People Analytics: Performance Assessment Fundamentals
Can leadership be boiled into a few measurable attributes? To answer this and other longstanding questions, the field of People Analytics looks at personnel management decisions through quantitative models, bringing scientific precision to organizational functions like performance assessment.
For an American public-sector organization experiencing an ever-increasing pace of technological and political change, People Analytics offers mission-critical insights in how to best recognize and promote exceptional Government leaders.
The cardinal challenge of performance assessment is separating skill from luck. Human Resources (HR) offices use performance metrics to evaluate how well employees are working, but these do not perfectly align to talent or work ethic. Situational factors beyond an employee’s control are always present, wedging a gap between ability and outcomes. When a talented and hard-working employee undertakes a project, a range of outcomes are possible – most positive, but some neutral or negative.
To bridge the gap, Government can take advantage of the statistical phenomenon regression to the mean. The phenomenon shows that following an extreme random event, the next random event is likely to be less extreme. In a performance assessment context, events dependent on luck (random) regress to the mean across performance periods, but events based in skill (non-random) persist.
Performance metrics that effectively measure skill show little regression to the mean. For example, when an agency like the Transportation Security Administration (TSA) assesses leaders on a skill-based metric, the top 5% of performers in 2016 will on average remain near the top in 2017. If the average rating of the 2016 top performers drops to the 50% percentile in 2017, TSA needs to rethink this metric. The dramatic regression to the mean indicates that the metric reveals more about situational factors than leaders’ inherent abilities.
Finding persistent, skill-based metrics usually requires pivoting from final products to process steps. Government should measure both what is accomplished and how it is accomplished. This is done by working backwards from final products to define process steps and using analytics to determine which of these drive mission value and pass the persistence test.
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