Is Poor Quality the Antihero of Data?

As a kid, I enjoyed reading comic books and watching animated television series about superheroes.  Among my favorites were Spider-Man, Batman, Thor, Iron Man, and the X-Men.  Which is why, as a adult, I enjoy the super advancements in cinematic technology that not only powers (mostly) live-action superhero movies, but has also propelled them into mainstream culture.

One of the mainstays of the superhero genre is that the supervillains are usually its most interesting characters, displaying more of a flair for the dramatic (e.g., Joker vs. Batman), striking a more commanding presence (e.g., Magneto vs. Professor X), and spouting off far wittier lines (e.g., Loki vs. Thor — Spider-Man and Robert Downey, Jr.’s Iron Man being exceptional exceptions).

Furthermore, the supervillain is really the story’s focal point, providing the obstacle the superhero must overcome.  No offense intended to the good guys, but without bad guys doing bad things there would be no story worth telling.  After all, no one is interested in a story about your friendly neighborhood Spider-Man swinging on his webs from building to building in Mister Rogers’ Neighborhood.  Just as no one is interested in a story about the night the Dark Knight patrolled the uneventful streets of Gotham City and Police Commissioner Gordon used the Bat Signal to invite the Caped Crusader over for coffee and donuts.

In fact, the critical importance of the supervillain to the superhero story was probably put best when Jack Nicholson’s Joker exclaimed: “Can somebody tell me what kind of a world we live in where a man dressed up as a bat gets all of my press?”

Which is why superhero stories seem eerily similar to data quality.  No one is interested in hearing about all the stalwart nights the Data Steward kept careful watch over the high-quality databases and file systems of Data City.  But as soon as the night erupts with digital explosions and echoes of maniacal laughter, the DQ Signal blazes across a foreboding sky as business leaders cry out for The League of Extraordinary Data Quality Professionals to save them from Poor Quality.  However, I am not certain that that all-too-common, almost clichéd, storyline, which forms the basis of most sales pitches for data quality solutions, makes poor quality the supervillain of data.

Perhaps poor quality is data’s antihero — a central character lacking some of the conventional heroic attributes but nonetheless benefits the greater good, albeit not always by the most noble means possible.  After all, without poor quality there would not be, according to Gartner research, a market for data quality technology projected to produce $2 billion in constant-dollar revenue by 2017.  Which is not to say that all you have to do is throw technology at poor quality data, but it is one of many essential aspects of data quality best practices.  After all, without technology Batman and Iron Man would just be Bruce Wayne and Tony Stark.

Of course technology vendors aren’t the only beneficiaries of poor quality data.  The Arkham Asylum of the data quality industry also has conferences, publications, consultants, analysts, and (ahem) freelance writers that are also benefiting from the fact that poor quality data is not going gentle into that good night anytime soon.

I am not trying to cast anyone as either a supervillain or a superhero.  I am just wondering out loud (in writing) whether or not poor quality is the antihero of data.  What (heroically, villainously, anti-heroically, or otherwise) say you?


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