Big Data and the Rise of Asymmetrical Analytics

The McKinsey Global Institute just issued their report Big data: The next frontier for innovation, competition, and productivity.  It’s worth the time to read and act on (not think about).  Now.

The McKinsey analysis looks at five sectors in some detail–health care in the U.S., the public sector in Europe, retail in the U.S. and manufacturing and personal location data globally.  In each of those areas, the ability to use the “data exhaust” to create value is stunning.  One such example is the assertion that more effective use of the big data sets created by the health care industry will result in 8% savings annually in the United States, or $300 billion in efficiencies.

So what’s changed?

First, we now we have the ability to capture data and store it (via inexpensive storage technology) and actually sort through the data (via new tools like Hadoop).  Second, the world has changed as we morph from a world of “data asymmetry” to a world of “analytic asymmetry.”

In the past, the data itself was hard to collect, store and disseminate.  That led to the rise of information services companies like Experian.  Get the data, sort it, store it (on big iron) and license it to others.  Companies that had more data than the competition won, even if they used it in relatively crude fashion. For example, in paper-based direct response marketing, the old RFM (recency, frequency, monetary value) segmentation schemes were startlingly effective.  All you needed was access to purchase information in a reasonably timely fashion and you could do well against the competition.

Now, information is relatively easy to collect and disseminate.  It becomes so much “exhaust.”  The McKinsey report says that Facebook users create 30 billion bits of data a month.  Twitter also creates mountains of data, as do other companies.  And a lot of it is now available via API in either free or so cheap a form as to be effectively free.

So if the data is out there and available, what’s the competitive advantage?  It’s in making sense of it. Asking the right questions to avoid being bogged down is important.  Having the tools to quickly make sense of the data is critical to beating the competition to the punch–who will, ultimately, be working from more-or-less the same data sets.

What should you do?  First, decide if you buy in.  Assuming you do (and reasonable people may have different opinions, which I’d love to discuss) then you’ve got to figure out how to create transparency into the data, build the algorithms and start building new products and services using that data. Create “asymmetrical analytics” differentiation between you and the competition.  And win.

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