sqrrl Focused on Helping Customers Extract Value from Big Data, says COO Kahn
Channel: SiliconANGLE theCUBE
Date: 06/02/2013

In past posts, I've written about the profile of a Data Scientist, especially the skills needed for people to grow into this new role. I've also written about the opportunities that Big Data provides, drawn in part from the Human Face of Big Data book, written by Rich Smolen.

Despite all of the terrific possibilities that Big Data can enable, people ultimately need to realize that the value of the data is about people and driving change. About two years ago, Harvard Business Review published an article in which authors conducted research studies trying to pinpoint the key drivers of productivity within an organization, which included decision-making ability, access to social media, and many other elements. Ultimately, the most valuable productivity driver proved to be access to information. The implication of this finding is that if those people making important decisions have access to more or better information than others, they will make better decisions.  

Now fast forward to a more recent HBR article from January/February 2013 in which the authors have a somewhat different perspective, saying it is not enough to have better information; you also need to act on it.   The reality is for many people data is foreign, and making decisions based on it can be counterintuitive or confusing. That is, we have a situation where people may get a report on certain information, new markets, or flagging products, but they choose to ignore the information it provides because they may not know how to interpret the data or what to do with it.

Recently, I was at Babson College participating in an advisory roundtable discussion about analytics and Big Data.  The advisory group in which I was participating was asked "What are the biggest impediments to adopting analytics or Big Data, and what can be done about it?" A panelist seated to my right, Chief Scientist at a local Big Data company, commented that "People need to learn to trust their data.  Trust the math."

 

He explained that he has a PhD in aeronautical engineering, and in his former life he had to trust what the data told him, because it had a life or death impact. Putting 500 people on a plane at 35, 000 feet above the ground meant that the data had to be correct, and the people building and controlling the planes had to trust it. 

I think this is a critical truth that organizations must embrace in order to make the leap to using Big Data in the confines of their organizations. It's not enough to just buy a new piece of analytics software, or store much more data than before. Unless they believe data analytics will work and they are willing to try it, I don't think people have a chance to drive change. And isn't that what all this is about? If we are collecting mountains of data, but no one is analyzing it, then we are not deriving any new value. If people are analyzing data, but the analysis is not driving any decisions, then it's a lot of work for nothing. 
 

At a previous employer, I worked as an advisor to banks examining portfolios of mortgages and other types of consumer loans to assess their level of compliance with Federal and State regulatory laws. In case after case, the lenders who fared best were, of course, the ones who did rigorous analysis and took the appropriate actions. Among the rest, it was clear that it was worse to do an analysis and know a problem existed and ignore the data, than it was to never do an analysis at all.  The reality, however, is that it is very difficult to change habits. If people are used to making decisions a certain way, then introducing more data may not influence their decision process. For these reasons, I think it is incumbent on leaders who want to implement Big Data strategies to be open-minded -- take pains to identify and challenge operating assumptions. As Charles Duhigg argues in his book "The Power of Habit," any habit can be changed, but one must be conscious of the habits that drive behavior and decisions.