Retail Analytics Pipeline
6 hours of manual reporting → 0
A multi-channel retailer had sales sitting in four places and no single number anyone trusted. They wanted one view of the business that didn't depend on a person rebuilding it by hand every morning.
6 hrs → 0
Daily manual reporting
4
Sales channels unified
<5 min
Fresh data each morning
The challenge
Four sales channels meant four separate reports, rebuilt from scratch each morning. Roughly six hours of copy-paste before anyone could see how the business was doing. Errors crept in most weeks, usually a mispasted column, and by the time someone spotted one the day's decisions had already been made on it.
Our approach
- 1
Mapped all four source systems first, then agreed a single set of analysis-ready tables with the finance team before writing any pipeline code.
- 2
Built the ingestion to be idempotent, so a re-run after a failed night produces the same numbers instead of double-counting.
- 3
Added quality checks at each load: row counts, null rates, and revenue variance against the previous week, with an alert when something moves more than it should.
- 4
Posted a morning summary into Slack so the numbers land before anyone opens a laptop, and handed over documentation so the client's own team can extend it.
The outcome
Nobody rebuilds the morning report by hand any more.
Bad rows get caught at load time rather than surfacing days later in a board pack.
Finance and ops now argue about what the number means, not about which spreadsheet is right.
“What used to take my team a full day every Monday now lands in Slack before we wake up.”
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