The majority of data quality software implementations have long been focused on customer data– more so than on other data domains like product data, financial data, asset data, location data, and so on. The focus on customer data helps explain why most data quality software techniques–the most common being data standardization, verification, and matching–were originally designed for customer data, whether built in-house by IT or built into a software vendor's tool. That's great for customer data, but not so good for other data domains. Customer-oriented data quality techniques and tools can be retrofitted to operate on other data domains, but with limited success. There's a need to redesign standard data quality techniques–and design new ones–that address the unique requirements of non-customer data domains.