Since it’s a beautiful time of year for baseball metaphors, let’s post two! (My apologies to Ernie Banks.)
If good data quality gives our organization a better chance to succeed, then it seems logical to assume that perfect data quality would give our organization the best chance to succeed. However, as Yogi Berra said: “If the world were perfect, it wouldn’t be.”
My previous baseball metaphor was based on a statistic that measured how well a starting pitcher performs during a game. The best possible performance of a starting pitcher is called a perfect game, when nine innings are perfectly completed by retiring the minimum of 27 opposing batters without allowing any hits, walks, hit batsmen, or batters reaching base due to a fielding error.
Although a lot of buzz is generated when a pitcher gets close to pitching a perfect game (e.g., usually after five perfect innings, it’s all the game’s announcers will talk about), during the 143 years of Major League Baseball history, during which approximately 200,000 games have been played, there have been only 20 perfect games, making it one of the rarest statistical events in baseball.
When a pitcher loses the chance of pitching a perfect game, does his team forfeit the game? No, of course not. Because the pitcher’s goal is not pitching perfectly. The pitcher’s (and every other player’s) goal is helping the team win the game.
This is why I have never been a fan of anyone who is pitching perfect data quality, i.e., anyone advocating data perfection as the organization’s goal. The organization’s goal is business success. Data quality has a role to play, but claiming business success is impossible without having perfect data quality is like claiming winning in baseball is impossible without pitching a perfect game.
This post originally appeared at OCDQ Blog.
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