Data Quality and Miracle Exceptions
Jim Harris in Books, Data Quality, Debates, Methodology, Technology tagged Best of 2012, Data Governance, Philosophy, Rant Thursday, March 1, 2012 at 3:00AM “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.
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Reader Comments (4)
I guess that pretty much says it all. Nice job, Jim!
Nicely delineated argument, Jim. Successfully starting a data quality program seems to be a balance between getting started somewhere and determining where best to start. The data quality problem is like a two-edged sword without a handle that is inflicting the "death of a thousand cuts". Data quality is indeed difficult to get "a handle on".
Thanks for your comments, Bryan and Gordon.
From the LinkedIn Group for the IAIDQ Professional Open Community, Richard Jarvis commented:
“Well said Jim. I've often referred to it as the silver bullet approach to management, since what you're describing isn't isolated to Data Quality (DQ).
Once the business accepts there is a problem that needs to be solved (that poor data quality is negatively impacting the bottom line both directly and indirectly), then it's fair to say vendors are preying on the business' lack of knowledge of what can be done about it. It's also fair to say businesses are praying (pardon the pun!) that the black magic invoked by putting the vendor's DVD into a drive will miraculously fix the problem.
Needless to say, as in most negotiations where information asymmetry plays a significant part, it's better to be the seller than the buyer!
Knowing what I do now, it's clear that hoping a static action or product can solve an ongoing, continuous management problem is pure fantasy. The silver bullet simply doesn't exist.
As you've said before though, there is still an ordered approach to permanently addressing DQ issues which maximizes your chance of success.
Vala Afshar posted this on the #BIWisdom discussion group late last year, and I believe it's just as true here: "Culture of transparency first, accountable employees to cultivate and then the necessary tools. A customer focused culture is key"
Vala has correctly outlined the culture > accountability > tools hierarchy. I like this because it recognizes that effective use of a tool (DQ in this case) will be the natural outcome of establishing responsibility, which in turn is the natural outcome of a cultural shift.
As much as vendors (and most managers...) want you to believe they have a way of pushing the water back up the hill, it requires so many buckets and mops it's almost certainly doomed to fail.”
And I responded:
Thanks for your excellent comment, Richard.
As I have previously blogged, data management has its own form of lycanthropy, wherein although it is the humans who are bitten, they are not the ones who transform. Enterprise information initiatives (e.g., MDM) are often transformed into a legendary beast when their implementation complexity is revealed (perhaps by the light of a full moon).
When we allow these initiatives to morph into a monster, we start believing in the magic of finding a legendary technology, we start believing a silver bullet is the only thing that can save us.
Vala’s equation — Culture > Accountability > Tools — definitely puts the essential role of technology in its proper place.
Best Regards,
Jim
And Garry Ure responded:
“Great post Jim, I know exactly what you mean. As a data quality practitioner I often find myself being expected by a client to 'just fix the data' as if it were that easy or indeed I had some kind of magical superpower. You can see the disappointment in people's faces as you explain the various components that need to be considered and the work that needs to be undertaken. It's as though they transition through the various stages of grief before your eyes!”
And I responded:
Thanks for your comment, Garry.
How about this as DABDA for DQ:
Denial — Our business is doing fine. Data quality issues can’t be happening to us.
Anger — Why do we have data quality issues? How can this be happening to us? Who is to blame?
Bargaining — I’ll approve a data cleansing project. That will fix all of our data quality issues, right?
Depression — We keep having data quality issues, so perhaps we shouldn’t bother doing anything at all about data quality.
Acceptance — I can’t fight the truth anymore, we need to do the hard daily work of continuously improving our data quality.
And from the LinkedIn Group for Data Governance & Data Quality, John Flaxman commented:
“I agree with your assessment. For a company, data quality is definitely not a one trick pony and folks should never expect that they will achieve a 100% solution. It requires all of the elements you mentioned but based on the specific data quality issues to be solved. There is no one size fits all set of solutions and no magic bullet!”
As usual, a good insight. From our experience, business tend to be guided by the vested interests of the particular sales pitch. Consulting-driven sales people will punt methodology and hope to sell a lot of bodies. Similarly, the principle that just dropping a tool will resolve the issue is equally misguided.
Any sensible approach must combine methodology and experience with appropriate technology in order to enable a sustainable, ongoing data quality capability within the organization.
At one client we were pulled into the process right at the end of the evaluation process. They had already budgeted to pull in a large team of SQL programmers to analyze and clean their data. We were able to show that we could achieve the objective with two consultants using a tested methodology and a data quality platform at a fraction of the cost of the alternative.
One other point - the requirement for the project was "to clean our data" - with very little understanding initially as to what this meant. Experience meant that we were able to manage expectations and guide the client through an initial data audit - we were fortunate that they were reasonable and adapted to reality.
The biggest challenge that is always faced is that you cannot make firm commitments as to scope and cost before you have profiled the data and linked the results to the actual business need.