Article

Enterprise-wide customer data quality still elusive at most organizations

Jeff Kelly

Highly accurate, up-to-date data -- especially customer data – is one of the keys to maintaining strong customer relationships and ultimately growing revenue.

But, while most organizations acknowledge the importance of data quality, only around half of all companies have actually deployed data quality tools or started data quality initiatives, according to Ted Friedman, an analyst with Stamford, Conn.-based Gartner Inc.

Even among organizations that do use data quality tools – either commercial or homegrown – less than a third have deployed the tools enterprise-wide, according to Gartner.

The consequences are not trivial. Poor or siloed data can result in missed cross-sell and up-sell opportunities and can even alienate customers who have come to expect personalized interactions.


Don't miss the other installments in this data quality management guide
Managing data quality programs during a recession
Trends in the data quality market
Avoiding data quality pitfalls and using data quality tools for discovering new opportunities
Q/A: Identifying data quality problems with a data quality assessment
FAQ: Best practices/tips for data quality

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"Especially in the under-40 demographic, customers do expect a high level of customization/personalization from companies -- and this puts pressure on companies to deliver or risk losing their existing customers," Leslie Ament, managing partner at Lexington, Mass.-based Hypatia Research, said in an email interview.

Standardizing and normalizing [disparate customer data] is akin to having root canal surgery at the dentist.

Leslie Ament
Managing Partner Hypatia Research

So why do so few organizations use data quality tools for customer data enterprise-wide? The reason, according to some, is that most companies collect and store customer data in numerous data sources spread throughout the organization with no way to connect them.

Put another way, lacking a single view of the customer through a master data management (MDM) system or customer data integration (CDI) initiative, organizations lack any realistic way of applying data quality tools enterprise-wide. Their only alternative is to tackle customer data quality one department or database at a time.

"Many larger retailers have upwards of 10 different databases with different schema for collecting customer data," Ament said. "Standardizing and normalizing this information is akin to having root canal surgery at the dentist."

Navin Sharma, director of product management for global data quality at Pitney Bowes Group 1 Software, which recently released an enhanced version of its customer data quality platform, agreed. "Even when we talk to our customers, many of them are deploying our capabilities, but for specific needs [such as for marketing or compliance issues]," Sharma said.

"The issue [is] there is disparity in how data is stored and spread across the enterprise," he said. "The fact of the matter is most organizations still struggle to maintain that single view of the customer. From that perspective, the maturity as well as the adoption [of enterprise-wide data quality] is still very low."

Gartner's Friedman, in a 2008 Gartner report, said companies need to start thinking about data quality more broadly and in the context of enterprise information management and MDM initiatives. In fact, data quality is an integral part of any MDM program. With a single view of customer data achieved, the thinking goes, applying data quality standards to it is a much simpler task than attacking the problem one database or data warehouse at a time.

But lacking a full-blown MDM initiative, Friedman wrote, companies should still conduct an inventory of all the data quality tools being used through the enterprise and identify "ones that can be used broadly across the business to reinforce a uniform approach to data quality, as well as to reduce procurement and maintenance costs."

In some instances, companies with simple customer data quality needs can even get started with tools embedded in their existing applications, Friedman said in an interview. Most business intelligence applications include data transformation capabilities that can reconcile customer names, for example. But most organizations will need to invest in more specialized data quality tools for sophisticated tasks like data parsing and standardization.

Sharma, for one, predicts that adoption of customer data quality tools and their implementation across the enterprise, while still lagging, will eventually gain momentum as more and more companies recognize the advantage it can provide over competitors.

As Hypatia's Ament points out: "Using data to understand and respond to customers can make a huge difference in a crowded marketplace."


Don't miss the other installments in this data quality management guide
Managing data quality programs during a recession
Trends in the data quality market
Avoiding data quality pitfalls and using data quality tools for discovering new opportunities
Q/A: Identifying data quality problems with a data quality assessment
FAQ: Best practices/tips for data quality


 


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