Tuesday, September 30, 2014

Lost Precision in Dataminea

Things are looking good, they say... Our data is getting more structured, and data mining has become a standard working tool in the business toolbox. We can analyse our customers, their behavior, the markets in which we operate and our own supply-chain environment.

With all this apparent maturity - you would think we are in a good place.

Initially we gave away a lot of our data without realizing it. Now, at least, we are aware of what data we share and kind-of how it is used. It sounds sensible and fair and for the most part of it - this is true.

The danger we have opened ourselves to, however, is an increased sensitivity to information misconceptions. While we may have increased our precision in representing data, we have insufficient tools to control its accuracy.

Before going further, I think it is important to highlight the distinction between the two. Precision, refers to the ability to generate results which are consistent and repeatable - meaning that our tool is reliable in generating the same result over and over again. Accuracy, on the other hand, is how close the measurement is to the actual truth.

Now as I have noted in the past, the truth can be perceived from different points of view, and while we may be able to generate more reliable results, they tend to serve a limited set of perspectives. This is no accident, as these views are used to satisfy specific measures and drive  specific behavior. This is not new. Politicians, advertisers and a lot of other groups and individuals continue to use this ability to distort the view on certain realities and create an arbitrage in opinion to their advantage.

Some may call this the art of doing business, and perhaps that is what it is.

The bottom line is, however, that with all this Dataminea going on around us, we are becoming much more sensitive to data miss-representation which can be a good or bad thing - depending what you are trying to achieve, and how you manage your data.

My question to you is: do you understand the perspectives and the level of precision of the data you handle?

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