Improve the quality of your product data with Data Quality Rules
Product data: the backbone of any PIM system. Whether you are a retailer, manufacturer or wholesaler, the quality of your product data is a key pillar. For example, complete, accurate product information contributes to your customers' buying process, and poor product data negatively impacts manual operations and internal processes.
We often see organizations struggle with getting their product data in order. Fortunately, there is a solution to this: data quality rules.
What are data quality rules?
In PIM systems, you can use data quality rules to ensure the quality of product data. Think of simple checks like "Is the product name filled in?" to more complex rules like "If the product falls in category X, field Y must be filled. It is a powerful tool to make data quality measurable and to improve it.
A well-established set of data quality rules can make the difference between mediocre and excellent product data
Start small and expand
When our PIM Masters are working on a new PIM implementation, we ask our client what product features they want to monitor with data quality rules. The answer is often, "We want rules for everything!" While having all your product data in order is obviously a good goal, our advice is to start small. For these reasons:
- System performance: Too many rules can negatively affect the speed of your PIM system.
- Focus on concrete action: Data quality rules are only valuable if you act on them. An abundance of rules makes it cluttered, so actions fail.
But, how small is small?
It depends on the situation, but start with the following questions, for example:
- Are there legally required features? Think energy labels for electronics, allergens in the food industry or safety information for chemicals.
- Are there mandatory features for our publishing channels? For example, one or more fields required to publish on marketplaces like Amazon or Bol.com.
- Are there critical features that directly contribute to a better customer experience or higher conversion?
Once you have this foundation in place, then categorize the features by high, medium and low priority and pick them up in phases.
Actions make the difference
Data quality rules are only effective when you turn the results into actions. Two practical ways to address this:
1. Combine with workflows
By combining data quality rules with the product enrichment workflow within your PIM system, you enforce compliance with certain rules. Within a workflow, a department or person is responsible for enriching the product data so that it meets the data quality rules.
We often use this to check whether a product contains all the features needed to publish to the relevant publishing channel. This prevents problems when loading product data and avoids a separate process for fixing these problems.
2. Create insights with dashboards
Are the data quality rules not mandatory? Then you can display them, in addition to the mandatory rules, in a dashboard. You perform the actions ad hoc or project-based, without them interfering with the progress of the regular business process. Again, it is important to set up the dashboard so that one department or person is responsible for the actions. This way everyone gets their own dashboard and it is clear where optimizations can still be made.
Better small and effective, than big and cluttered
A well-established set of data quality rules can make the difference between mediocre and excellent product data. Start with a focus on the most important characteristics and scale up when your processes are ready for this. This way you keep an overview, improve data quality step by step and get the most out of your PIM system.