Is poor data quality really that big of a problem? Surely not much harm can come from the odd misspelled surname or out-of-date phone number? Perhaps if you’re sending a birthday card to your mum and the postman can work what your poorly scribbled address is supposed to say. However, scale up these errors to hundreds of millions of customer data records and the issue becomes much more terrifying.
In 2016, research found that poor data cost the US economy $3.1 trillion. That’s enough to buy Apple and Google – with just about enough left over to wipe out the entire of America’s outstanding student debt. Put another way, it’s more than the UK and Thailand’s GDP combined.
The impact of poor quality data
Knowing how it is costing the economy as a whole is one thing. But how is bad data affecting businesses on a more individual level? A study from Gartner indicates that the average financial impact of poor quality data costs organisations $9.7 million per year.
Outside the US, a report conducted by Royal Mail suggests that it is a similar tale for businesses in the UK. Its research shows that around 6% of businesses’ annual revenue is being lost through poor quality data.
Yet, while the report also claims that retailers alone could save £500,000 a year through data cleansing, 34% of marketers still fail to fully understand the financial impact of poor quality data.
The bottom line effect on your revenue may be the ultimate headline figure, but the effects of poor quality data go beyond revenue. It also impacts in ways that may not be so obvious, but no less worrying.
For starters, there are the costs associated with resources, manpower and maintenance of data. One report points out that data workers waste 50% of their time finding and correcting errors, or attempting to confirm data sources they don’t trust.
Then there are issues it causes for the marketing department. For example, making misinformed or under-informed decisions, the failure of marketing initiatives, distorted campaign metrics and the creation of erroneous reports.
Customers, too, will respond to the use of bad data. This could be by unsubscribing from emails they feel to be inaccurately targeted and increasing churn rate, or even taking to social media to publically voice their dissatisfaction with your services. It could mean they never receive any of your communications to begin with.
With such high costs and undesirable consequences associated with bad data, it’s easy to appreciate how better management of your customer data can improve business and marketing performance.
The first place to start would be with a data quality audit, to assess the current condition of your customer and prospect database. Although not a ‘magic bullet’ to solve all your bad data woes (after all, the information in your database is far from static and needs frequent updating), it can help establish a trustworthy baseline from which you can set up a continuing data hygiene regimen to improve data quality. After all, your customers are going to be far less tolerant of your mistakes than your friendly postie…