To Our Data Perfectionists

Had our organization but money enough, and time,
This demand for Data Perfection would be no crime.

We would sit down and think deep thoughts about all the wonderful ways,
To best model our data and processes, as slowly passes our endless days.
Freed from the Herculean Labors of Data Cleansing, we would sing the rhyme:
“The data will always be entered right, the first time, every time.”

We being exclusively Defect Prevention inclined,
Would only rubies within our perfected data find.
Executive Management would patiently wait for data that’s accurate and complete,
Since with infinite wealth and time, they would never fear the balance sheet.

Our vegetable enterprise data architecture would grow,
Vaster than empires, and more slow.

One hundred years would be spent lavishing deserved praise,
On our brilliant data model, upon which, with wonder, all would gaze.
Two hundred years to adore each and every defect prevention test,
But thirty thousand years to praise Juran, Deming, English, Kaizen, Six Sigma, and all the rest.
An age at least to praise every part of our flawless data quality methodology,
And the last age we would use to write our self-aggrandizing autobiography.

For our Corporate Data Asset deserves this Perfect State,
And we would never dare to love our data at any lower rate.

But at my back I always hear,
Time’s winged chariot hurrying near.

And if we do not address the immediate business needs,
Ignored by us while we were lost down in the data weeds.
Our beautiful enterprise data architecture shall no more be found,
After our Data Perfectionists’ long delay has run our company into the ground.

Because building a better tomorrow at the expense of ignoring today,
Has even with our very best of intentions, caused us to lose our way.
And all our quaint best practices will have turned to dust,
As burnt into ashes will be all of our business users’ trust.

Now, it is true that Zero Defects is a fine and noble goal,
For Manufacturing Quality—YES, but for Data Quality—NO.

We must aspire to a more practical approach, providing a critical business problem solving service,
Improving data quality, not for the sake of our data, but for the fitness of its business purpose.
Instead of focusing on only the bad we have done, forcing us to wear The Scarlet DQ Letter,
Let us focus on the good we are already doing, so from it we can learn how to do even better.

And especially now, while our enterprise-wide collaboration conspires,
To help us grow our Data Governance Maturity beyond just fighting fires.
Therefore, let us implement Defect Prevention wherever and whenever we can,
But also accept that Data Cleansing will always be an essential part of our plan.

Before our organization’s limited money and time are devoured,
Let us make sure that our critical business decisions are empowered.

Let us also realize that since change is the only universal constant,
Real best practices are not cast in stone, but written on parchment.
Because the business uses for our data, as well as our business itself, continues to evolve,
Our data strategy must be adaptation, allowing our dynamic business problems to be solved.

Thus, although it is true that we can never achieve Data Perfection,
We can deliver Business Insight, which always is our true direction.


This blog post was inspired by the poem To His Coy Mistress by Andrew Marvell.


Related Posts

The Dichotomy Paradox, Data Quality and Zero Defects

The Asymptote of Data Quality

Data and its Relationships with Quality

Data Quality and Miracle Exceptions

Data Quality and Chicken Little Syndrome

Data Quality: Quo Vadimus?

Data Myopia and Business Relativity

Plato’s Data

What going to the dentist taught me about data quality

How Data Cleansing Saves Lives

A Tale of Two Q’s

Data Quality and The Middle Way


Related OCDQ Radio Episodes

Clicking on the link will take you to the episode’s blog post:

  • Organizing for Data Quality — Guest Tom Redman (aka the “Data Doc”) discusses how your organization should approach data quality, including his call to action for your role in the data revolution.
  • Redefining Data Quality — Guest Peter Perera discusses his proposed redefinition of data quality, as well as his perspective on the relationship of data quality to master data management and data governance.
  • The Johari Window of Data Quality — Guest Martin Doyle discusses helping people better understand their data and assess its business impacts, not just the negative impacts of bad data quality, but also the positive impacts of good data quality.
  • Studying Data Quality — Guest Gordon Hamilton discusses the key concepts from recommended data quality books, including those which he has implemented in his career as a data quality practitioner.