Bad Data Costing Companies A Fortune

Bad Data Costing Companies A Fortune

Bad Data Costing Companies A Fortune

The cost of poor data quality is tremendous. Estimated by IBM to be roughly $3 billion a year in the US alone, it costs organizations between 10-30% of their revenue a year. Subsequently, despite the promise of big data, just 25% of businesses are successfully using it to optimize revenue, while the rest are losing out on millions.

The sum of money IBM believes is being thrown away may seem unbelievable, but it makes sense when you consider how often data is used in everyday working practices, and the impact that wrong data could have a result. The primary cause of bad data is simple - data decay. Data decay is estimated to be as much as 70% in B2Bs. Using out of date data is like filling a competitive egg eater’s bowl up 70% with rotten eggs - while they might look right, if they don’t stay down then the outcome isn’t going to be pretty for anybody.

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Data is constantly decaying. Lives change every day - people move houses, they switch jobs, they change numbers, and their contact details change as a result. It can even happen that streets will get renamed and area codes change. As a consequence, email addresses change at a rate of about 23% a year, 20% of all postal addresses change every year, and roughly 18% of all telephone numbers change each year. If you’re not on top of these changes, your sales teams will not be calling the right numbers, your marketing teams are not sending campaigns to the right email addresses, and you do not have anywhere near the understanding of your potential reach that will enable you to reach clients. You’re wasting manpower and money that’s far better allocated elsewhere.

Another cause of bad data is corruption as it passes through the organization from the initial source to decision makers. In a recent interview with us, Vijay A D'Souza, Director of the Center for Enhanced Analytics at the US Government Accountability Office, explained that, ‘Regardless of the goals, it’s important to understand the quality of the data you have.

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