Day 1: Designing Indicators from Result to Definition

Results Before Numbers

• Distinguish activity output and outcome
• Define change for customer or process
• Remove indicators disconnected from objectives
• Write measurable results for practical examples

Selecting the KPI

• Test relevance interpretability and controllability
• Balance quality time and cost constraints
• Avoid cosmetically improved measures
• Select KPIs for three different results

Indicator Definition Card

• Document formula, unit, source, update frequency
• Define exclusions and data ownership
• Clarify performance direction and application scope
• Prepare a complete KPI definition card

Day 2: Data, Targets, and Performance Dashboard

Managing Data Quality

• Test accuracy completeness timeliness and consistency
• Reconcile figures with independent sources
• Assign responsibility for correcting source data
• Analyze conflicting values for same indicator

Performance Baseline and Improvement Target

• Choose representative and fair baseline period
• Set targets using capability/planned change
• Use milestones over unrealistic annual jumps
• Build target trajectory for one indicator

Performance Dashboard Design

• Show value target trend and variance
• Remove indicators not requiring decisions
• Organize views around results, not units
• Redesign report page around trend/cause

Day 3: Variance Analysis and Review Operation

Reading the Trend

• Compare several periods over one snapshot
• Identify seasonal versus sustained change
• Detect improvement caused by definition changes
• Analyze a performance time series

From Variance to Cause

• Ask about process resources and demand
• Return to detailed data before judgment
• Identify testable causes through action
• Prepare a concise variance analysis

Action and Decision Follow-Up

• Assign improvement actions owners and deadlines
• Link action with indicator and result
• Review action effects next measurement cycle
• Simulate short review ending in accountability

• KPI definition cards built from realistic performance results
• Data quality target and trend exercises
• Redesign of crowded performance dashboards
• A performance review simulation connecting measures with action