Data & Intelligence
Automated reporting: the report is ready before anyone's at their desk
The report that takes the whole morning
Weekly operations summaries, monthly client reports, board packs: most are assembled by hand. Exports from the CRM, accounting, the ad platforms and analytics, pasted into a spreadsheet, formulas fixed, formatting redone. It takes hours every cycle.
The numbers differ depending on who built the report, and they're already old by the time it's sent. When a figure looks wrong, nobody can easily trace where it came from. And when the person who builds it is away, the report doesn't happen.
Today
- 01Someone exports data from the CRM, accounting, ad platforms, analytics and other tools.
- 02They clean and combine it in a spreadsheet, often with formulas only they understand.
- 03Metric definitions live in that spreadsheet, or in someone's head.
- 04The report is formatted, checked and sent by email or dropped in a shared folder.
- 05Questions about a number lead back to the spreadsheet and the person who built it.
Where it breaks: hours of manual work every cycle, inconsistent definitions, silent errors when an export changes, and reports that stop when their builder is away.
With automated reporting
- 01Source systems are connected through their APIs or scheduled exports.
- 02Raw data lands in a staging area, and each load is recorded.
- 03Validation checks for missing, duplicated or out-of-range data before anything uses it.
- 04Transformations turn raw data into one model with agreed metric definitions.
- 05The report is assembled on schedule from those metrics.
- 06Where interpretation matters, a person reviews it and adds commentary.
- 07It's delivered by email, as a dashboard, or as a shared link.
- 08A data problem raises an alert and holds the report instead of sending wrong numbers.
Follow one Monday's report
Run
Report run · Week 42
- Status
- Loading
- Data as of
- Mon 05:00
- Sources
- 5 of 5
- Checks
- —
- Reviewer
- —
- Recipients
- —
- Delivered
- —
Step 1 of 7: sources.loaded
The pipeline under the report
A dashboard is presentation. The real system is the pipeline underneath it: ingestion, staging, validation, transformation, one model, and metrics computed the same way every time. Get that right and reports, dashboards and later analysis all draw on the same numbers.
Built from six capabilities
Deliver
Templates, schedules, review and delivery.
- template
- schedule
- review rule
- recipients
- format
checks failed → held, owner alerted
Where people stay in charge
Metric definitions
Owned by the business
Agreed and owned by the business, not by whoever builds the report.
Review before sending
A pause where judgment matters
Client-facing or board reports can pause for a person to check them and add commentary.
Data quality alerts
A named owner, not a shared inbox
Failed validation goes to a named owner rather than into the report.
Calculated, then commented on
AI-written commentary, where it's used, is grounded in the governed metrics and kept visibly separate from them. The calculation is deterministic; the explanation is not, and readers should be able to tell which is which.
Calculated
- Jobs completed, Week 42
- 142
- Booked revenue
- $61.4k · on target
- Riverside web-form bookings
- 15 · down from 22
Commentary · AI draft · reviewed by the ops manager
Riverside's web-form bookings have dropped for two weeks while calls held steady. Worth checking the Riverside landing page and form before adjusting spend.
What changes from one business to the next
For modest systems the starting point is simple: ingestion through APIs or n8n into PostgreSQL, SQL views and models for the metrics, and Metabase for dashboards and scheduled reports. Larger or connector-heavy environments may justify Airbyte and dbt-style transformations into a warehouse. These are starting candidates, not requirements.
Is it the right fit?
Good fit
- The same report is rebuilt by hand every week or month.
- The data comes from several systems that don't talk to each other.
- People disagree about what the numbers are.
- An agency or team produces similar reports for many clients or business units.
Avoid when
- The report is a one-off, or an occasional analysis.
- The source data can't be reached by API or export, or isn't reliable enough to automate yet.
- Nobody agrees on what the metrics mean. Automation will deliver the disagreement faster; the definitions need agreeing first.
How we'd approach it
01
Understand
As much as the problem needs
We map how the work runs today: who does what, which systems hold the records, and where it breaks.
02
Design
Decided before it's built
Ownership, rules, integrations and exceptions, agreed with you and written down with acceptance criteria.
03
Build
Connected to what you use
We build the scoped system, connect it to the tools you already run, and test it on the cases that matter.
Questions about automated reporting
Can it produce a different report for each client?
Yes. A report is defined once as a template and generated per client, location or team, each with its own filters, recipients and format. Agencies often start here.
What happens when a source is late or wrong?
Validation catches it before the report is built. The report is held, the owner gets an alert saying what's missing, and it goes out once the data is fixed, or is released deliberately with a note.
Do we have to replace our current tools?
No. Your CRM, accounting and ad platforms stay as they are; the pipeline reads from them. Reports can arrive by email, as PDFs, or in a dashboard tool you may already use.
How long until the first report is automated?
It depends mostly on how many sources there are and whether the metric definitions are agreed. We start with one recurring report and its sources, then add reports on the same model.
Will AI write our reports?
The numbers are always calculated, never generated. Where commentary helps, AI can draft it from the governed metrics, clearly marked, and a person approves it before it's sent.
Get your Mondays back
Tell us which reports take the longest to put together today. We'll show you which could assemble themselves and which need a person.