For truly comic relief, there is perhaps no better resource than Scott Adams and the Dilbert comic strip.
Data Quality: A Tail of Two Rabbits
Since this recent tweet of mine understandably caused a little bit of confusion in the Twitterverse, let me attempt to explain.
In my recent blog post Who Framed Data Entry?, I investigated that triangle of trouble otherwise known as data, data entry, and data quality, where I explained that although high quality data can be a very powerful thing, since it’s a corporate asset that serves as a solid foundation for business success, sometimes in life, when making a critical business decision, what appears to be bad data is the only data we have—and one of the most commonly cited root causes of bad data is the data entered by people.
However, as my good friend Phil Simon facetiously commented, “there’s no such thing as a people-related data quality issue.”
And, as always, Phil is right. All data quality issues are caused—not by people—but instead, by one of the following two rabbits:
Roger is the data quality trickster with the overactive sense of humor, which can easily handcuff a data quality initiative because he’s always joking around, always talking or tweeting or blogging or surfing the web. Roger seems like he’s always distracted. He never seems focused on what he’s supposed to be doing. He never seems to take anything about data quality seriously at all.
Well, I guess th-th-th-that’s all to be expected folks—after all, Roger is a cartoon rabbit, and you know how looney ‘toons can be.
As for Harvey, well, he’s a rabbit of few words, but he takes data quality seriously—he’s a bit of a perfectionist about it, actually. Harvey is also a giant invisible rabbit who is six feet tall—well, six feet, three and a half inches tall, to be complete and accurate.
Harvey and I sit in bars . . . have a drink or two . . . play the jukebox. And soon, all the other so-called data quality practitioners turn toward us and smile. And they’re saying, “We don’t know anything about your data, mister, but you’re a very nice fella.”
Harvey and I warm ourselves in these golden moments. We’ve entered a bar as lonely strangers without any friends . . . but then we have new friends . . . and they sit with us . . . and they drink with us . . . and they talk to us about their data quality problems.
They tell us about big terrible things they’ve done to data and big wonderful things they’ll do with their new data quality tools.
They tell us all about their data hopes and their data regrets, and they tell us all about their golden copies and their data defects. All very large, because nobody ever brings anything small into a data quality discussion at a bar. And then I introduce them to Harvey . . . and he’s bigger and grander than anything that anybody’s data quality tool has ever done for me or my data.
And when they leave . . . they leave impressed. Now, it’s true . . . yes, it’s true that the same people seldom come back, but that’s just data quality envy . . . there’s a little bit of data quality envy in even the very best of us so-called data quality practitioners.
Well, thank you Harvey! I always enjoy your company too.
But, you know Harvey, maybe Roger has a point after all. Maybe the most important thing is to always maintain our sense of humor about data quality. Like Roger always says—yes, Harvey, Roger always says because Roger never shuts up—Roger says:
“A laugh can be a very powerful thing. Why, sometimes in life, it’s the only weapon we have.”
Really great non-rabbits to follow on Twitter
Since this blog post was published on a Friday, which for Twitter users like me means it’s FollowFriday, I would like to conclude by providing a brief list of some really great non-rabbits to follow on Twitter.
(Please Note: This is by no means a comprehensive list, is listed in no particular order whatsoever, and no offense is intended to any of my tweeps not listed below. I hope that everyone has a great #FollowFriday and an even greater weekend.)