Does your big data project need a data concierge?

When CIOs decide to invest in big data, they tend to talk about new technologies or missing skills, but, according to Anthony J. Scriffignano, senior vice president of worldwide data and insight at Dun & Bradstreet, that talk will get businesses only so far. CIOs also need to learn how to ask better questions and invest in the "neural networks between their [employees'] ears," he said. (Aka: human biases.)

The typical ways of dealing with ballooning data problems -- to build bigger hard drives or more distributed storage environments -- provide only a partial solution for leveraging big data. But the data volume, velocity, variety and veracity are evolving at such an accelerated clip, it's nearly impossible to keep up. "The elephant in the room is that the problem is changing faster than we are as a human race," Scriffignano said during his presentation at the Big Data Innovation conference in Boston.

Given our insatiable appetite for newer, better, faster technology, which inevitably generates more and more big data, that's a problem that doesn't seem to be going away anytime soon. Consider the iconic photograph comparing Pope Benedict XVI's installation in 2005 to Pope Francis' installation in 2013. In 2005, it was hard to spot more than a couple of flip phones; eight years later, the crowd is aglow from smart device screens. But rapid adoption of technology produces an unbalanced equation for enterprises: More devices means more data; but more data doesn't necessarily mean better answers, Scriffignano said.

Better questions of big data should include factors that influence the answer but tend to go overlooked. Scriffignano sums this up with his four E's: exogeny, expression, enterprise and environment. Don't just ask the question, but understand the assumptions, beliefs and biases that influence the question you're asking, he said. Before sending in the brilliant data scientists, CIOs should evaluate their data curation practices: Do those analyzing the data know how it was cleaned, collected and can be used? "Unless you understand the context of data, you often don't know how to use it," he said.

Even worse than a lackadaisical metadata program? "You don't assess the impact of the data you don't have that could have informed decisions [italics, mine]," he said. One way Dun & Bradstreet unearths the known unknowns is with the use of proxy data, or data that can act as a stand-in for the stuff that doesn't exist. Think of these questions in terms of the moon landing. "When [NASA] was creating the models for how to land on the moon, no one knew what the surface of the moon was like," he said. "We do this with our data."

As the company gets smarter about data collection and query, Dun & Bradstreet is also aware that the environment is still morphing. Scriffignano pointed to two huge challenges: unstructured data, which accounts for nearly 80% of all data produced today, and foreign language and writing systems, where meaning can get lost in translation.

An example: Boxpark, founded by Roger Wade, is a pop-up mall in Shoreditch, England constructed entirely from shipping containers. Businesses sign short-term leases that are three to 12 months long, making it possible for one shipping container to host four different businesses in a single year. "That's a big, big problem," Scriffignano said, especially for Dun & Bradstreet. The typical third-party data provider collects and sells data about businesses, which are instantiated based on name and address.