MIRAS

Case Study

Automated Reporting

A system that builds the weekly report and flags what your team would miss

A marketing team was spending six hours every week pulling numbers from five different systems and formatting them into a report that was already out of date by the time anyone read it. We built a system that assembles it overnight and flags problems the team would have missed.

The problem

In this case it was a marketing team, but anyone who has to produce a recurring report will recognise the problem.

Five data sources, six hours of assembly, and by the time leadership saw the numbers they were already a week old. One person knew how to build it. When they were away, nobody got a report.

6

hours lost

Every week, manually pulling and formatting

5

data sources

Each one requiring a separate login and export

7

days stale

By the time the report landed, the data was old

1

person

If they were on holiday, nobody got a report

The approach

We sat with the team and mapped how the report was actually put together.

The team was good at their jobs. The report was just a bad use of their time. Instead of trying to make it faster to build, we removed the need to build it at all.

01

Pull and assemble

The system connects to every data source overnight and assembles a single view of the week.

02

Analyse and flag

It runs the checks the team used to do by hand, along with trend analysis across multiple weeks that they never had time for.

03

Deliver

A finished report is waiting before anyone arrives. The team reviews it and adds what they know.

What it catches

The system does more than assemble. It reads the numbers and works out what changed and why.

Watch

Declining trend detected

Issue Utilisation down 3 consecutive weeks 74% → 71% → 68%

Likely driver North West region Project intake slowing since mid-March

8-week trend

Recommended action Review North West pipeline and intake forecast before next planning cycle.

Trend detection

It tracks metrics over weeks, not just the latest snapshot. A slow decline that nobody notices from one week to the next gets flagged once it becomes a pattern.

Root cause identification

When something moves, the system works out why. If utilisation dropped, it will point to the region or segment behind it rather than just reporting the number.

Actionable next steps

Every flag includes a recommendation for what to look at next. The team goes straight to the issue instead of spending time working out where to start.

What changed

The report nobody wanted to build now builds itself.

The team walks in to a finished report, reviews it, adds their own notes, and moves on. The hours that used to go toward assembly now go toward doing something about what the numbers say.

Weekly Performance Summary

Auto-generated · 7 Apr 2026

Week 14 · 31 Mar – 6 Apr

Revenue

£284k +6%

Pipeline

£1.2m +3%

Utilisation

68% ↓ 3 wks

Satisfaction

4.6 Flat

Performance by region

Region	Status	Revenue
London & South East	On track	£98k
Midlands & East	On track	£76k
North West	Watch	£62k
Scotland & North East	On track	£48k

For the first two weeks, the team double-checked every number by hand. By week three, they stopped.

Impact

What the team got back.

Before

Six hours spent assembling a report every week
Data was already a week old by the time it was read
Formatting changed depending on who built it
Problems buried in the numbers went unnoticed

After

Zero assembly time, the report arrives overnight
Data is from the previous day, always current
Consistent format and structure every week
Anomalies are flagged and explained automatically

What this means

Your team should be reading their reports, not building them.

We spent time with the team to understand how the report was put together and where the hours went. Then we built a system to handle it. The whole thing took eight weeks from first conversation to handover, and the team has been running it on their own since.

#Automation #Reporting #Intelligence

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