This project presents an interactive Power BI dashboard designed to analyze and monitor analyst performance in an incident and support management environment.
The dashboard provides visibility into ticket volumes, SLA compliance, resolution efficiency, and workload distribution across analysts and roles.
By combining operational and performance metrics, the report helps identify service risks, bottlenecks, and optimization opportunities.
It supports data-driven decision-making for team leads, managers, and operations teams.
- Monitor analyst productivity and workload distribution
- Evaluate SLA compliance and service risks
- Analyze ticket resolution efficiency (MTTR)
- Identify top-performing and overloaded analysts
- Support resource planning and operational optimization
- Descriptive analysis of ticket volumes and analyst workload
- Performance comparison across analysts and roles
- Severity-based incident analysis (S1–S4)
- Time-based trend analysis (monthly and weekly)
Shows total analysts, active analysts, and average ticket load per analyst.
Displays ticket breakdown by severity and analyst handling capacity.
Visualizes SLA met vs breached cases and MTTR trends.
Highlights workload imbalance, service risks, and improvement opportunities.
(All visuals are interactive and support filtering by analyst, severity, and time period.)
- High Operational Load: 2,270 tickets resolved over 6 months, averaging ~91 tickets per analyst.
- SLA Risk Identified: Overall SLA compliance is ~47%, indicating a major service delivery risk.
- Resolution Efficiency: Average MTTR is ~13 hours, acceptable overall but higher for complex cases.
- Uneven Workload Distribution: A small group of analysts (e.g., Meena, Sanjay, Rohit) handle the highest ticket volumes.
- Role-Based Performance Pattern: L1 Support resolves most tickets, while L2 and Senior Analysts handle fewer but more complex issues.
- Speed vs Quality Trade-off: Higher ticket volumes correlate with increased MTTR and SLA breaches.
- SLA Fairness Gap: Uniform SLA targets do not account for issue complexity.
- Actionable Improvements: Workload rebalancing, complexity-based SLAs, and targeted training can improve performance.
- Power BI Desktop
- DAX (Data Analysis Expressions)
- Excel / CSV
- Download the file
Analysts Performance Dashboard.pbix - Install Power BI Desktop: https://powerbi.microsoft.com/
- Open the
.pbixfile in Power BI Desktop - Refresh the data if required
- Navigate through dashboard pages:
- Overview
- Analyst Insights
- Severity Analysis
- SLA & MTTR
- Summary & Insights
- Use slicers to filter by analyst, role, severity, or time period
- Drill down into individual analyst performance for deeper analysis
Let’s connect on LinkedIn for project discussions or data-driven collaborations:
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