The current generation of customer data integration (CDI) and master data management (MDM) software provides powerful tools for managing data quality. There is, however, still a significant human element to the overall quality of your data. For example, a wide range of people may be entering data about entities. Salespeople may enter identity and transaction data about leads and customers, customer service people may enter information about new individuals within customer companies as well as transaction data, and order entry people may enter customer identity information. Similarly, people throughout the enterprise may modify, update and enhance data for sales, marketing, customer service and billing needs. All this disparate data - created by many people over different time periods - can be aggregated and partially cleaned programmatically, but maintaining high quality data over time will require implementing the five keys to data quality.

Number 1: Establish data standards and definitions. Standalone data systems generally have implicit, and sometimes explicit, data standards and definitions. The users of the system have agreed on standards or definitions in order to make the system work. These standards and definitions are almost certainly not consistent across all the data systems within your enterprise. They need to be made explicit, and people across your organization need to agree to them. The last sentence sounds significantly easier than the actual practical experience. People resist change, and that includes changing practices about standards and definitions. One compelling reason for recruiting a strong executive sponsor as well as a cross-functional steering committee of senior managers for your project is to provide the leverage needed to make these changes.

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