Data Quality Pays
Build Your Customer Intelligence on a Sound Foundation
By Mark E. Atkins
Without QUALITY data, companies risk acting on misinformation and suffering financial consequences. PricewaterhouseCoopers' Global Data Management Survey 2001 found three-fourths of companies studied reported problems from poor data quality, including extra costs, failures to bill or collect receivables and lost sales. Therefore, it pays for companies to examine their data quality: what it is, the role it plays in creating customer intelligence, and how it contributes to a healthy bottom line.
How do companies obtain customer intelligence? When they are migrating legacy data to a CRM (customer relationship management) system, they need to aggregate or integrate data from diverse systems to produce a single, complete view of each customer across the enterprise. For true customer intelligence, this view must identify and include all relationships inherent in the data but which were previously unknown because the data resided in separate systems. For example, it must specify which products each customer owns and how many, which customers share a household, and which different businesses are subsidiaries of one company.
For CRM and other customer-centric systems, high-quality data mean accurate, complete data about customers, including all of their relationships with each other, products, locations and other entities across the enterprise. Only data that exhibit all of these relationships will produce valid information for accurate segmentation, profiling and targeted marketing.
The Value of Data Quality
To gather customer intelligence —and realize the full return on investment (ROI) of customer initiatives—companies need to match data components from diverse records to create enterprise-wide customer views. When companies view legacy data with this goal in mind, quality issues emerge, including diverse formats, inconsistencies such as name variations, missing data, data in the wrong fields and errors. These issues impede successful record matching, make it difficult to find duplicate records and prevent companies from gaining complete views of their customers and all their relationships.