Tags: #python #sql #datascience #beginners
How can an IT support team identify recurring problems, measure service quality and improve resolution times?
I explored these questions through my Help Desk Ticket Analysis project using Python, Pandas, SQL and Matplotlib.
The Dataset
The project uses 900 synthetic IT support tickets from 2025.
The dataset contains information about issue categories, priorities, support teams, response times, resolution times and service-level agreement (SLA) performance.
Because the data is synthetic, the findings demonstrate analytical techniques rather than the performance of a real organisation.
Data Cleaning
Before calculating performance metrics, I worked with common data-quality problems, including:
- Duplicate ticket identifiers
- Missing values
- Inconsistent support-team names
- Incorrect timestamp formats
- Inconsistent issue categories
One important decision was not to automatically discard every incomplete ticket.
A record missing an analyst name may still contain useful resolution and SLA information.
Key Results
The repository reports the following results for its synthetic dataset:
| Metric | Result |
|---|---|
| Total tickets | 900 |
| Average first response | 0.91 hours |
| Average resolution time | 7.74 hours |
| SLA compliance | 70.3% |
| Average satisfaction | 4.03/5 |
The most common issue category was Application Error.
Why SLA Compliance Matters
A low average resolution time can look positive while hiding serious delays for high-priority tickets.
That is why I compared multiple indicators rather than relying on one overall average.
The analysis considers ticket volume, issue categories, resolution time, SLA compliance and customer satisfaction.
Tools Used
Python and Pandas for cleaning and aggregating records.
SQL for operational reporting queries.
Matplotlib for visualising ticket patterns.
Jupyter Notebook for exploratory analysis.
What I Would Improve
Future extensions could include an interactive Power BI dashboard, ticket backlog analysis, reopen rates, first-contact resolution and automated ticket categorisation.
Conclusion
This project demonstrates how data analysis can support IT service management.
The main lesson was that operational performance should be evaluated through several connected metrics.
GitHub: https://github.com/tasnem-tech/help-desk-ticket-analysis
Which service desk KPI do you find most useful: response time, resolution time or SLA compliance?
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