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Improving data quality also requires investigating and fixing the data-collection processes. This may require programming changes to data-capture screens. For example, pull-down menus can be used instead of free-form data entry to guard against typos and data entered into the wrong field. Additionally, it requires that organizations train their customer service personnel on proper data entry procedures and data proofing so that they treat data with careful attention to detail.
But simple data entry training is just the start. Above all, organizations need to create a culture of data advocacy-treating customer data as a valuable asset and investing in it. Data advocacy starts at the top. Senior managers should promote the value of data to the entire organization, specifically highlighting the cost of poor data quality in hard numbers. Managers must also examine the customer service work environment to determine if workloads hamper data quality and if personnel are properly motivated to improve their processes and watch the quality of their own data.
The ultimate goal is to establish a comprehensive system for surveillance of new and existing data so that an organization can transform data into intelligence to make better business decisions. Proper data management begins the moment data is collected from a customer or prospect-whether on the web, over the phone or at any point of customer interaction.
Companies that work to achieve this will be able to treat customer data as a strategic asset, not as a possible liability.
Charles Chung is vice president of Information Intelligence for Experian, a global provider of data cleansing, integration and analytics services. He can be reached at email@example.com