Insights
Squadra PIM Survey: the Dutch PIM landscape as of 2025
This edition of the Squadra PIM Research shows that Product Information Management PIM is clearly in a transitional phase at many organizations: traditional ways of working and modern data technologies coexist.
Product data is increasingly managed centrally under the direction of specialized data departments; for 98 percent, centralized product data management is the norm. Yet the foundation is not yet mature everywhere and data quality remains a structural challenge, while 97 percent of respondents indicate that high quality product data is very important or extremely important.
Key take-aways:
- Centralized product management is the standard: almost all organizations 98 percent now manage product data centrally. This leads to more control over quality, consistent data flows, and better compliance with laws and regulations.
- Product data has a direct impact on commercial results: organizations see that better product information improves conversion, reduces customer questions, and lowers returns. PIM is therefore becoming an increasingly important commercial and digital growth accelerator.
- Sustainability data is still insufficiently developed: 57 percent have no data strategy for sustainability and focus mainly on raw materials and certifications. Data on circularity and lifespan lags behind, despite upcoming legislation.
- AI application is highly promising, but broad adoption is still at an early stage: more than half do not yet use AI for product data management. Where AI is used, it mainly concerns translation, classification, and content generation, often still performed manually.
- Good supplier data remains a major challenge: 80 percent receive insufficient product information from suppliers, leading to extensive manual enrichment and delays in time to market. Standards and supplier portals are seen as the solution.
- Datapools are still used in a limited way: despite the benefits, almost half do not use datapools for supplying or receiving data. This slows scalable collaboration in the chain and consistent use of standards.
- Data quality remains a structural challenge: although 97 percent consider data quality very important, technical and logistical attributes are often incomplete or inconsistent. This hinders sales, filtering, and product comparison.
Download the Squadra PIM Research 2025:
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