Big Data Analytics, Beyond The Buzzwords And The Data Lake

25. January 2018.








Data is everywhere. Data is not static. Fraudsters are everywhere, and they are never static, never at rest.

Taking data in and making decisions — whether about customers or hackers, products or web traffic — may be a simple concept, but it is hard to translate into consistent practice at the enterprise level.

That’s because, as DataTorrent CEO Guy Churchward described it in an interview with PYMNTS’ Karen Webster, data all too often resides in huge data lakes — bodies of information, centrally housed within firms, that are not utilized effectively.

Simply put, collecting the data, storing it and analyzing it may make firms feel they’re in control of their environment, that they have information and analytics at the ready and are able to even access them in real-time. But there’s a big gap between perception and reality when it comes to real-time activities — whether it be a changing competitive environment or an assault via cyber thieves.

“You think about business, and it is really about trying to understand what is happening with the landscape, getting an evaluation of it and making a decision on it as fast as you can,” the executive told PYMNTS.

However, the speed and efficiency of decision-making is slowed, and even rendered useless, by the data lake — marked as it is by what Churchward called “stale data.” The salve is to shift the focus away from data lakes toward the very point where information comes into the organization: at the periphery.

Churchward said there seems to be confusion over just what real-time analytics might mean, even to the people who say they’re deeply involved in doing the analyzing. All too often, IT professionals think they’re working with data in real-time, but what they’re actually doing is launching real-time inquiries into stale databases.

And that “loop around” — where an inquiry about an event is sent to a data lake, information is retrieved and then examined — can take time, which is a precious commodity when it comes to real-world operating environments. Churchward cited Google Earth as an example, where a user may zero in on his or her street, find a photo and know that a tree in front of a given house was cut down recently. Thus, the data is stale, but the person consuming that data has other ancillary knowledge to make a judgment on what they’re being shown.

Or consider a camera-driven house alarm system, which sends you a text that there was activity outside the back door … three hours ago.

This, of course, is not advisable in an environment where firms must authenticate customers or approve transactions on the fly. With data lakes, he said, companies and IT professionals “are running off a batch process … the time lag is huge.” If enterprises are running architecture for security or fraud prevention analytics efforts in a batch mode but the fraudsters are operating in real-time, “Who’s going to win?”

Thus, Churchward told Webster, education of DataTorrent’s corporate customers has been key, encapsulated by a maxim: “He who finds out the information first and more accurately will actually win” in the corporate arena in which he plays. That mindset holds true with, for instance, online trading systems.

Firms may benefit from the realization that they may also only have a small population of their applications (or businesses) that need to work in real-time.

Most organizations do not have the practical ability to embrace change — in this case, moving to real-time data analytics.

Simply put, said Churchward, firms exist on how much money they take in and how much money they spend. Or, to put it another way: “You are either competing with Uber, or you are Uber … When you are in hyper-growth, you don’t really care about the mechanics of costs … in theory, everybody knows what they need to do, and in practice, they do not have the dollars to do it.”

With an eye on costs, partnering with companies that provide Big Data analytics platforms — scalable ones (via DataTorrent, for example) — firms save hundreds of millions of dollars and prevent the lasting damage that can come by showing up on the front page of The Wall Street Journal amid data breaches, he said, only slightly tongue in cheek. He noted that his own firm has a Jumpstart offering wherein DataTorrent gets clients up and running and moving to their Big Data goals within 60 days.

If speed and agility are key in fraud prevention, bigger firms are taking notice, said Churchward, as some of the biggest players across far-flung industries are in the throes of transformation. Car manufacturers, he said, can be thought of as Big Data analytics companies that just happen to make cars, citing one auto behemoth that makes a model with 27 computers and hundreds of IoT (Internet of Things) sensors in the car. “They need both fast analytics and 100 percent integrity of the data,” he told Webster.

For retailers on the omnichannel side, he said, “Things get really interesting … we’ve gone from fraud detection to fraud prevention,” and analytics can play a role in cementing customer relationships.

At Nordstrom’s, for example, Churchward recounted that a consumer can swipe a card and in the less than a second it takes to read that card, the retailer can examine cookies tied to that user’s online identity, “and they know through the cookie what has been viewed on their website. They look in the inventory, and they see what [the customer] was looking for.” Thus, a pop-up at the point of sale, reminding the customer of a dress perused online — now tangibly in stock: Might they be interested?

When walking into a brick-and-mortar location, the design is to “sell you everything they have on-premise in that store,” while the internet, by way of contrast, “is designed to sell you anything that you want.” Such tailored solicitation is a leg-up in a world where brick-and-mortar retail must compete with Amazon at every strategic twist and turn. Thus, data rendered actionable, in real-time, can make all the difference as competitive dynamics change.

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