We work with datasets that companies have already collected but can't use effectively. That usually means inconsistent formats, gaps in coverage, or raw figures that need aggregating before they mean anything. We clean, structure, and analyse the data, then deliver findings in whatever format your team actually reads.
We start by agreeing on what you need to know — not just what data you have. This shapes the entire project.
Raw data is almost never analysis-ready. We handle deduplication, format normalisation, and gap-filling before any analysis runs.
You get findings in the format your team uses — spreadsheet, dashboard, PDF report, or a data feed into your existing tools.
What they had: Three years of their own sales data plus manually collected competitor prices — inconsistent formats, different date ranges, no common SKU identifier.
What we did: Matched products by description and category, normalised prices to a per-unit basis, and built a monthly view of how their margins compared to market across 400 SKUs.
Output: A spreadsheet model they could update themselves each quarter, plus a one-off report identifying the 40 SKUs where they were priced more than 15% above the nearest competitor.
If you know roughly what question you need answered but aren't sure whether your data is good enough to answer it, send it over. We'll tell you honestly what's possible.
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