Day 1: From Service Standards to Useful Measures

Defining the Service Standard

• Describe service result in observable terms
• Specify time quality and completion requirements
• Identify acceptable variation where relevant
• Test whether staff interpret standard consistently

Choosing Performance Indicators

• Select measures reflecting the actual standard
• Balance speed with quality/resolution
• Avoid metrics that reward undesirable shortcuts
• Build indicator for one service outcome

Baseline and Target

• Use representative historical evidence
• Document the starting performance level
• Set targets reflecting service capability/priority
• Explain why the target is credible

Day 2: Data Quality, Variance, and Interpretation

Data Definition and Ownership

• Define exactly how measure is calculated
• Identify system or team owning source
• Set update timing and validation responsibility
• Record known data limitations

Understanding Variance Patterns

• Compare units/periods/service types appropriately
• Distinguish normal movement from meaningful deviation
• Look beyond averages to spread/outliers
• Identify questions requiring operational investigation

Conducting Cause-Oriented Review

• Connect deviation with demand/staffing changes
• Avoid assigning blame from indicator alone
• Collect supporting evidence before intervention
• Form testable explanation for the pattern

Day 3: Dashboard and Performance Decision

Designing Dashboard Structure

• Group measures around management questions
• Show baseline, target, trend, exceptions clearly
• Remove indicators not supporting decisions
• Design a concise service performance view

Thresholds and Actions

• Set trigger levels for management attention
• Match escalation with seriousness of deviation
• Connect each threshold with review action
• Define who owns the follow-up

Service Performance Review Lab

• Interpret a dashboard containing mixed signals
• Prioritize the issue requiring investigation
• Link deviations to owners/review dates
• Present a service performance decision

• Service datasets with different volumes and patterns for hands-on analysis
• Indicator-definition exercises covering formulas sources and common data-quality problems
• Variance interpretation cases requiring diagnosis before corrective action
• A dashboard project ready for recurring service-performance review