Day 1: From Objective to Measurement Logic

Defining the Result to Measure

• Distinguish objective result activity and output
• Identify who benefits and what changes
• Write results with direction and time
• Improve objectives that are to measure

Results Tree and Performance Drivers

• Connect outputs behaviors capabilities and results
• Identify factors management can influence
• Expose indicators disconnected from claimed results
• Build measurement tree for one objective

Selecting Meaningful Indicators

• Test relevance interpretability and measurement cost
• Avoid metrics encouraging counterproductive behavior
• Use multiple measures for multidimensional quality
• Review an overloaded KPI list

Day 2: KPI Definition, Data, and Measurement Quality

KPI Definition Card

• Specify formula unit source and frequency
• Define inclusions exclusions and data responsibility
• Document calculation assumptions and rounding rules
• Complete a full KPI definition card

Data Quality and Validation

• Assess completeness accuracy timeliness and consistency
• Identify transformation points changing results
• Reconcile with an independent source
• Resolve KPI discrepancies across reports

Baseline and Target Setting

• Choose baseline period representing normal performance
• Set capacity-based targets for planned change
• Use ranges/trajectories in volatile environments
• Develop a staged annual target

Day 3: Thresholds, Dashboards, and Performance Visualization

Performance Limits and Alerts

• Define acceptable ranges before status colors
• Connect thresholds with actions/escalation levels
• Avoid colors exaggerating immaterial variance
• Set thresholds for several indicator types

Balanced Dashboard Design

• Use limited decision-relevant indicator set
• Show trend target and explanation together
• Organize dashboards by results over departments
• Redesign a dense dashboard

Leading and Lagging Measures

• Select signals preceding final outcomes
• Connect leading indicators with testable logic
• Avoid rewarding easily manipulated measures
• Build one leading-lagging indicator pair

Day 4: Variance Analysis and Performance Review

Trend and Variation Interpretation

• Compare performance with baseline/target path
• Distinguish temporary deviation from pattern change
• Connect data with relevant operational context
• Analyze time series for one KPI

Explaining Causes without Excuses

• Gather process evidence before assuming cause
• Use questions that reveal system factors
• Connect causes with testable actions
• Build an analysis for recurring deviation

Leadership Performance Review

• Present exceptions and decisions first
• Assign action owners and closure dates
• Prevent reviews becoming data presentations
• Simulate a monthly performance review

Day 5: Performance Governance and System Design Laboratory

KPI Ownership and Governance

• Distinguish performance owner from data owner
• Control KPI definition changes through approval
• Set formal update and review calendars
• Build a KPI governance register

Reviewing KPI Relevance

• Retire measures no longer supporting decisions
• Change definitions when operating models shift
• Assess behavioral effects created by indicators
• Review an annual KPI portfolio

Performance System Laboratory

• Build results indicators and definition cards
• Set targets thresholds and monitoring dashboards
• Run review meeting using changing data
• Approve measurement package ready for use

• Results-tree workshops linking objectives with measurable drivers
• KPI definition cards separating metric logic from presentation
• Data trend and variance analysis across realistic cases
• A dashboard and performance-review laboratory ending in explicit actions