailers have been doing this for a long time—deciding which products to remove from inventory and which to increase. But it wasn’t done in real-time. It wasn’t done with the pinpoint accuracy that we have today. Thanks to the data that we’re getting in from various sources, retailers can make better decisions faster and increase their bottom line.

Ask yourself: How could we use big data and high-speed analytics to make better decisions faster so that we could gain competitive advantage and drive increase profitability?