Big Data, Meet Long Data

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Are there real world advantages to saving every bit of digital information that comes a company's way? And can a vast repository of historical business data -- facts and figures dating back decades or even longer -- help an organization understand its customers better?

In a recent Wired article, "Stop Hyping Big Data and Start Paying Attention to 'Long Data,'" applied mathematician and network scientist Samuel Arbesman argues that big data, as its commonly defined today, lacks historical context.

"Sure, big data is a powerful lens -- some would even argue a liberating one -- for looking at our world. Despite its limitations and requirements, crunching big numbers can help us learn a lot about ourselves," Arbesman wrote.

Indeed, historical data certainly has a lot to teach humankind in general, not just businesses hoping to sell more widgets.

What we need to focus on is "long data" -- information with a "massive historical sweep" that holds "tremendous potential for learning about ourselves," Arbesman wrote.

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Of course, the crux of Arbesman's essay isn't that organizations should warehouse every bit of digital information that comes their way. But according to Pitney Bowes marketing director Benjamin Bruce, businesses can enhance their customer relationships by embracing many of Arbesman's long data ideas.

"Big data is more about taking a slice in time across many different channels," Bruce told InformationWeek in a phone interview.

But long data involves looking at information on a much longer timescale. And ignoring customer data and records that go back decades can limit a company's ability to connect with its customers, he said.

"Recently we did a survey on big data, and one of the things we found was that 80% of senior executives really aren't getting the value out of it," said Bruce.

The cause of this big data disillusionment is open for debate, but the ability to pull insights from historical data could help brighten these executives' dour outlook.

"Many companies, if they've been around, [have] data that goes back 10, 20, 30 years," said Bruce, adding that a "longer-term view" can provide information that businesses might miss if they examine data that only goes back five years or less.

"Life events, transactions, store visits: The subtle shifts may not be visible in one to three years," Bruce noted.

Acknowledging that his advice for companies was "aspirational" rather than a clearly defined game plan, Bruce said that organizations need to decide which long data is worth keeping -- a feat that is easier said than done.

Compounding the problem is the fact that most organizations lack a CDO: a chief data officer.

"You don't see many firms with chief data officers or somebody who maintains that customer data," said Bruce. "From my perspective, the lack of a single role to own that (data) makes things a little more complicated."

As an organization creates a big data platform -- moving beyond theoretical concepts toward a nuts-and-bolts strategy -- a major debate may break out in the c-suite: What, exactly, should we save?

For instance, does the company become a big data hoarder, spending vast sums to stockpile every bit of information, no matter how arcane? Should it save only the data that will deliver value decades down the line? And if so, how does it determine which information is worth keeping?

Of course, there's no official, international standards body-sanctioned definition of big data. But the most popular description -- Gartner's "3V" definition trio of high volume, high velocity, and high variety -- doesn't explicitly include historical information as a key component of big data.

Companies want more than they're getting today from big data analytics. But small and big vendors are working to solve the key problems. Also in the new, all-digital Analytics Wish List issue of InformationWeek: Jay Parikh, the Facebook's infrastructure VP, discusses the company's big data plans. (Free registration required.)