What is “100% Data Quality”?

Isn’t your goal “100% Data Quality”? And don’t you expect (at least if you are  a retailer) your suppliers to deliver via GDSN 100% correct item data?

But what does this simple term “100% Data Quality” really mean?

Imagine that you are a retailer. You are receiving item data from your suppliers via GDSN. You are feeding that data directly and exclusively into your warehouse system. And your warehouse is fully automated so you are really dependent to receive from your suppliers the correct measurements. So you are very proud of your warehouse logistics and you are managing that in the most efficient manner which gives you serious benefits over your competition. But because you are so focused on efficient logistics you have not yet implemented any ecommerce strategies and therefore you have very little demand in any additional product information like nutritional information or things like that.

So for you “100% Data Quality” does mean – get me the measurements and my other logistics information (like packaging hierarchies) correct!

Now imagine you are a e-commerce retailer. What you need to sell is good images and very detailed product feature descriptions. If you are selling food you need nutritional and allergen information. In some countries you need this already for legal reasons.

Now “100% Data Quality” means logistic information including measurements + feature information + images.

And now try to imagine what “100% Data Quality” means to a small supplier who is confronted with forms with hundreds of attributes to fill out. He is happy when he gets his data passed the damn data pool validations!

Got my point?

Data quality – and my examples so far only touched on the completeness of the data – is “relative”. It is dependent on the requirements of the recipient. If you are using the measurements in your systems and your business processes depend on that information, you need it to have “100% Data Quality”. If you do not use it you can still have 100% Data Quality for your purposes if the measurements are incorrect or even empty.

Same applies for all dimensions of data quality: correctness, completeness, in time delivery and validity.

Imagine the perfect world all retailer requirements regarding data quality are equal because they are fully using the full set of product information in all their processes. But in todays world implementation levels of retailers are different and therefore they today might have very different requirements depending on the business processes where they are using the data today.

My recommendation to retailers would be, give a clear communication and documentation to your suppliers what you consider to be “100% Data Quality” and build into your system validations to ensure that quality level including feedback for suppliers if their data does not meet those requirements.

To suppliers I recommend ask your retail customers what they understand under the term “100% Data Quality”. Then start implementation accordingly.