Tom Davenport is the President’s Distinguished Chair at Babson College and a Research Fellow at the MIT Center for Digital Business. He is also a Senior Advisor to Deloitte Analytics and the co-founder and Research Director of the International Institute for Analytics. Beyond those credentials, Tom somehow finds time to be a prolific book author.
Keeping Up with the Quants: Your Guide to Understanding and Using Analytics (Harvard Business Review Press, 2013), was described as a “go-to guide on ‘quantitative literacy’ — so you can develop the analytical skills you need to keep up in today’s data-driven world, no matter your role or experience.” This is the 17th book he has authored, coauthored or edited. In fact, his next book, Big Data at Work: Dispelling the Myths, Uncovering the Opportunities, is due out in early 2014.
Smart Enterprise Exchange Editor and Community Manager Paula Klein caught up with Tom to get his response to a few key points about big data and analytics at large enterprises. Read his blog here.
Q1. In the recent book, Keeping Up with the Quants, you argue that everyone needs data analytic skills and you offer ways to develop these. Do you believe that most business people can — or should — master quantitative analytics? Don’t we need the quant specialists to do the heavy data crunching for us?
A. It's a matter of degree. We do argue that every manager needs a better understanding of quantitative decisions and analytical thinking. However, this doesn't mean that [managers] will have to do their own analyses. There will continue to be a need for quantitative analysts who can do the hard, serious analytics. But business managers and professionals need to work closely with them to frame the decisions being made, to discuss the choices of variables and models used, and to communicate and act on results. It's really a partnership — not something you can just turn over to the quant in the back room.
Q2. Why are organizations struggling to extract business value from their big data repositories? Is the organization to blame or the tools (or both)?
A. Well, by definition big data is data that is unruly — too big to fit on a single computer, too unstructured to fit into conventional databases, too fast-moving to be easily warehoused. So it's not surprising that it is difficult to transform into a valuable business resource.
When I interviewed a group of data scientists in 2012, I felt the job should more accurately be "data plumber" — extracting value from big data involves a lot of difficult and time-consuming labor, and you get your hands dirty in the data quality and extraction issues. In addition to the new problems raised by big data, most organizations are still dealing with data integration and management issues from the small data era — standards, integration, quality, governance and so forth.
When we add the complexities of big data, it's like adding a parade to an existing traffic jam, and it can mean that nothing moves for a while.
Q3. On the topic of privacy and security concerns, is there really any way to protect consumers and the public from having their personal data mined, analyzed, sold and dissected?
A. I don't think so, at least not in the United States. It is too difficult to control and prevent these activities on personal data, and no one in the U.S. government seems capable of regulating them intelligently. So, I think we need to get used to the idea that we don't really have any privacy unless you are willing to take yourself totally off the grid.
This may seem somewhat depressing, but most of us already act as if we don't care that much about privacy. We supply extensive details about our lives to social networks, and we tell companies they can exploit our data in exchange for a few discounts on merchandise. So, in principle we say we care about privacy, but our actions suggest otherwise.
I do think that companies that guard our personal data and don't exploit it without permission will gain business, so if there is more transparency about personal data policies in the marketplace, it may lead to somewhat more privacy. The best way to avoid personal data exploitation is to stay away from the organizations that do it.