One of the great things about teaching people about the applications of business intelligence and analytics is that you start to see just how much the world around us is driven by it.
We take massive amounts of disparate data and condense and interpret them into usable statistics and figures every day.
Take, for example, a credit score. These digestible numbers are actually the product of extensive calculations, incorporating metrics around the amount of credit available to you, what percentage of credit available to you is used, and existing financial obligations. All of that information comes from disparate sources that are processed and compiled into one simple figure.
Or consider baseball. Many teams in baseball use complex statistics, called sabermetrics, to evaluate a player’s suitability in their squad. These figures, which are created from complex analyses of massive amounts of player performance data, indicate the sort of luck a player has, how much of the field he can cover defensively, or the projected number of wins he’ll give a team compared to a “league average” player. The data mining that goes into these figures is extensive, and the algorithms that process the final numbers are proprietary. Hundreds of millions of dollars are won and lost on the usefulness of these analytics.
Not all analytics and business intelligence work perfectly, though. The Dow Jones Industrial Average, for example, is a well-meaning attempt to survey the financial health of the U.S. economy by glancing at an average of several “bellwether” stocks. The number is easily understood, but it’s not a good indicator of anything. It’s a great example of analytics run amok, actually: any statistician would tell you that the sample size (30 companies) is far too small to be useful, and any economist would tell you that the range of industries is too narrow to be useful. So while it’s an analytic number that everybody can comprehend, it’s not actually a good analysis.
In my last blog, I wrote that computers are smart and we should let them be smart. But don’t forget that they’re only as smart as we make them. We, as analytical minds, need to look at what we’re asking the computer to do and ensure that it’s what we actually want it to do.
Business intelligence and analytics are everywhere, once you know how to look. We mine data, process data, and interpret data every day, and that’s essentially what business intelligence and analytics is, only with the assistance of powerful software that does the heavy lifting for us.
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