Accuracy Before Advice

There’s a lot of excitement around AI systems that analyze business data and recommend what to do next. But getting reliable data into those systems remains a serious engineering challenge.

When building Nudge, we’ve spent a significant amount of time dealing with differences between data sources.

Marketplaces and advertising platforms define metrics differently. Sales and advertising data may cover different reporting periods. Returns can be reflected with a delay. And revenue becomes more complicated when a Shopify store operates across multiple markets and currencies.

We’ve encountered these challenges while implementing multi-market and multi-currency support for Shopify. Before analyzing sales performance across markets, we need consistent values, correct currency conversions, and a clear understanding of how Shopify reports transactions.

Otherwise, even individually correct numbers can produce misleading results when combined. An AI system might recommend increasing advertising spend based on revenue growth that partly comes from incorrect data aggregation.

This work rarely gets much attention compared to AI models and recommendation engines, but it’s a significant part of what we do at Nudge.

We’re building systems that account for where data comes from, how it’s calculated, and what limitations it has, rather than simply passing numbers to an AI model and expecting useful recommendations.

Accuracy first. Then advice.

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