Is your product data ridden with invisible errors that are killing your conversion rates? Discover why manual quality control is impossible at scale and how to build an automated "quality gate" for your PIM.

In this episode of Navigating Data, Sam and Alex dissect the hidden costs of poor data quality—from expensive returns to regulatory fines. They explain why manual checking collapses as soon as you scale to thousands of SKUs and why a modern Product Information Management (PIM) system needs a multi-layered defense strategy.

The hosts detail the "Three Pillars of Validation" used within the OMN platform: Static Rules for non-negotiable standards, Scripts for complex business logic, and AI-driven Plausibility Checks to spot outliers that look right technically but are contextually wrong (like a 30kg smartphone). Learn how to turn your data maintenance from a chaotic reactive process into a streamlined, automated workflow.

Key Topics Discussed:

• The Cost of Errors: How "dirty data" leads to higher return rates and damages brand reputation.
• Pillar 1 - Static Rules: Enforcing mandatory fields and value ranges as the first line of defense.
• Pillar 2 - Scripting: Using automation to handle complex dependencies (e.g., if "Size M" then "Price X").
• Pillar 3 - AI Plausibility: Leveraging Artificial Intelligence to detect semantic anomalies and outlier patterns that rules miss.
• Workflow Integration: How to physically block incorrect data from being published to your shop or catalog.

Want to automate your data quality? Read our deep dive into setting up rules, scripts, and AI validation for your commerce strategy on our blog:

https://apollon.de/en/artikel/pim-validation-automatic-plausibility-checks-for-clean-data/