DQ-View: Baseball and Data Quality

Data Quality (DQ) View is an OCDQ regular segment.  Each DQ-View is a brief video discussion of a data quality key concept.

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Comments

[Jim Harris]

From the LinkedIn Group for the IAIDQ Data Quality Professional Open Community, Mike Meier commented:

"I liked this. I wonder about the viability of measuring outcomes of decisions since it is nearly impossible to get anyone on record as to a decision. Baseball managers are perhaps the most exposed of all decision makers. The manager may consult with, for example, the pitching coach and may, in the privacy of the dugout, defer to the pitching coach's recommendation but as far as anyone watching is concerned, the decision is owned by the manager. I can't think of [m]any business situations in which the accountability is so clear. The idea is sound. Let's find a way to make it work."

And I responded:

Excellent point, Mike.

Yes, the transparency and accountability of the manager's decision in baseball is very different than what we typically see in the business world, where the only business decisions that usually go on record are the successes, and with the fear of regulatory compliance failures, perhaps that might not change any time soon.

However, perhaps a potential starting point is focusing on the successful decisions.

I am always interested in correlating data quality and decision outcomes, so for the successful business decisions, I like to note what the data quality metrics were, i.e., was the decision successful when the data quality was near perfect--or were there data quality issues that didn't negatively impact a successful business decision outcome.

Of course, this would only be a correlation, and not a causation, but still it would be good to have some correlative evidence to point to for making the case for setting effective data quality thresholds.

And having the data quality correlation for failed business decisions might help make an effective business case for making data quality improvements and instituting data quality controls.

Best Regards,

Jim