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The 1-10-100 rule: The real impact of poor data

Rather than risk overestimating your data quality, a good idea is to follow the 1-10-100 rule. This useful rule was developed by George Labovitz and Yu Sang Chang in 1992 and is great for any business to follow in order to assess the impact of dirty data.

In phase one of the 1-10-100 rule, $1 equates to the amount it costs to verify data in the first instance. This is the cheapest and most effective way of ensuring you capture clean and accurate data. In phase two, the amount increases to $10 – a significant rise compared to the $1 in phase one. This $10 signifies the increased cost that incorrect data has on your business the longer you leave it.  In the third and final phase, the initial $1 rises dramatically to $100, and this figure represents the amount of money businesses will have to pay after doing nothing at all to clean their data.

Bad data leads to poor decisions, communication and efficiency, which can have serious impacts on your business. So, instead of overestimating the quality of your data, take a moment to consider the real consequences poor data may leave you faced with. Rather than paying a hefty sum trying to clean data further down the line, tackle the issue from the very beginning by validating at the point of capture.

In our recent study, we discovered that two thirds (66%) of retailers believe that  address accuracy is critical to their business. However, 80% of retailers suggest that customers often don’t realize that failed deliveries are due to them mistyping their own address. Data that is entered incorrectly at the beginning is certain to lead to numerous future issues.

So, using tools such as address lookup to validate addresses at the point of entry will ensure that not only will you save money in the long run, but will also help strengthen customer relationships, brand reputation and business efficiency.

Interested in learning more about the impact of data quality? Download our report Fixing Failed Deliveries: Improving Data Quality in Retail.


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