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According to a new report from Validity, 76% of respondents characterize their customer relationship management (CRM) data quality as either “good” or “very good,” yet a staggering 44% of respondents estimated their company loses over 10% of annual revenue due to poor data quality. 

Which begs the question: do you actually know how healthy your company’s data is?

Maintaining high-quality data health remains a challenge for many businesses, and poor quality CRM data results in significant customer losses, sales deficits, and high employee turnover.

Pie graph indicating the disconnect between respondents' confidence in their data quality, and the actual harmful effects (including loss of revenue) due to their poor data quality. Statistics in these pie graphs are written throughout the text of this article.

Respondents’ unfounded confidence in data quality reveals a disconnect, indicating most organizations don’t have a true understanding of their data health today. Heightening this tension are the effects of COVID-19, with 79% agreeing data decay has accelerated at an unprecedented rate as a result of the pandemic. Additionally, data mismanagement often leads to dubious internal practices, with 75% admitting they often (33%) or sometimes (42%) fabricate data to tell the story they want decision-makers to hear. 


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The impact extends to employees too. CRM users are at their breaking point, with 64% saying they would consider leaving their current role if additional resources were not allocated to a CRM data quality plan.

Luckily, there’s light at the end of the tunnel – companies are planning to address these issues with increased budgets and full-time staff. In 2022, 57% expect their company to increase data management budgets, while 56% of those who do not currently have full-time employee(s) dedicated to CRM data quality have active plans to make hires in the next six months.

The report includes responses from more than 1,200 CRM users and stakeholders across the U.S., U.K., and Australia.

Read the full report by Validity.

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