Big Data: Stop Focusing On Size

It’s not the size of your data, it’s what you do with it, says IBM analytics executive

15 March 2013 – Rich Rodts, who manages IBM’s analytics academic programs, often finds himself discussing big data with family, friends, clients and business partners, including representatives from top universities across the U.S.  ”There really is no wrong definition of what big data is,” Rodts told InformationWeek in a phone interview. “I like to explain big data as taking a vast amount of information and being able to distill it in a way that can be consumed and acted upon.”

A common definition that’s often overused is one that focuses solely on the vast quantities of data being created, said Rodts, who offered an alternative view.  Big data, he said, “paints a picture” of a human being, including the often mundane tasks a person completes through the day: using an ATM, paying bills or buying movie tickets online, taking public transportation, and so on. “Each one of those things creates a unique data point,” said Rodts. “One that points back to me as an individual, what I like to do, what I don’t like to do, and where I am at certain times of the day.”

That’s a lot of collectable information about each of us, of course. But petabytes of data have little value unless they provide actionable information. “It’s not just the fact that there’s big data, it’s what you do with it,” said Rodts. “If you have that insight and don’t act on it, then it’s wasted effort.”

The ability to use big data to reduce reduce medical fraud or predict maintenance problems in a city’s water infrastructure are just two examples of what’s possible.

“As I start thinking about big data, and why I’m excited to be in this space, I tend to focus not so much on what big data is, but on what can be done with it,” Rodts said.

The ability to analyze social media streams, for instance, is changing the way businesses market a variety of products and services, including movies, clothing and cars.

IBM offers a suite of software tools for big data analytics, including Cognos, WebSphere and SPSS. But cognitive systems such as IBM’s Watson, which combines natural language processing, machine learning and the ability to generate its own hypotheses, have a future in big data as well. In-depth analysis of social media feeds, for instance, requires a better understanding of how words are used contextually in, say, Twitter and Facebook posts.

“Case in point: ‘Sick’ has very different meanings, especially in today’s society,” said Rodts. “It can mean a very derogatory remark, or ‘I don’t feel well,’ or ‘you’re distasteful’ — or it could also mean ‘this is really neat.’ The key is to determine how words are used contextually, particularly “when you’re looking at things like movie data, and especially when you’re looking at a younger generation,” Rodts added.

From a business perspective, one major benefit of applying big data analytics to social streams is that it provides insight into customer sentiment. When customers go online to research a particular product, they’re not just looking for an advertiser’s sales pitch, but also for comments — both good and bad — from their fellow shoppers. “That really changes the scope of how we look at data,” said Rodts. “How did they feel about how they were treated? Did they feel whole after the transaction?”

Businesses are looking to leverage big data to engage customers in a unique way “that endears them to their brand,” Rodts added.