Case study
IT Service Management Analytics
Turning Jira service desk data into actionable operational and SLA insight.
Overview
The IT / Service Management team ran its service desk through Jira, but had no consolidated analytical view of how it was actually performing — ticket volume, SLA health, workload distribution, or where service demand was concentrated. I built an end-to-end analytics solution turning Jira ticket data into operational insight, from an executive overview down to individual ticket investigation and back into Jira itself.
Data Privacy Note
🔒 Data privacy: This case study reflects a real-world project and solution. Names, figures and identifiable information shown in dashboard screenshots have been anonymised or replaced with representative data to protect confidentiality.
The Challenge
This project started with a technical challenge before it became a reporting one: the required Jira data wasn't already available through an established analytics source, and I hadn't previously built this kind of Jira integration.
Technical decision-making
Choosing the right integration approach
Rather than treating the missing data source as a blocker, I researched Jira's documentation and evaluated two realistic ways to get the data into Power BI.
More control
Jira API
- Greater flexibility and control
- No connector licensing cost
- More development effort and delivery time
Selected
Power BI / Jira Connector
- Faster implementation
- Simpler path to the required data
- Additional connector cost
I presented both options to the relevant stakeholder, along with the trade-off between cost, effort, and delivery speed. Since the business wanted the solution relatively quickly and the connector cost was considered reasonable, the connector approach was selected.
End-to-end ownership
My Role
How I built it
Building the Data Foundation
Power Query
Prepare
- Field identification
- Data cleansing
- Transformation
Data Model
Model
- Ticket lifecycle logic
- SLA & aging measures
- DAX calculations
Power BI
Deliver
- Overview & breakdown pages
- Drill-through
- Direct Jira navigation
From Tickets to Service Insight
The report
Service Management Overview
The Service Management Overview is the executive view of the service desk — organised around the questions stakeholders actually asked, not just a list of charts.
Understanding Service Demand
Tickets opened vs. closed, and incident vs. service request volume.
Monitoring Operational Workload
Active tickets and how workload is moving over time.
Tracking SLA Health
Overall SLA performance, including first-response and resolution breaches.
Identifying Emerging Risks
Tickets approaching an SLA breach, before they become one.
Workload Distribution
How ticket activity is distributed across the team, by priority.
Ticket Trends
Volume and priority trends at month, week, or day granularity.
Proactive SLA visibility
From reporting to operational awareness
Beyond reporting on what already happened, the overview surfaces tickets approaching their SLA threshold — giving stakeholders a way to see what may need attention now, not just what was missed after the fact.
Diagnostic view
Diagnosing the Workload
The Ticket Breakdown page answers a different question: not 'how are we performing?' but 'what's actually driving the workload?'
Category & Subcategory
Where service demand is concentrated, to support further investigation.
Resolution Analysis
How service issues are actually being resolved.
Ticket Aging
How long tickets remain in the process, alongside priority and SLA context.
From insight to investigation
From either the overview or the breakdown page, users can drill through from an aggregated pattern straight into the underlying tickets — moving from 'something looks off here' to the actual records behind it.
The reporting experience is designed as a journey: high-level operational monitoring, through deeper ticket analysis, to individual ticket investigation in Jira.
Drill-through investigation
Users can move from high-level metrics into individual ticket details, with filtering by status, priority, category, assignee, and SLA performance, and direct links back to Jira for further investigation.
Making the solution self-service
Rather than separating documentation from the report, I embedded a Notes & Definitions page directly into the reporting experience — covering metric definitions, aging and SLA terms, and how to use the drill-through and Jira navigation, so the report didn't depend on someone else explaining it.
Technology Stack
Business Value
Key Takeaway
This project is less about connecting Jira to Power BI, and more about what came before and after that step: researching an unfamiliar source system, evaluating integration approaches with a stakeholder, translating SLA and service-management concepts into analytics, and building a reporting experience that moves from executive overview to ticket-level investigation to the original Jira record.
Jira → Data → SLA Logic → Power BI → Investigation → Action