Dirty Little Data Secret: CRM's Real Truth Requires Enterprises to Clean Up-Reorganize-Customer Information with Data Integration Solutions, Web-Services Technology That Integrates Data Applications

Article excerpt

DESPITE CRM'S RAPID EVOLUTION AS A BUSINESS PROCESS standard, some companies remain vexed by its frequently fuzzy ability to identify a customer at the point of interaction. While the days of messy CRM experiences like integration flameouts and legacy system nightmares have receded for the most part, myriad enterprise systems housing variations of duplicated, incorrect, and/or unusable customer data still frustrate enterprises of all sizes. Customer data integration (CDI), however, is succeeding where CRM has failed, and is helping to make good on CRM's promise.

"The dirty little secret about CRM is, it's become another group of legacy systems within the enterprise," says Jill Dyche, a partner in and cofounder of Baseline Consulting. This isn't to say that CRM has failed in its bid to unite sales, marketing, and customer service, but its success has revolved around unifying business processes. Data quality was an afterthought and relegated to point solution implementations, says Evan Levy, Dyche's partner and cofounder at Baseline Consulting. "The old sales pitch that CRM was the single version of the truth implied a unified source of customer data, enterprisewide," Levy says. "That hasn't happened."

With customer data distributed on average across 20 to 40 systems, companies understand the need to consolidate data via a unified hub to work for that single version of the truth. This realization has led to hard dollars, because despite its relative immaturity, the CDI/MDM market is growing at a dramatic pace. By 2008 the market for CDI solutions will exceed $1 billion, up from $680 million in 2006, according to Gartner, while IDC projects the overall MDM market to exceed $10 billion by 2009. This growth, however, is being hindered by uncertainty on the part of enterprises as they look to understand the cultural changes associated with using CDI and comprehend how it fits into master data management (MDM).

THE CDI DIFFERENCE: IT'S BAKED IN

CDI differs from previous data quality tools, and learning how CDI can improve customer relationships is key--again, it's important to note that previous data quality solutions haven't failed, but their success has been limited. "They've missed the big picture," says Navin Sharma, director of product management, global data quality, at Pitney Bowes. "Data quality became an after-the-fact term."

Data quality initiatives used to come into play at the application level and were specific to certain sets of functions like address standardization. As a result, the rules defining how to standardize customer data were specific to the needs of that application and/or department. The results were heterogeneous collections of customer definitions and identifying attributes relevant to a particular department, or group of departments, within the enterprise. "It's a matrix of relationships that ultimately boil down to one particular individual, "Dyche says. "Even companies with great CRM and data warehousing are struggling with this."

Data warehousing; enterprise information integration (EII); extraction, transformation, and load (ETL) tools--(see "Re:Tooling" on page 44 for an ETL tools review); and operational data stores (ODS) address these problems, and for most companies they have become the de facto remedy for the classic definition of CDI. But there are differences.

With a data warehouse or ETL tool, data integrity and validity are optional. The information a data warehouse collects could be good or bad, depending on the environment. Unlike warehouses, CDI contextualizes the data; it understands the concept of address, and is thus capable of identifying where and how transactions and data are related to a particular individual. A CDI hub standardizes any information about the customer by recognizing, comparing, matching, and reconciling customer data across disparate systems according to predefined rules. With CDI, data standardization and correction is "baked in," Levy says. …