IT Service Ticket Analysis

Overview

As an IT Data Analyst on the Warren Wilson College ITS Crew, I wanted to move the team from reacting to tickets to understanding them: how long issues actually take to resolve, when demand spikes, and which problems keep coming back.

I exported ticket records from ServiceDesk Plus, cleaned and reshaped them in R with tidyverse, and built summaries the team could use to plan staffing and prioritize fixes.

Data

1,000+ IT service tickets exported from ServiceDesk Plus, covering categories like hardware, network, accounts, and classroom technology, along with timestamps for submission, assignment, and resolution.

Cleaning included standardizing category labels, parsing timestamps into workable date and hour fields, and removing anything confidential before analysis. All private student information, names, and ticket details are excluded from anything shown publicly.

Analysis

• Median and distribution of resolution times by issue category, separating quick fixes from long-running problems
• Ticket volume by hour of day and day of week to find peak workload periods
• Week-over-week trends to see whether recurring issues were actually shrinking
• Breakdown of hardware and network tickets to identify the most frequent failure patterns

Visualizations

The working dashboards include a resolution-time histogram by category, a heatmap of ticket volume by day and hour, a weekly trend line, and a ranked bar chart of recurring hardware and network issues.

Findings

• Resolution times vary widely by category: account requests close quickly, while network issues take the longest
• Ticket volume peaks at the start of the week and during morning hours, which lines up with when the campus is busiest
• A small set of recurring hardware problems accounts for a large share of total tickets, pointing to equipment that may need replacing rather than repairing