“Reading superhero comic books with the benefit of a Ph.D. in physics,” James Kakalios explained in “The Physics of Superheroes,” “I have found many examples of the correct description and application of physics concepts. Of course, the use of superpowers themselves involves direct violations of the known laws of physics, requiring a deliberate and willful suspension of disbelief.”
“However, many comics need only a single miracle exception — one extraordinary thing you have to buy into — and the rest that follows as the hero and the villain square off would be consistent with the principles of science.”
“Data Quality is All About . . .”
It is essential to foster a marketplace of ideas about data quality in which a diversity of viewpoints is freely shared without bias, where everyone is invited to get involved in discussions and debates and have an opportunity to hear what others have to offer.
However, one of my biggest pet peeves about the data quality industry is when I listen to analysts, vendors, consultants, and other practitioners discuss data quality challenges, I am often required to make a miracle exception for data quality. In other words, I am given one extraordinary thing I have to buy into in order to be willing to buy their solution to all of my data quality problems.
These superhero comic book style stories usually open with a miracle exception telling me that “data quality is all about . . .”
Sometimes, the miracle exception is purchasing technology from the right magic quadrant. Other times, the miracle exception is either following a comprehensive framework, or following the right methodology from the right expert within the right discipline (e.g., data modeling, business process management, information quality management, agile development, data governance, etc.).
But I am especially irritated by individuals who bash vendors for selling allegedly only reactive data cleansing tools, while selling their allegedly only proactive defect prevention methodology, as if we could avoid cleaning up the existing data quality issues, or we could shut down and restart our organizations, so that before another single datum is created or business activity is executed, everyone could learn how to “do things the right way” so that “the data will always be entered right, the first time, every time.”
Although these and other miracle exceptions do correctly describe the application of data quality concepts in isolation, by doing so, they also oversimplify the multifaceted complexity of data quality, requiring a deliberate and willful suspension of disbelief.
Miracle exceptions certainly make for more entertaining stories and more effective sales pitches, but oversimplifying complexity for the purposes of explaining your approach, or, even worse and sadly more common, preaching at people that your approach definitively solves their data quality problems, is nothing less than applying the principle of deus ex machina to data quality.
Data Quality and Deus ex Machina
Deus ex machina is a plot device whereby a seemingly unsolvable problem is suddenly and abruptly solved with the contrived and unexpected intervention of some new event, character, ability, or object.
This technique is often used in the marketing of data quality software and services, where the problem of poor data quality can seemingly be solved by a new event (e.g., creating a data governance council), a new character (e.g., hiring an expert consultant), a new ability (e.g., aligning data quality metrics with business insight), or a new object (e.g., purchasing a new data quality tool).
Now, don’t get me wrong. I do believe various technologies and methodologies from numerous disciplines, as well as several core principles (e.g., communication, collaboration, and change management) are all important variables in the data quality equation, but I don’t believe that any particular variable can be taken in isolation and deified as the God Particle of data quality physics.
Data Quality is Not about One Extraordinary Thing
Data quality isn’t all about technology, nor is it all about methodology. And data quality isn’t all about data cleansing, nor is it all about defect prevention. Data quality is not about only one thing — no matter how extraordinary any one of its things may seem.
Battling the dark forces of poor data quality doesn’t require any superpowers, but it does require doing the hard daily work of continuously improving your data quality. Data quality does not have a miracle exception, so please stop believing in one.
And for the love of high-quality data everywhere, please stop trying to sell us one.
This post originally appeared at OCDQ Blog.
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