I've Got All This Big Data, Now What?

Now that your organization has loads of big data, It's time to start poking around TO learn what you don't already know.

Jim Schakenbach, Contributing Writer

February 13, 2015

3 Min Read

Big Data. Ugh. Mention it in a room full of C-level professionals and marketing people, and the response can be a mixture of eyeball-rolling and a vaguely-queasy, induced fluttering in the stomach.

We’ve been gathering, talking about, and pushing around big data (to make it less intimidating let’s not capitalize it here) for almost 15 years. Still, while some companies handle it better than others, lots of IT and marketing departments suspect they’re not really dealing with it well. So what do you do with all that big data?

First, let’s wrap our heads around what big data really is, because the definition has grown exponentially fuzzier over the years as more and more people define and assign value to big data based on their particular wants and needs. In 2012 Gartner defined it as a “high volume, high velocity, and/or high variety information asset that require new forms of processing to enable enhanced decision-making, insight discovery and process optimization.” Okay, then. Everybody clear on that?

I prefer how technology marketing consultant Lisa Arthur, writing for Forbes, defines big data: “Big data is a collection of data from traditional and digital sources inside and outside your company that represents a source for ongoing discovery and analysis.”

She further defined it as a mixture of unstructured (stuff that is unorganized or not easily interpreted by traditional databases or models) and multi-structured data (other stuff in a variety of data formats and types, often gathered from people/machine interactions, such as web apps).

What people find most intriguing is the unstructured data. Industry analyst Susan Etlinger of the Altimeter Group declared in a recent white paperthat “the human-generated and people-oriented nature of unstructured data is both an unprecedented asset and a disruptive force.” To properly utilize unstructured data and extract the most value from it, Etlinger recommends adapting processes and technologies to:

  • Identify appropriate sources

  • Crawl and extract the data

  • Detect and interpret the language being used

  • Filter it for spam

  • Categorize it for relevance (for example, “Gap store” vs. “trade gap”)

  • Analyze the content for context

  • Classify it for action (e.g., customer service issue, product upgrade, sales, etc.)

In short, big data is only as valuable as your ability to organize, analyze, and utilize it. It requires a new way of looking at both new and traditional data and taking a different approach to it. Here’s an example: Back in 2009, Google used big data to help the Centers for Disease Control (CDC) track the H1N1 virus. Initially, the CDC took the traditional route to track the disease’s spread by requesting physicians report signs of flu in their areas. The data took a while to compile, so although it was accurate, it was of relatively little use for helping the CDC get ahead of the illness spreading across the country.

Enter Google. The search engine giant realized it could quickly analyze flu search terms geographically and build its own database using big data methodologies to provide equally accurate data almost instantaneously. Here’s the interesting part: Google staffers saw no patients, rendered no diagnoses, and didn’t even have any medical training. Yet their results were just as accurate -- and more effective -- than those generated by thousands of trained medical professionals.

So what’s the point? That big data’s just data. It’s all in how you look at it. If you’re sitting on a big, fat database chock full of customer, supplier, sales, production, and distribution data, think about how transportation companies mine weather and traffic pattern data to make distribution routes more efficient. Think about how retailers use social media feedback, demographic trends, and sales returns to fine-tune their stock orders. Then take all that big data that’s gathering dust and poke it around a little bit to see what you can tease out of it.

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