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    Big Data: If you can save 1% your are doing well - if you can save 30% run away! Response to Bloomberg’s Leonid Bershidsky

    Eugen G Tarnow  June 2 2016 09:10:03 AM
    By Eugen Tarnow, Ph.D.
    Avalon Business Systems, Inc.
    http://AvalonAnalytics.com

    My experience in big data has taught me one piece of "big data philosophy": if you can save 1% or so, you are doing great.  If you see a way to save 30% - run away as quick as you can!

    Before I explain the reason for this philosophy, let's use an editorial in Bloomberg by Leonid Bershidsky as background.  The title is "Big Data Is Still Only a Little Helpful and is quoting an article by Hyunyoung Choi and Hal Varian.

    Lenoid writes that trends in Google queries can predict the number of current autosales before official numbers are made available.  In other words, because Google calculates auto query reports much faster than official reports are put together on actual sales, the Google data can be helpful.  The predictions can be improved by 5-20% according to Choi and Varian but they are looking at second order differences.  The forecast without Google may be 6.34% and with Google trends 5.66% giving an improvement of 10.6% but the actual difference is less than one percent.

    If we consider the Choi and Varian improvement to be a 1% improvement, in my book they did a great job. People are pretty good at what they are doing and using a computer can make small improvements but it is hard to think of ways it can make a big difference on problems that have been studied many times over.

    Or can it?

    Sometimes it is easy to find much larger improvements using big data.  For example, I found that provider Medicare reimbursements are too high and they increase list prices by an average of 45%, that Medicare payments to hospitals for heart attacks could be lowered by 37% without effecting the quality of care.

    Why didn't Leonid Bershidsky write about these results instead and change the title to "Big Data Can Save 40%"?

    That is because if your big data algorithm finds such a large number it is likely it has encountered systemic corruption.

    Finding 30% in fields such as health care is like shooting fish in a barrel.  But then run away unless you are big data's equivalent of Dirty Harry.
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