While I read these news snippets with the intent of escaping the exciting world of data management for just a brief moment, I couldn’t escape for long because both sides of the table bring up reams of data to defend their positions.
According to the media reports and analysis, the Yankees' ownership is seemingly paying less attention to Jeter’s Hall of Fame-worthy career statistics, including a fantastic 2009 season, and his intrinsic value to the Yankees brand, but instead is focusing on Jeter’s arguably career-low 2010 season on-field performance and advancing age (36 years old is practically Medicare-eligible age in baseball).
Jeter’s side of the negotiations, on the other hand, point out that Jeter’s value to the Yankees is “immeasurable,” and that one off year shouldn’t be used to define his value to the team. They point out that Jeter, as team captain, is a major leader in the clubhouse and excellent role model for younger players. He’s certainly among the most popular players the Yankees employ and influences boatloads of fans to attend games, watch the Yankees cable network, and provide significant licensing revenue. And of course they point out that Jeter is still an excellent player and 2010 should be viewed as an anomaly, not the norm.
I’m not a baseball analyst (lucky for everyone), and I have no intention in joining the debate on whose point of view is correct or how much Jeter should earn, for how many years, etc. (That’s best discussed over a few beers, not on a blog, right?)
But I do think this situation poses a great example of a mission-critical business decision that has to be made with the most effective use of trusted data and performance management metrics to ensure that the business remains competitive while managing its limited resources effectively. (Okay, the Yanks' resources are not as “limited” as the next team's, I know. Work with me here, I’m getting to my point!)
What’s interesting from a data management standpoint is that everyone has access to much of the same data (e.g., batting stats, fielding stats, post-season contribution, licensing revenue on “Derek Jeter” merchandise … not to mention career stats on practically every major league shortstop who has ever played the game), and unlike most enterprise data, everyone actually agrees that this data itself is accurate and trustworthy. What they can’t agree on is how to apply this trusted data to effectively evaluate past performance, predict future performance, and determine ROI contribution to the organization. Sound familiar?
Large organizations invest heavily in human resource management apps that are meant to help measure employee performance management, business performance solutions that measure financial performance, and business intelligence and predictive analytics solutions that are meant to provide business insights into past trends and high confidence models to anticipate future events and needs. But none of these investments will deliver on their promise if business process owners and strategic decision-makers can’t agree on what needs to be measured, how often, and how to leverage the results of this analysis to optimize their business and deliver the most value to the organization.
The cynic in me just views these back-and-forth negotiations as further proof that a truism told to me by a manager early in my career still rings true: “Statistics lie, and liars use statistics.” His (half-joking) point was that anyone can take raw data and make it support a story he wants to sell. But in my subsequent professional experience, I recognize that a well-governed data management, business intelligence, and performance management set of competencies, best practices, and tools can deliver significant value and insight to senior management to truly differentiate an organization from its competitors and provide positive business outcomes. The challenge is always going to be getting these efforts aligned.
Of course, data epiphanies aside, if I see Derek Jeter in another uniform next year I might have to start watching competitive basket-weaving to make up for the free time I’ll gain by dropping the Yanks like a bad habit. ;-)
Why worry about managing data. Just do what the x-purts do. Manufacture data. Since you can't rely of data, just create your own! Here's some real data.
There is a 99% probability Derek Jeter will be discovered using "performance reducing additives". How does that affect his ROI?
By the way, the "performance reducing additives" are the aging process!