| schedule 2 minute read | label Product Information Management

The high cost of small product data mistakes in retail


The smallest errors and gaps in information can have big repercussions.  While some data errors have been known to cause catastrophes, others quickly and silently eat away at your bottom line. 

While not as dramatic as a self-destructing satellite, we are talking about a lot of time and money disappearing into thin air simply because of incorrect or missing product data. 


The amount of data in a single supply chain has exploded over the past decade. Keeping everything perfect is impossible.

Don't think you're affected? The estimated and "accepted" error rate for data is around 30%. The consequences of bad or erroneous product data are astounding. Bad reviews, returns, delays in product launches, mis-categorized items....etc


The numbers

To get an idea of what we are talking about, check out these numbers:

 Other losses are not directly measureable like reputation and missed opportunities. 

Stop errors at the source

Or at least as close as you can get. Otherwise you will be dealing with expontential costs as the error moves down the supply chain.

The 1-10-100 rule states that errors multiply costs expontentially depending on which stage it is or is not corrected. $1 spent on prevention saves $10 on correction later on and avoids the expense of $100 worth of consequences down the line. This makes the case for prevention undeniable.

Make data quality governance a cultural imperative for your whole team so that you can stop even the smallest mistakes at the source. 

Check out this great SlideShare about getting started with a data culture.


Small mistakes, small business, big impact

A mis-categorization gone unnoticed could cause you to suffer sales losses, customer confusion, or flat-out rejection from retailers. 

Many smaller companies think they cannot justify the cost, both time and money, that comes with implementing standard operating procedures for data quality. The fact is that integrating a culture of data governance may seem like a headache at first, but it will save so much money down the road. 

Decide what data is crucially important to making your business work and then create standards that are easy for a team to implement. Some things to consider are:

  • What product data is essential for your customers to buy your products?
  • What format is required to get your products onto each of your sales channels? 
  • What tools do you currently use and how effective are they for your needs?
  • Do you maintain one finalized source for your product content?


While 100% accuracy is not a reasonable expectation, you can at least start to chip away at potential profit-eating errors by tackling the problem. 

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