Regulatory Compliance Requires Metadata and Data Quality Programs

Ensuring data quality is a big part of regulatory compliance. Its importance cannot be understated. Poor data quality costs the typical company at least 10% of revenue, and 20% is probably a better estimate in the view of data quality expert Thomas C. Redman. According to software marketing and technology expert Hollis Tibbetts, “Incorrect, inconsistent, fraudulent, and redundant data cost the U.S. economy over $3 Trillion a year.”

The cost of poor data quality notwithstanding, high-quality data is crucial for complying with regulations. Think about it. If the data is not accurate, how can you be sure that the proper controls are being applied to the right pieces of data to comply with the appropriate regulations?

Good data quality starts with metadata. Accurate data definitions are required in order to apply the controls for compliance to the correct data. But what is metadata?

Metadata characterizes data, providing documentation such that data can be understood and more readily consumed by an organization. Metadata answers the who, what, when, where, why, and how questions for users of the data.

Metadata is required to place the data into proper categories for determining which regulations apply. For example, PCI-DSS applies to payment card transactions, HIPAA applies to healthcare data, and so on. Some data will apply to multiple regulations and some data will not be regulated at all. Without proper metadata definitions, it is impossible to apply regulatory compliance to data.

The next step is to ensure that the data, once accurately defined, is itself accurate. Imposing regulatory controls on the wrong data does no good at all. This raises the question “How good is your data quality?” Estimates show that, on average, data quality is an overarching industry problem. According to Redman, payroll record changes have a 1% error rate; billing records have a 2% to 7% error rate; and the error rate for credit records is as high as 30%.