[Project] Operational Performance & MI Reporting: Turning Service Data into Daily Decisions

Why I worked on this project

Operational data rarely gets much attention.

It doesn’t look exciting.

It doesn’t come with big strategic questions.

And it’s often seen as something that just exists in the background.

But in many organisations, this type of data quietly shapes day-to-day decisions.

This project came from a simple observation:

operational teams don’t need complex analytics - they need clear, reliable visibility.

Visibility into workload.

Into backlog.

Into whether performance is drifting out of control before it turns into a real problem.

The problem I wanted to understand

Operational performance is often discussed in fragments.

A spike in tickets here.

A missed SLA there.

A general feeling that one team is “always overloaded”.

Without consistent reporting, those signals stay anecdotal.

The questions behind this project were simple, but important:

• Is workload actually stable over time?

• Where does backlog build up - and does it affect service quality?

• Are some teams under structural pressure rather than temporary spikes?

• Are there patterns that could support planning, not just reacting?

  
  

The aim wasn’t to explain everything.

It was to make everyday operational performance easier to see and talk about.

How I approached the analysis

I treated this as a typical MI / operational reporting scenario.

Before thinking about dashboards, I focused on what managers would realistically need on a regular basis:

• a quick sense of whether performance is stable

• early warning signs of operational risk

• enough context to ask the right follow-up questions

  
  

I started in Excel, validating and structuring the core metrics - ticket volumes, backlog levels, resolution times and SLA compliance - to make sure the data aligned with how operational teams actually track performance.

Power BI came later, mainly to organise those metrics into a structure that could be scanned quickly and revisited consistently.

The priority throughout was clarity, not sophistication.

What stood out during the analysis

A few patterns became clear very early on.

Overall ticket volume was relatively stable, but backlog levels varied significantly across teams. That suggested uneven workload distribution rather than a demand problem.

Technical Support consistently carried a higher backlog, pointing towards ongoing structural pressure rather than short-term spikes.

There was also a clear relationship between backlog levels and SLA compliance. Teams under sustained backlog pressure were far more likely to miss SLAs.

Resolution times differed by region as well, hinting at process or escalation differences rather than purely volume-driven issues.

Finally, demand followed a predictable weekday pattern - busy during the week, quieter at weekends - reinforcing how important timing and resource alignment are in operational planning.

Why this matters from a business point of view

Without clear operational reporting, teams end up managing by intuition.

This type of MI reporting supports more grounded conversations around:

• where workload balancing is genuinely needed

• which teams are under long-term pressure rather than temporary strain

• how backlog impacts service quality over time

• where planning adjustments could prevent future issues

  
  

Instead of reacting to individual incidents, managers can focus on patterns and trends.

That shift alone can make operational decision-making calmer and more effective.

Tools used (kept intentionally simple)

• Excel for data validation, structuring and initial analysis

• Power BI for modelling, visualisation and dashboard design

  
  

The focus was on transparent logic and practical interpretation rather than advanced technical features.

Want to see the technical details?

The full project - including the Power BI report and supporting files - is available on GitHub.

That’s where the dashboards and structure live.

The blog post focuses on the thinking behind the analysis.

Final thought

Operational reporting is rarely glamorous.

But when it works well, it quietly prevents problems before they escalate.

This project reinforced something I keep coming back to:

boring, clear reporting often has more impact than complex analysis that no one revisits.

And in many real business environments, that’s exactly what’s needed.