This past week was the beginning of the 2012 Major League Baseball (MLB) season.

Since its data is mostly transaction data describing the statistical events of games played, baseball has long been a sport obsessed with statistics. Baseball statisticians slice and dice every aspect of past games attempting to discover trends that could predict what is likely to happen in future games.

There are too many variables involved in determining which team will win a particular game to be able to choose a single variable that predicts game results. But a few key statistics are cited by baseball analysts as general guidelines of a team’s potential to win.

One such statistic is a quality start, which is defined as a game in which a team’s starting pitcher completes at least six innings and permits no more than three earned runs. Of course, a so-called quality start is no guarantee that the starting pitcher’s team will win the game. But the relative reliability of the statistic to predict a game’s result causes some baseball analysts to refer to a loss suffered by a pitcher in a quality start as a tough loss and a win earned by a pitcher in a non-quality start as a cheap win.

There are too many variables involved in determining if a particular business activity will succeed to be able to choose a single variable that predicts business results. But data quality is one of the general guidelines of an organization’s potential to succeed.

As Henrik Liliendahl Sørensen blogged, organizations are capable of achieving success with their business activities despite bad data quality, which we could call the business equivalent of cheap wins. And organizations are also capable of suffering failure with their business activities despite good data quality, which we could call the business equivalent of tough losses.

So just like a quality start is no guarantee of a win in baseball, good data quality is no guarantee of a success in business.

But perhaps the relative reliability of data quality to predict business results should influence us to at least strive for a quality start to our business activities by starting them off with good data quality, thereby giving our organization a better chance to succeed.

This post originally appeared at OCDQ Blog.

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