Operational Performance
SLA Pareto Analytics Framework
A data-driven analysis framework designed to identify long-running tickets, waiting statuses, SLA risks and high-impact operational bottlenecks across support operations.
The Challenge
Support operations were experiencing long-running tickets, waiting statuses and limited visibility into the factors driving SLA risk and operational delays.
Business Impact
Improved visibility into SLA performance, enabled data-driven prioritization and helped operational teams focus on the most impactful problem areas.
Key Metrics
SLA PerformancePareto AnalysisMB + ITC Impact
Key Findings
- ✦ Waiting statuses were major contributors to total resolution duration.
- ✦ A limited number of categories created a disproportionate operational impact.
- ✦ MB and ITC waiting periods increased SLA risk and created hidden delay patterns.
- ✦ Long-running tickets required stronger prioritization and operational follow-up.
Key Highlights
- ✦ Combined multiple ticket datasets to create a consolidated SLA analysis view.
- ✦ Analyzed long-running tickets, waiting statuses and operational delay patterns.
- ✦ Measured MB and ITC impact to understand where waiting time affected total resolution duration.
- ✦ Applied Pareto methodology to identify the categories creating the highest operational impact.
- ✦ Translated analysis findings into actionable improvement areas for operational teams.
Technologies & Methods
Power BIPower QueryRoot Cause Analysis