PIM validation: Automatic plausibility checks for clean data
PRODUCT EXPERIENCE MANAGEMENT
Whether in e-commerce, industry or healthcare, poor data quality is one of the biggest killers of efficiency and revenue. Incomplete attributes, contradictory information or incorrect product details lead to:
- false information in online shops
- The complaint
- damage to reputation
- legal issues
The solution lies in automatic data validation directly in the PIM system. This ensures that only complete, consistent and correct data finds its way into catalogues, shops or sales channels.
Why manual checks are not enough
Many companies still check product data manually. This is time-consuming, error-prone and does not scale.
Common issues:
- Attributes are missing (e.g. dimensions, weight, material specifications).
- Required fields are not filled in.
- Values do not match (e.g. product width greater than height).
- Information contradicts between language versions.
A PIM system with automated verification mechanisms solves these problems before data enters distribution.
Technical approaches for automatic validation
1. Rule-based checks
- Mandatory fields: The system checks whether all relevant attributes have been filled in.
- Value ranges: Example: “Voltage must be between 110V and 240V”.
- Dependencies: If attribute A is filled in, attribute B must also be present (e.g. for food: list of ingredients + allergens).
- Format validation: Check whether codes (e.g. GTIN, EAN) have the correct structure.
➡ Advantage: Very reliable, easy to configure.
➡ Use: Ideal for standards and mandatory information.
2. Scripts & Automation
Custom validation logic can be mapped in scripts.
Example: A script checks whether all units of measurement are consistent. Automated reports highlight data errors and can trigger corrective workflows.
➡ Advantage: Flexible, tailored to individual requirements.
➡ Application: Particularly suitable for companies with special product logic.
3. AI-supported plausibility checks
- AI recognises patterns and anomalies in large amounts of data.
- Example: A product is listed as weighing “20 kg”, while all similar products weigh around 200 g → the AI raises the alarm.
- AI can also perform semantic checks: Do the text description and attributes match?
- Translation and consistency checks between language versions.
➡ Advantage: Detection of “soft” errors that rules cannot map.
➡ Use: Ideal for ensuring quality on a large scale – in headlines, metadata, blog articles.
Best practices in PIM projects
Iterative introduction: Don’t do everything at once – start with core tests and expand gradually.
Rules first, AI supplementary: secure basic logic (mandatory fields, formats) via rules, use AI for pattern recognition.
Integrate workflows: Don’t just display errors, block them in approval processes until they are corrected.
Creating transparency: Dashboards and reports on data quality help to clarify responsibilities.
Practical example: How it works with apollon OMN
The OMN platform allows data checks to be mapped on several levels:
- Validation rules for mandatory fields, value ranges, dependencies.
- Automated workflows prevent incorrect data from being published.
- Scripting options allow for customised checks – ideal for industries with complex requirements.
The result: error-free data, fewer manual checks, faster time to market
Conclusion: Quality assurance must be automated
In times of omnichannel, AI and growing data floods, manual data maintenance is a risk. Only those who automatically ensure data quality can remain competitive.
With automatic validation and plausibility checks in the PIM system, companies lay the foundation for:
- legally compliant communication
- consistent customer experiences
- efficient processes
👉 With apollon OMN, you can transform data chaos into reliable data quality at the touch of a button.
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