The 1-10-100 rule: The real impact of poor data
Data is an asset to any company – regardless of business. So, why is it that so many businesses fail to keep their data clean? With bad data leading to failed deliveries, poor communication with customers, missed opportunities and lack of compliance to name just a few examples, it is vital that businesses start to really assess the quality of their 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 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 a recent study, two thirds (66%) of businesses believe that address accuracy is critical to their business. However, 80% of businesses suggest that customers often don’t realise 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 to validate at the point of entry and processes to ensure accuracy through the data lifecycle 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.
You can test this approach with any key data item and calculate the impact on the down stream processes of poor data quality.
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