Some of the hardest work in retail analytics happens long before dashboards, forecasts, or machine learning models.
It begins with a much less glamorous challenge: making sure “the same product” is actually the same product across systems.
In a typical supermarket, there were 31,795 items on the shelf on average in 20241. Across suppliers, distributors, and point-of-sale systems, the data behind those items is often fragmented, inconsistent, and noisy. GS1 reports that retailers may need 10–15 interactions with suppliers to launch each SKU, and that they face ~15,000 issues with inaccurate product data per year on average.2
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