Measurement, Data Analytics, and Performance Management
Measurement and analytics
Performance management links measures to strategy. A KPI is a small set of measures tied to critical success factors; a metric is any quantified indicator. Leading indicators predict; lagging indicators confirm. The balanced scorecard covers financial, customer, internal process, and learning and growth perspectives. Good measures are specific, measurable, achievable, relevant, and time-bound, with clear ownership and definitions. Data quality dimensions include accuracy, completeness, timeliness, consistency, and validity. Analytics progresses from descriptive to diagnostic, predictive, and prescriptive. Variation matters: common-cause variation is inherent; special-cause variation signals an anomaly. Control charts distinguish them; small samples and cherry-picked periods mislead.
Traps
Common traps: measuring what is easy rather than what matters; using lagging measures alone; gaming targets; confusing accuracy with precision; treating correlation as causation; and comparing dashboards with different definitions. Scenario questions often ask why a KPI improved while outcomes worsened. Look for suboptimization or distortion.
Worked example
A call center rewards short calls. Average handle time falls, but repeat calls double. The KPI drove unintended behavior. Add first-contact resolution and customer effort, define them precisely, and review the balanced set monthly.
Rule of thumb: every measure should have a known counter-measure to prevent gaming.