Reporting, Data and Dashboards

Recurring reports and dashboards built from validated data, so the numbers are ready when you need them and you can trust where they came from.

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How it works · From scattered exports to a trusted monthly report

STEP BY STEP

CRM export
Accounting
Spreadsheets

1
Collect01
Data pulled on a schedule
3 sources · 06:00
2
Validate02
Missing and duplicate rows flagged
4 duplicates removed
Human checkpoint
3
Calculate03
Agreed metric definitions applied
12 metrics
4
Explain04
Short written summary added
Margin down 2 pts
5
Publish05
Report and dashboard updated
Sent to managers

Out

Dashboard
Monthly pack
Exception list

See how this would run on your own process, with your tools.

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The problem

What this solves.

Reports are rebuilt by hand from exports and spreadsheets every week or month. It takes time, errors creep in, and by the time the numbers are ready they are already out of date.

What changes

Reports prepared automatically on schedule
One agreed definition for each metric
Data issues flagged before they reach a report
Dashboards people actually check

Use cases

What this looks like in practice.

Typical examples. Every build is scoped to your own process and systems.

Monthly management pack

Sales, finance, and operations data is pulled together, checked, and turned into a consistent monthly report with a short written summary.

Operations dashboard

Live figures from the CRM, ticketing, and finance systems in one view, with alerts when a number moves outside an agreed range.

Spreadsheet consolidation

Dozens of team spreadsheets are replaced by a single validated data source that feeds the reports everyone already uses.

Scope

What we can build.

Capabilities

Data pipelines from your business systems
Data cleaning and validation rules
Scheduled reports and exports
Live dashboards
Exception and anomaly summaries
AI-written commentary on agreed metrics

How it works

We agree metric definitions first, then build pipelines that validate data on the way in. Calculations use deterministic rules; AI is used for commentary and classification only where it is reliable.

Tools we commonly work with

Google Sheets
Excel
Looker Studio
Power BI
BigQuery
PostgreSQL
Supabase
Airtable

Limits and human review

Reports are only as reliable as the source data and definitions behind them. We surface data quality issues rather than hide them, and we do not present estimates as measured figures.

Delivery

From first call to working system.

01
Discover
Days
02
Map
1–2 weeks
03
Design
Days
04
Build and test
2–6 weeks
05
Improve
Ongoing

You receive

Data pipeline with validation
Metric definitions document
Dashboard or scheduled report
Exception summary process
Handover and documentation

The Shapes Method

Workflows

Workflows in this service.

Each one has its own page with a diagram of how it runs.

Questions

Common questions.

Do we need a data warehouse?

Not usually to start. Many reporting workflows run well on spreadsheets or a small database. We recommend a warehouse only when volume or complexity calls for it.

Can you feed Power BI or Looker Studio?

Yes. We prepare clean, validated data for the dashboard tool you already use, or set one up.

How often can reports refresh?

As often as the source systems allow: hourly or daily is typical, and near real time where the APIs support it.

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Next step

What would you like to improve?

Tell us what is taking too much time, what is difficult to manage, or what you would like your systems to do differently. You do not need a technical specification to start.

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