By Joris Polhaar, Sales Consultant
8 essential PIM features: why it already starts before your PIM.
A PIM system is quickly chosen. A PIM system that keeps working as you grow is something else. Comparisons often look at the same features: import, workflows, channels and language variants. But the most important difference often sits elsewhere: where does the platform start? At PIM, or already at the product data before it?
We see at retailers, brands and wholesalers that many product data problems already arise before data reaches PIM. Think of fragmented supplier feeds, deviating attributes, missing classifications and manual mappings. These eight components therefore determine not only what a PIM can do, but above all whether your product data can truly flow at scale.
1. Supplier onboarding and import before PIM.
At many organisations, the biggest product data bottleneck already arises before PIM. Every supplier delivers data differently: Excel, CSV, XML, API or sometimes even PDF. Without structured onboarding, a team keeps mapping, correcting and checking again and again.
What to look for:
- Support for multiple feed formats and frequencies.
- Reusable mapping per supplier.
- Validation and normalisation before data enters the rest of the chain.
- Scalable processing of large numbers of SKUs.
At Azerty, dozens of supplier feeds are processed. At Wehkamp, it involves more than 2.5 million SKUs from different brands. At volumes like that, manual onboarding quickly becomes a structural bottleneck.
Learn more: Supplier Data Onboarding and Import & Onboarding.
2. Flexible data modelling and taxonomy.
A data model has to fit not only today's assortment, but also what is added tomorrow. New categories, brands, countries, standards and compliance requirements should not cause a technical migration every time.
What to look for:
- Multiple product types and attribute structures.
- Support for standards and your own taxonomies.
- Being able to model product relations and variants logically.
- Being able to adjust the data model without halting the daily operation.
Learn more: Data modelling and taxonomy and Data Model & Structure.
3. Governance and workflows.
As soon as purchasing, marketing, e-commerce and compliance work on product data at the same time, clear ownership becomes important. Who may change what? When is data good enough? And who has to check something before it is published?
What to look for:
- Clear roles and responsibilities.
- Configurable workflows and approval steps.
- Insight into changes and decisions.
- Quality rules that determine when a product may move on in the flow.
Governance is not about extra control layers. It prevents exceptions, corrections and knowledge from remaining dependent on individual employees.
Learn more: Product Data Governance and Workflows & AI.
4. AI enrichment and automation.
AI is mainly valuable when it structurally removes manual work. Not because there is an AI button in PIM, but because tasks that would otherwise be performed product by product can be automated in a controlled way.
What to look for:
- Generating product copy on the basis of existing product data.
- Contextual translations.
- Recognising or deriving attributes from source data.
- Data quality checks.
- Human review where that is necessary.
The real value of AI does not sit in automatically generating, but in controlled automation. The goal is that employees assess exceptions instead of building up every product manually.
Learn more: AI Product Data Automation.
5. Multichannel publication.
Every sales channel sets different requirements for product data. A web shop asks for different content than a marketplace, data pool or B2B portal. If those differences are resolved manually outside PIM, the product data problem merely shifts to the last step.
What to look for:
- Channel-specific mapping of product data.
- Content variants per channel.
- Automated publication and updates.
- Insight into errors and rejected products per channel.
Learn more: Publishing & Channels and Marketplace Expansion.
6. Reporting, audit and data quality.
As more product data is processed automatically, insight becomes more important. If rules, workflows and AI take decisions, it has to remain visible what happens, where exceptions arise, and where action is needed.
What to look for:
- Completeness and data quality per product group or channel.
- Insight into exceptions and errors.
- Audit log of changes.
- Reporting on processing and publication.
- Trends per supplier, category or channel.
What you do not see, you also cannot deliberately improve. Reporting is therefore not only a dashboard afterwards, but the control layer on top of the product data flow.
Learn more: Reporting & Analytics.
7. User management and collaboration.
A modern PIM platform is rarely used by one team. Internal teams, administrators, external partners and sometimes suppliers all work with different responsibilities.
What to look for:
- Roles and access rights per user or team.
- SSO and connection with identity management.
- Giving external users access only to relevant parts.
- Central and manageable user management.
Learn more: User Management.
8. Open architecture and integrations.
PIM never stands alone. ERP, DAM, web shop, marketplaces, data pools and other systems have to be part of the same product data flow. That is why not only the number of connectors matters, but above all how open the platform is.
What to look for:
- API access to relevant product data and processes.
- Event-driven or real-time connection options where necessary.
- Integrations with systems in your existing landscape.
- Clear technical documentation.
- As little vendor lock-in as possible in your product data flow.
View the integrations that ConnectingTheDots supports.
Which PIM features are really important for your organisation?
Not every organisation has the same priorities.
A retailer with hundreds of suppliers looks differently at PIM than a manufacturer with a few complex product lines. An international organisation will attach more importance to languages, local content and governance. A wholesaler, on the other hand, to supplier data, classifications and technical product structures.
That is why you should not only look at the number of features. Look at:
- where in your current product data flow most manual work sits;
- where errors arise;
- where processes slow down;
- which systems are leading;
- and which growth you expect in the coming years.
The best PIM choice is the platform that makes that product data flow simpler, not the platform with the longest feature list.
Learn more: What is PIM?
Conclusion: look beyond PIM itself.
Most PIM comparisons mainly look at features inside PIM. But an important part of the complexity already arises before that.
If supplier data first has to be cleaned, mapped and completed manually before PIM can work with it, part of the bottleneck remains. A platform that starts earlier (at supplier data, mapping, validation and structuring) can solve those problems closer to the source.
That is ultimately what scalability is about: not adding more and more people to solve exceptions, but organising product data so that it can move through the chain increasingly independently.
Good product data starts before your PIM.
Discover which PIM fits your situation. Or view the PIM platform, or plan a demo.
Frequently asked questions about PIM features.
Where a platform starts in the product data flow is at least as important as which features it contains. Some systems only start after product data has been imported. Others also support the steps before that, such as supplier feeds, mapping, validation and normalisation. That difference determines how much manual work remains outside PIM.
AI becomes interesting especially as soon as the amount of product data, languages, channels or manual enrichment grows. Look not only at whether AI is present, but above all which tasks are automated with it and how human oversight is arranged.
Look at the data model, the way suppliers and data sources are connected, how easily new channels can be added, and how open the architecture is. Also always ask for customer cases with a comparable size and complexity.
For e-commerce, reliable product data, channel-specific publication, workflows, data quality and automation are especially important. Which combination weighs heaviest depends on the assortment, number of suppliers, sales channels and international complexity.
Do not start with a feature list, but with your current product data flow. Map where manual work, errors and delay arise. Then use those bottlenecks as selection criteria for PIM. The PIM selector helps you with that.