Most distributors know their product data is a mess. Few understand how much it's actually costing them.
The symptoms are obvious: search that returns garbage, product pages with missing specs, customers who call instead of clicking "add to cart" because they can't trust what they see online. But the real damage is structural. Bad product data doesn't just create friction — it makes entire categories of digital capability impossible.
The compounding problem
Product data problems compound. A missing attribute means search can't filter properly. Bad categorization means recommendations are useless. Inconsistent manufacturer part numbers mean your deduplication logic breaks, and suddenly you're showing the same product three times with different prices.
Each of these failures is manageable in isolation. Together, they create a platform that feels broken even when the code is fine. Your engineers are debugging data problems disguised as software bugs.
What good looks like
The distributors who get this right treat product data as infrastructure, not content. They invest in normalization pipelines, attribute governance, and automated quality checks. They hire people whose job is data quality, not just data entry.
This isn't glamorous work. It doesn't demo well. But it's the foundation that makes everything else — search, personalization, pricing, AI — actually functional.
The AI angle
Every distributor wants to "use AI" for product discovery or recommendations. But AI trained on bad data produces bad results with more confidence. The LLM doesn't know that your categorization is inconsistent — it just learns the inconsistency.
Before you invest in AI-powered search, invest in the data that feeds it. The model is rarely the bottleneck. The data almost always is.