First week of the month, twelve spreadsheets
Several of your retained clients pay for a monthly absence report. At the start of each month a consultant collects the data. One client exports from BreatheHR. Another sends a rota spreadsheet with sickness in red. A care provider sends a scanned sign-in sheet. A retailer's store managers each keep their own list.
The consultant copies it all into a template, recalculates each person's absence against the client's own trigger points, writes a few lines of commentary, and formats the report. By the time the last client's report is done, it is the middle of the month and the data is already a few weeks old.
Why it takes so long
- Each client's data arrives in its own format, so nothing can be reused from last month.
- Trigger points differ between clients, for example number of occasions, total days or a score such as a Bradford Factor, and are recalculated by hand.
- Part days, shift workers and people who changed hours make the arithmetic fiddly.
- Commentary is written from scratch because nobody has last month's figures side by side.
- Formatting eats time that should go on reading the numbers.
What it is costing
The report arrives late, so managers act on absence weeks after it mattered. Consultant hours go on data handling, which makes the service hard to profit from. Arithmetic errors creep in, and a client who spots one stops trusting the rest. Meanwhile, the useful part of the service, a consultant saying 'these three people have hit your trigger and here is what your policy says happens next', gets squeezed into the last few minutes.
| Step | Manual | Automated |
|---|---|---|
| Collect data | Email, chase, retype | API pull or fixed upload template |
| Standardise | Copy into template | Mapped into one dataset |
| Apply triggers | Recalculate by hand | Client's own rules applied |
| Compare months | Rarely done | Trend shown automatically |
| Commentary | Written from scratch | Consultant writes on a drafted summary |
How we build absence reporting
- For clients on HR systems with an API, such as BreatheHR, BambooHR, HiBob or Personio, absence records are pulled automatically on a schedule the client authorises.
- Clients on spreadsheets get one fixed upload template, with validation that catches missing dates or unknown employees on upload rather than at report time.
- Everything is mapped into a single absence dataset per client, handling part days, shift patterns and changes of hours.
- Each client's trigger points are stored as rules, taken from their own absence policy, and applied automatically. Employees who reach a trigger are listed with their history.
- The report compares this month with previous months and flags changes in patterns by team or site.
- A draft summary of the month is produced for the consultant, who adds their own commentary and recommendations before the report is released.
- Reports are published in the client's portal or sent as a branded PDF, and the client's managers can be given a live view if you want one.
Trigger points and any follow-up actions come from each client's policy and your consultants' judgement. The system counts. It does not decide what happens to an employee.
After it is set up
Setting up a new client becomes a checklist rather than a project: connect or issue the upload template, enter their trigger points, choose the report layout. That makes absence reporting something you can offer to more clients without adding a consultant's week to every month.
Data arrives on its own for most clients, and in a consistent shape for the rest. Reports can go out in the first days of the month. Consultants spend their time on the part clients value: explaining what the numbers mean and what their managers should do. And because each month is stored, questions such as 'is absence at the second site getting worse?' can be answered straight away.
Is this your monthly routine?
- Absence reports are assembled from differently shaped spreadsheets.
- Trigger points are recalculated by hand each month.
- Reports go out mid-month or later.
- Commentary is thin because data handling eats the time.
- You rarely compare this month with previous ones.