In-Depth

Pitney Bowes Focuses on Customer Data

Pitney Bowes unveiled its new Customer Data Quality (CDQ) Platform, an all-in-one data profiling, data cleansing, and data enrichment suite.

• By Stephen Swoyer
• 11/22/2006

It was about this time last year that direct mail giant Pitney Bowes Inc. abandoned its courtship of the former Firstlogic Corp., in part because of unwelcome interest from federal regulators. That paved the way for the acquisition of Firstlogic by Business Objects SA, leaving Pitney Bowes—which is a tertiary business intelligence (BI) player at best—with only one best-of-breed data quality tool to its name—the assets it inherited from the former Group 1.

Fast forward to this November, as Pitney Bowes Group 1 Software unveils its new Customer Data Quality (CDQ) Platform, an all-in-one data profiling, data cleansing, and data enrichment suite. Pitney Bowes has also announced version 6.0 of its DataFlow data integration tool, touting improvements in metadata exchange, automation, maintainability, and usability.

CDQ is an intriguing deliverable, says James Kobielus, a principal analyst for data management with consultancy Current Analysis Inc. On the plus side, he notes, it enhances Pitney Bowes’ DataSight 4.0 data quality tool by introducing customer data integration- (CDI) and regulatory compliance-friendly improvements.

“[Pitney Bowes] has provided various incremental enhancements to its DQ and DI products, but has not introduced any compelling or differentiating new features into its product set,” Kobielus comments. “[It] has provided incremental enhancements that address various features requested by customers. It has synchronized enhancements to its DQ and DI products, signifying the importance that it places on evolving these products in tandem as critical components in support of master data management, customer communications management, and regulatory compliance.”

On the other hand, he stresses, CDQ doesn’t really pack any show-stopping new improvements. Its most significant new addition, in fact, is probably its new name—Customer Data Quality—which Kobielus says helps more clearly highlight its intended market. “[Pitney Bowes] has not included any compelling new features in its DQ and DI products that distinguish them from the competition,” he writes. If Pitney Bowes were a best-of-breed data integration player, instead of a mere tertiary power, it might be able to count on significant cross-selling of its DI products to help drum up sales for CDQ, Kobielus points out.

Nor has Pitney Bowes imitated the example of other recent data quality deliverables, he argues. “It does not provide tools that help business analysts to engage in profiling and other collaborative data stewardship tasks, in contrast to recent moves by data quality rivals Informatica and IBM. And, in the MDM arena, Pitney Bowes Group 1 is almost exclusively focused on customer data integration—profiling and enrichment of name/address records—but fails to address product information management [PIM] and other MDM use cases that require a deep focus on non-name/address data.”

By contrast, Pitney Bowes’ rivals—vendors such as IBM Corp., Oracle Corp., TIBCO, and NCR Corp. (with its Teradata subsidiary)—have introduced broad MDM product sets that address both CDI and PIM.

On balance, Kobielus calls it a wash. “Group 1 provides a best-of-breed DQ tool in CDQ Platform and, in [DataFlow], a respective ETL tool. However, the latest releases of both products are lackluster and fail to provide any compelling, differentiating new features,” he concludes.