Data quality problems cost US businesses more than $600 billion a year.  These cost estimates would be significantly higher if lost revenue due to customer attrition and customer dissatisfaction were included.  Data errors cause the steady erosion of an organization’s credibility among customers and suppliers which has a lasting impact on an organization’s bottom line.  Our data experts experience first-hand the lasting impact of data errors while resolving data quality problems in a variety of US businesses.  Data errors occur in all industries and provided below are a few examples that our experts have encountered over the years.

1. An insurance company lost millions of dollars annually in mailing costs (postage, returns, collateral and staff time to process returns) due to duplicate customer records.
2. A financial services firm lost $1 million a year for 5 years and alienated its customers because it repeatedly recalled reports sent to subscribers due to inaccurate data.
3. A large bank discovered that 62% of its home equity loan payments were being calculated incorrectly with the principal getting bigger every month.
4. A regional bank could not calculate customer and product profitability due to missing or inaccurate data.
5. An online banking provider lost 5% of its customer base as they opted-out of receiving future solicitations from their provider because they repeatedly sent offers for products they already owned.
6. A large retail bank was fined by regulators for sending a credit card solicitation to the 5 year old daughter of a banking regulator. This happened because the birth date on the girl’s custodian account was corrupted during a transition.
7. A major bank holding company had to pay a heavy penalty because they solicited business from customers in a newly divested portfolio where the agreement was not to market to them for the next 24 months.
8. A regional bank was penalized by regulators because its comprehensive risk performance reporting did not match up with other reports submitted by the bank for the same time period. This was caused by the bank’s inability to reconcile its source system reports with its data warehouse reports.

Poor data quality causes organizations to experience 1) inefficient processes (extra time and resources required to reconcile data), 2) loss of credibility in the system or application, 3) lost revenue, 4) incremental costs, 5) customer dissatisfaction and 6) compliance challenges. An organization with good data quality practices can turn this weakness into a major competitive advantage.
 

Profit Technologies’ experts bring more than 30 years of real life experience in resolving data quality challenges. They bring industry best practices in process improvement, data governance, data consolidation and enterprise data quality. If your organization is experiencing data quality challenges, please visit us at www.profit-tech.com.