Moving customer data quality upstream

Companies should move data quality controls "upstream" to save money, retain customers, and mitigate clean-up efforts, one expert says.

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"Going straight to the source" is an old idea with new meaning in customer data management.

With an ever-increasing amount of data coming from third parties and service providers, and more data sources than ever before, customer information quality is suffering, according to Aaron Zornes, founder and chief research officer for the CDI Institute, a San Francisco-based analyst firm. Solving customer data quality problems means dealing with issues "upstream" -- close to the source -- as well as making use of time-tested data governance techniques, he said.

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"The more sources you have coming into a stream of customer data, the more likely you are to get some pollution," Zornes said. "If everybody just pollutes a little bit, it adds up to a whole lot in the end. Ideally, you want to stop it or catch it at its source."

Zornes said that quality and accuracy issues should be addressed before data is entered into a customer data integration (CDI) hub or data warehouse.

"If you move data quality upstream and embed it in the [business] process, it's much better than trying to catch [flawed data] downstream and then fixing it in all the different applications that used it," Zornes said.

The best source for customer data is often the customers themselves, who can be included in the data quality process through Web self-service tools or service kiosks, he said. Another way to ensure accuracy is to train customer service representatives to update customer information. This method is often desirable because customers using Web self services may not have an interest in providing all of the up-to-date information that the service provider cares about, Zornes said.

Zornes explained that a hidden cost of bad data can be flawed interactions with customers -- or worse, loss of customers caused by frustration with the service provider's lack of accuracy.

Ultimately, data governance is critical for any company managing customer data from multiple sources, Zornes said. Data governance is the practice of assessing data ownership and operational requirements on an enterprise level. It's especially relevant today because customer data is generally stored throughout an organization's various business units, each with different operational needs and dissimilar systems. Zornes said that data governance is more about politics than systems, and that makes it a tough, yet worthwhile, issue to address.

The idea of data governance is to define who "owns" each aspect of customer data versus who are stewards or consumers of the information, Zornes said. Data governance also covers the definition of business rules for data. For example, it covers what happens when an address change comes in differently from three sources. Data governance sets up the rules for which of those sources is the most trusted for that information. As data becomes more distributed, data governance becomes even more important and, Zornes said, it should never be an afterthought.

Zornes is often asked whether data governance issues should be resolved before data is integrated, or vice versa.

"They're co-dependent in the good sense of the word," he answers. "They have to evolve together continuously."

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