Data Quality Strategies for mySAP CRM

  • by Steve Sarsfield, Product Marketing Manager, Harte-Hanks Trillium Software
  • June 15, 2006
Many companies starting CRM initiatives are not satisfied with the resulting customer views, citing distrust of the data in their systems. Data quality strategies help you resolve data issues that distort customer views. Find out tips for preparing, blueprinting, and implementing a solid data quality strategy.
Key Concept
Identifying inconsistencies and anomalies in your data can range from basic issues of syntax (formatting) to the more technically challenging semantic definitions of the data. A simple syntax issue could entail making to format all product codes in the same way, which may require a simple conversion process. The more complex semantic problems may require the intelligent interpretation of data within the appropriate context.

Many believe that CRM solutions offer the much-sought-after 360 degree view of customer relationships. However, even the best CRM technologies cannot generate clear and unified customer views on their own. They are constrained by limitations of the data within them. In CRM projects, if you factor in the number of systems — often 10, 20, or more — companies target for consolidation, the result can be a tangle of unreliable and often cryptic data.

For example, take the case of a major gas company that was cleaning up and consolidating customer lists from various CRM systems. Several lists contained references to LDIY, although it was unclear what this represented. Name fields contained it (e.g., John Smith LDIY) as well as address fields (e.g., 240 Main St. LDIY). The project manager considered discarding these extra characters, but wisely didn’t. Instead he brought the business users into the process and discovered that LDIY means “Large Dog in Yard” to a meter reader.

Since there was no other place in the system to put this information, meter readers added it where they could to the customer record. The company needed to maintain this information to prevent emergency room trips and workers’ compensation claims. As part of its data quality strategy, the company created a separate field in the customer database for this information.

The most common problem companies encounter when beginning a data quality project is understanding where to begin. At my company, we developed a data quality best practices guide for SAP implementations, based on past successes with SAP clients (Figure 1). I'll explain the first three phases of a data quality project (project preparation, blueprint, and implement).

Steve Sarsfield

Steve Sarsfield is the product marketing manager for Harte-Hanks Trillium Software and the author of many white papers and articles about data quality. He believes in the philosophy that “it’s all about the data," especially when it comes to making enterprise applications successful.

See more by this author


No comments have been submitted on this article. 

Please log in to post a comment.

To learn more about subscription access to premium content, click here.