How important is data quality to businesses that rely on heavy direct mail campaigns? Very important—in fact, American businesses count losses up to 600 billion dollars per year, due to poor data quality. This may be the result of inaccurate information, typographical errors, or outdated resident addresses. Data quality is the starting point for most marketing projects. You can’t achieve successful results with inadequate, incomplete, or incorrect data. Data quality tools that validate contact names, addresses, telephone numbers and email addresses help enhance the quality of your data for direct mail, target marketing, and data-driven initiatives.

Understanding the key data quality dimensions is the first step to data quality improvement. Being able to segregate data flaws by dimension or classification, allows analysts and developers to apply improvement techniques using data quality tools to improve both the information, and the processes that create and manipulate that information.

Organizations rely on data quality tools to ensure contact records are complete and accurate – and prevent fraud and waste associated with bad contact data. Data quality tools can function in realtime on the front-end of the data collection process, acting like a data quality firewall, to prevent incomplete or inaccurate data from entering the database in the first place via web forms and call center order entry. Additionally, data quality tools can perform ongoing maintenance on the back-end by correcting and updating records in batch.

One of the misconceptions is that data quality can be achieved with a one-time action that results in perfection. A dynamic, not static process, is necessary to keep data from degrading over time; so any data quality initiative must include ongoing maintenance.

Data quality management is an ongoing process that should be applied consistently to ensure the long-term lifetime of your data for the highest return on investment (ROI). Ensuring accuracy of vital data quality involves finding and correcting errors, as well as validating current information, and then updating the record as needed through services like NCOALink® processing to find out if and where your customers have moved.

Data quality primarily refers to accurate customer information, including a valid address, name, email, and phone number. If you are looking for excellent data quality solutions, Melissa Data can help you get started with solutions—as well as programming tools—that can be added to your custom applications to capture, clean, and maintain quality contact data.