How Does Your Talent Help You Win?

Before you start deciding what investments to make in your talent, it helps to understand as best you can how your talent can actually help you win. Without at least some understanding of this, you have reduced your ability to know where or how much to invest, which leads at the very least to opportunity costs that put you at a competitive disadvantage. However, relying strictly on conventional wisdom and/or intuition, while perhaps giving the illusion of understanding, doesn’t substitute for what “Hard Facts, Dangerous Half-Truths And Total Nonsense: Profiting From Evidence-Based Management” authors Pfeffer and Bob Sutton describe as “evidence-based management.” They point out fallacies in much of the conventional wisdom around how talent affects organizational performance. In addition, Michael Lewis’ “Moneyball: The Art of Winning an Unfair Game“, showed how your opponents’ mistaken conventional wisdom about what helps you win could be used to your advantage if you have a better model.

“Moneyball” showed how current decision makers in baseball still relied on measurements that were developed under different circumstances and were biased by the subjectivity of the statistics gatherers at the time. These measurements resulted in a distorted view of how player talent could contribute to winning games. For instance, the venerable “Batting Average” measure was developed under the assumption that if a batter was walked, it was solely the pitcher’s fault, and therefore the batter was not credited for getting on base, even when the result was just as good as if they had a base hit. It turns out that a batter’s ability to hit influences whether they are more likely to walk. In the end, there is a chain of effect between getting on base (particularly so in not getting out), which leads to scoring runs, which leads to winning games. An “on base” measure was shown to be statistically more able to predict winning games. Finding and developing that talent would therefore contribute to winning more games.

Another example was the conventional wisdom that stolen bases and sacrifice plays were key to winning games, but the statistics showed otherwise. Here, the statistics also point to how the conventional wisdom might have gone off track. Both stolen bases and sacrifice plays are ways to execute strategy that seemed to make sense intuitively (advance runners into a scoring position), but the cost in number of outs turned out to be a losing proposition in the long run. In comparing a “winning games” equation based on conventional wisdom vs. one derived through regression analysis, it’s as if the “weights” for stolen bases and sacrifice plays were overvalued and the costs of outs undervalued in predicting wins. The conclusion: perhaps talent at stealing bases is overvalued and therefore not something to seek or invest in as much.

How does this lesson translate to other organizations? There are many instances where, just as in baseball, companies substitute conventional wisdom or intuition for hard facts when it comes to how talent helps them win. In many cases, this is not only an opportunity cost for the company in missing out on a better way, but it can have a damaging effect in the long term. In their book, Pfeffer and Sutton question some of the reasoning behind the conventional wisdom regarding talent. In many cases, these “half-truths” can obscure how your talent is really affecting your strategic success. In the end, it’s best summed up in that because successful execution of strategy is a complex thing to fully understand, with a complex interaction between individuals and systems, there is a natural tendency to look for shortcuts; to do what the latest fad, group consensus, or conventional wisdom says to do. That’s not to say it’s necessarily wrong, but without it being tested, you don’t know if it’s right for your company, either.

So instead of just following the herd, work at getting a true understanding of how your strategy’s execution is affected by your talent and organization capabilities. While this work is challenging due to the complexities involved, there are ways to approach it intelligently. To sum up how “Beyond HR: The New Science of Human Capital” authors Boudreau and Ramstad put it, if you have a real strategy, it’s about how you are different and therefore, your success in executing that strategy hinges on whether your talent has the required unique capability. This insight allows you to focus on what really matters most instead of having to spread your attention and analysis across all the capabilities you are willing to cede as being basically the same as your competition. With that focus, you can then apply HR and financial analytics to mine performance and profile data for patterns. Then, you can develop and confirm your hypotheses as to how those pivotal roles in the organization and the pivotal competencies within those roles result in strategic success. While it covers mostly analytics in areas such as finance, manufacturing, customer relationship, etc., “Competing on Analytics: The New Science of Winning” by Davenport and Harris, describes how beyond professional sports, businesses are now making use of HR analytics to gain a competitive advantage, especially in talent management.

The continual testing and development of the analytical model of how your talent helps you win is key. A big mistake, much like the blind use of conventional wisdom, is the misapplying of analytics by blindly following a model that was built once to every situation going forward. In fact, a huge opportunity is being overlooked by not always reevaluating the results, restrictions, accuracy of prediction, etc. of the model. Doing so is a way to further increase your understanding of how your talent helps you win by causing you to reconsider variables and how they interact. As author Ian Ayres describes it in “Super Crunchers: Why Thinking-by-Numbers Is the New Way to Be Smart“, it’s the interplay of intuition in the form of testable hypotheses, and the incredible analytical power we now have at hand with huge, interconnected datasets to test them, that result in competitive advantage. Intuition by itself is too susceptible to human errors of bias and perception, while mindless number crunching is scattershot and too likely to miss what really matters.