Active Data Quality: Keeping Up with Customers Is Not a Static Process

Data Enrichment

Along with consumer demographics, in-depth business firmographics enable CRM data to be enhanced with detailed information such as company name, phone numbers, SIC codes, number of employees, total sales estimates, and contact title, name, address, and more. Active data quality’s enrichment processes can blend a range of other data sources to add these elements as well as geocoding and IP location data. This strengthens CRM initiatives by enabling better lead scoring and targeting—improving overall marketing, sales, and support efforts as well as fraud detection and prevention.

Data Matching

When all disparate representations of a record are resolved, the enterprise can effectively link together all touch points of customer data. This smart approach eliminates duplicate data by discerning the best information, selecting the surviving record based on level of quality of its information. For the global enterprise, this provides the means to attain and enhance a true 360-degree single view of the customer.

Embracing the Data Quality Opportunity

Because there is a vast range of data quality solutions that integrate directly with CRM systems, there is no reason to consider data quality as just a “nice to have” option.  It’s imperative, and the proof is in the 1-10-100 rule, which demonstrates the ever-increasing cost of bad data. It is estimated to cost $1 to verify customer contact information at the point of entry, $10 to implement a batch solution to cleanse and de-duplicate data after it is submitted, and $100 per record to do nothing—based on cost of wasted activities and lost marketing opportunities because the data is so bad it cannot be fixed.

Fortunately, data quality has evolved significantly over the past 20 years. Just as changes in decision support systems, data warehousing, and business intelligence now rely on deeper analysis of the data used to measure and monitor corporate performance, we now have higher expectations of what is meant by “quality information.” Today, the concept goes well beyond data cleansing, standardization, and enhancement, to include active contact data quality that supports KYC initiatives across a global customer base. And because of the variety of tools available, users can integrate ideal options that prioritize their individual data requirements. Almost every CRM system on the planet has some form of missing or incorrect data on any given customer, even as we know good data is good business.  However with the advance of active data quality, CRM users have a powerful opportunity to embrace data quality as the basis for their CRM strategy.

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Greg Brown is vice president of Melissa, provider of global contact data quality and identity verification solutions that span the entire data quality life cycle and integrate into CRM, e-commerce, master data management, and Big Data platforms. He has written on a range of data quality trends and challenges, with articles appearing in Target Marketing, Total Retail, Website Magazine, Database Trends and Applications, Health Management Technology, Customer Magazine, and more.