Zain The AnalystAvailableCase Study
Data Analysis and Visualization
IT Help Desk Dashboard
A Power BI dashboard that helps understand IT tickets, SLA performance, resolution time, agent workload, priority issues, and satisfaction trends.
Key Metrics Analyzed
Total Tickets97K
Total Agents50
SLA Compliance Rate0.48
Average Resolution Time5 Days
Average Satisfaction Rate4.1
Overview & Objectives
This project analyzes IT help desk ticket data using Power BI. It helps users understand ticket volume, SLA compliance, agent performance, resolution time, satisfaction rate, issue types, priority levels, severity levels, and monthly ticket trends.
The dashboard includes two report pages. The first page focuses on help desk KPI tracking, ticket resolution performance, top agents, day type analysis, priority analysis, request categories, and resolution groups. The second page focuses on satisfaction category, SLA status, issue type, priority and severity analysis, monthly ticket trends, and normal ticket growth over time.
Project Goal
Build a clear Power BI dashboard that helps IT teams monitor ticket performance, improve SLA tracking, understand resolution delays, and manage help desk workload.
The Business Problem
IT support data can be difficult to understand when ticket volume, SLA performance, issue type, priority, severity, agents, and satisfaction scores are analyzed separately. The business needed one clear dashboard to monitor help desk performance, find ticket delays, track SLA compliance, and understand support team workload.
Methodology
My Approach
- 1I cleaned and prepared the IT help desk ticket dataset.
- 2I created Power BI measures for total tickets, total agents, SLA compliance rate, average resolution time, satisfaction rate, and ticket trends.
- 3I designed dashboard pages for help desk overview and deeper ticket performance analysis.
- 4I added visual comparisons by issue type, month, priority, severity, resolution status, agent, satisfaction category, and SLA group.
Implementation
Roadmap Execution
Data Preparation
Cleaned and prepared the IT help desk ticket dataset for analysis.
Data Modeling
Created the Power BI data model for tickets, agents, issue types, priority, severity, SLA status, and satisfaction.
DAX Development
Built measures for total tickets, total agents, SLA compliance rate, average resolution time, satisfaction rate, and ticket trends.
Dashboard Design
Designed help desk overview and ticket performance analysis pages.
SLA and Ticket Analysis
Created visuals to analyze tickets by SLA status, priority, severity, issue type, request category, agent, month, and day type.
Final Formatting
Formatted KPI cards, bar charts, line charts, donut charts, area charts, and grouped visuals for clear IT support reporting.
Key Features
IT help desk KPI overview
Ticket volume analysis
SLA compliance tracking
Average resolution time analysis
Top agent performance
Tickets by day type
Tickets by priority and resolution status
Tickets by request category and priority type
Tickets by resolution group
Satisfaction category analysis
SLA status by issue type
Monthly ticket trend analysis
Normal tickets trend over months
Business Impact
Helps IT teams monitor support performance in one place
Makes SLA compliance and resolution delays easier to understand
Shows which agents, priorities, and issue types drive the most tickets
Supports faster help desk decisions and workload planning
Reduces manual IT reporting work by using one interactive Power BI dashboard
Challenges Overcome
- Combining ticket, agent, SLA, priority, severity, issue type, and satisfaction data into one dashboard
- Making ticket delays and SLA issues easy to understand
- Showing support workload clearly across agents and categories
- Comparing ticket trends across months and years
- Designing simple IT support visuals for quick decision-making
Outcomes
Final Outcomes & Learnings
- The final dashboard gives a clear view of help desk performance, ticket workload, SLA compliance, agent productivity, resolution speed, and customer satisfaction.
- Users can quickly identify high ticket categories, SLA problems, delayed resolutions, top agents, and monthly support trends.
Project Gallery & Screenshots
Technologies Used
Power BIPower QueryDAXExcelData ModelingData Visualization
Data Sources
IT help desk ticket datasetSLA status dataAgent performance dataIssue type dataPriority and severity dataSatisfaction rate dataMonthly ticket trend data
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