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Is Inaccurate Data Damaging Your Business and Your Bottom Line?

68% of companies recognize that data quality issues are an obstacle to digital transformation (source: Experian), while companies with “high-quality data management” generated 66% more revenue than companies with insufficient data quality strategies (source: SiriusDecisions). For businesses, the negative impacts of poor data quality include productivity loss, operational expense, reputational damage, and missed marketing, sales or relationship-deepening opportunities. For customers, poor data quality often makes it difficult to complete processes designed for self-service.

February 17, 2021
Prove
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68% of companies recognize that data quality issues are an obstacle to digital transformation (source: Experian), while companies with “high-quality data management” generated 66% more revenue than companies with insufficient data quality strategies (source: SiriusDecisions). For businesses, the negative impacts of poor data quality include productivity loss, operational expense, reputational damage, and missed marketing, sales or relationship-deepening opportunities. For customers, poor data quality often makes it difficult to complete processes designed for self-service.

With so much at stake, are you taking the appropriate actions to protect your company against the pitfalls of inaccurate data? And are you in-the-know about new technologies that can help your company reap the revenue benefits of solving these issues?

Take 3 minutes to read our newest white paper to understand:

  • The root issues that cause poor data quality
  • How a leading company saw $36 million in annual ROI by improving data quality using a single Phone-Centric Identity™ solution to improve customer contactability, append missing customer phone numbers, and authenticate customer identities during digital servicing
  • How fixing the root cause of data quality challenges enables companies to increase revenue, reduce OPEX, and mitigate fraud

Download the white paper (3-minute read):

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