Pricing Dirty Data

Putting a Price on the Value of Poor Quality Data by Dylan Jones.

From the post:

When you start out learning about data quality management, you invariably have to get your head around the cost impact of bad data.

One of the most common scenarios is the mail order catalogue business case. If you have a 5% conversion rate on your catalogue orders and the average order price is £20 – and if you have 100,000 customer contacts – then you know that with perfect-quality data you should be netting about £100,000 per mail campaign.

However, we all know that data is never perfect. So if 20% of your data is inaccurate or incomplete and the catalogue cannot be delivered, then you’ll only make £80,000.

I always see the mail order scenario as the entry-level data quality business case as it’s common throughout textbooks, but there is another case I prefer: that of customer churn, which I think is even more compelling.


The absence of the impact of dirty data as a line item in the budget makes it difficult to argue for better data.

Dylan finds a way to relate dirty data to something of concern to every commercial enterprise, customers.

How much customers spend and how long they are retained, can be translated into line items (negative ones) in the budget.

Suggestions on how to measure the impact of a topic maps-based solution for delivery of information to customers?

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