Support Ticket Analyzer

This Blog Post provides a detailed overview of the queries and visualizations performed on the Support Ticket Analyzer database. The goal of this analysis was to gain insights into support ticket trends, issue categories, and resolution times.

Please Note:  All Data is purely Fictional, created by me, using a combination of ChatGPT and Mockaroo.  Any similarities to any Business, Person or Entity are absolutely coincidental.  Everything is for demonstration purposes.  Coding was completed using ChatGPT for speed of delivery using relevant Prompt Engineering processes and iterations.

Database Structure

The Support Ticket Analyzer database contains a table with the following columns:

• TicketID (INTEGER) – Unique identifier for each ticket
• AssetType (TEXT) – The type of asset associated with the ticket
• IssuesType (TEXT) – Category or type of issue reported
• ResolutionTime(Hours) (INTEGER) – Time taken to resolve the ticket, measured in hours

Queries Executed

1. Tickets by Issue Type

This query grouped tickets based on the issue type to identify the most common problems faced. The result was visualized as a bar chart, showing the frequency of each issue type.

2. Average Resolution Time by Issue Type

This query calculated the average resolution time for each issue category. It helped highlight which issue types typically take longer to resolve.

3. Tickets by Asset Type

This query grouped tickets according to the asset type. The visualization provided insight into which assets required the most support interventions.

4. Tickets with High Resolution Times

This query filtered tickets that had resolution times more than twice the overall average. These outliers were visualized to better understand unusual or problematic cases.

Insights

From the analysis, the following insights were derived:

• Certain issue types occurred more frequently, indicating possible recurring technical challenges.
• Resolution times varied significantly by issue type, suggesting that some problems require deeper expertise or resources.
• Specific asset types were more prone to generating support tickets, which may indicate reliability issues or greater usage volume.
• Outlier tickets with extremely high resolution times warrant closer review to identify bottlenecks or inefficiencies in the support process

Conclusion

The queries and visualizations performed on the Support Ticket Analyzer database provided valuable operational insights. These findings can help IT teams prioritize resources, address recurring issues more efficiently, and improve overall service quality.