According to a new report by the Government Accountability Office (GAO), performance information’s effect on Federal managers’ decision-making has remained largely unchanged in six years.
Despite the increase in the amount, variety, and availability of performance data and analytics tools to drive decisions, performance data’s promise in the Federal Government has not yet been realized. There are many theories as to why this is the case, but I would argue that the shift to data-driven decisions in the Federal Government requires disruption of the mental models most commonly used in making decisions.
Despite the Nats’ absence from the World Series this year, Major League Baseball serves as a great example of how disrupting a mental model may enable a leader to improve decision-making and organizational performance. One recent revolution in the baseball world is the now-accepted practice of applying empiricism and analytics to performance data, to drive strategy and tactics. Analytics helps baseball executives find hidden value in player effectiveness and situational game tactics that lead to the ultimate criteria of success: wins on the field.
The rise of this new field — Sabermetrics — pioneered by Bill James, has been well-documented in Michael Lewis’s 2003 book-turned-movie, Moneyball. Embracing this approach — gathering data and performing analysis to determine what skills and behaviors contribute to wins on the field — is one of the things that help teams from smaller markets bring greater competitive parity to the game.
For over a century, baseball talent evaluators relied on conventional wisdom to assess players using generally accepted criteria regarding the “5-Tools” of baseball: running speed, arm strength, hitting for average, hitting for power, and fielding. During a player’s early career, scouts would assess all players on these traditional success criteria. Usually these assessments were made simply by watching the players perform. Little effort was made to systematically gather data in a way that permits players to be compared. Despite these haphazard attempts to measure raw skills, some players who excelled on the 5-Tools metrics were not able to perform under game conditions to produce wins. This indicates that while raw tools were important, they were inadequate as sole predictors of success.
In addition, baseball’s conventional wisdom — informally referred to as “the Book” — around game tactics: when to bunt, steal a base, position the defense for certain hitters, and even make player substitutions has relied on time worn, but not necessarily rigorously tested presumptions about what actually produced wins. By asking the same questions for over a century, baseball scouts and executives relied on consistent criteria, analyzed in the same way, to make player assessments.
Over the last 15 years, more focused measurement, the rise of behavioral economics, and the improved willingness and ability to perform statistical analysis are testing these assumptions and causing new wisdom to be applied to baseball. This approach is yielding greater insights into what skills, behaviors, and tactics lead to team wins. While 15 years ago very few teams would have been aware of or invested in this approach to discovering and benefitting from objective truths about baseball, now every team has staff dedicated to measurement and video and statistical analysis to identify and leverage a winning edge.
By scouts and executives shifting their viewpoint, asking different questions, measuring performance differently, and then performing analysis on the data they measured, they learned to value different things that had been proven to affect performance.
Federal leaders can shift their viewpoint to apply lessons learned from the MLB by:
- Being Prepared for Data to Dispel Beliefs
Every organization has tightly held beliefs that are, in fact, not true. As you shift to data-driven decisions, it’s likely that some organizational myths will be disproven. Individuals may hold on to those beliefs tightly despite strong contrary evidence. Part of your role as the leader is to take your team on the journey of not only questioning data when it is counter to anecdotal evidence, but also accepting the truths the data reveals.
- Assessing Situations and Options Based on Analytics
Just as assessment of a player evolved from visual assessment to quantitative measures, leaders can seek out data to inform assessment of situations and options in decision-making. Ask your team to be prepared to defend their recommendations with analysis results. Ask about the data and analytics they used to inform their recommendation and be sure it’s from a valid source.
- Reexamining the measures of performance
Are the performance measure you use driving achievement of mission objectives? If they aren’t, which measures do? As a leader, the more real-team reliable performance measures you can access, the better you can drive performance. This approach applies both to organizational performance and assessment of individuals. In the case of your team members however, consider whether the competencies on which you assess your team members are the right ones to drive performance. If they aren’t, work with your HR team to change to those that get the job done right.
- Evaluating the Effectiveness of Data-Driven Decisions
As part of your regular hot wash processes, evaluate how effectively your team used analytics in making decisions. Did using analytics result in better decisions than just instinct? How could you change the game with the data you have now? If analytics is not helping to improve performance, determine what can be done differently. Sometimes there is an issue with data quality. Sometimes teams need better analytics skills. Most often, however, it’s a combination of both.
Bottom Line: Measure more intentionally, ask better questions, and you may find that you get different — more insightful — answers that can lead to significantly improved performance in the areas that matter.