Customer data integration and data quality

Customer data integration (CDI) can help organizations achieve a unified view of the customer, but data quality management comes with its share of challenges. Browse this collection of customer data integration and data quality news, tips and other resources to help you capture, manage and analyze customer data in your organization.

Customer data integration (CDI) can help organizations achieve a unified view of the customer, but data quality management comes with its share of challenges. Browse this collection of customer data integration and data quality news, tips and other resources to help you capture, manage and analyze customer data in your organization. 

Table of contents:
 

  • Understanding customer data integration and data quality
  • Customer data integration and data quality best practices
  • More customer data integration and data quality resources

     

     

      Understanding customer data integration and data quality  

    Customer data integration and data quality management is a continual process, says Filip Sanna, director of product solutions at Harte-Hanks' Trillium Software. In the past, many organizations had thought of customer data integration and data quality efforts as having an established starting and ending point, but this method proved insufficient, as customers and products change and companies merge (requiring CRM and data consolidation). Now, many organizations are adopting the notion of customer data management through data symbiosis, and are aligning their customer data integration, data profiling and data quality efforts simultaneously.

     

     

      Customer data integration and data quality best practices  

    According to Wayne Eckerson, director of research at The Data Warehousing Institute (TDWI), a successful data consolidation project requires these three things:

     

  • A mandate from the CEO to begin a data consolidation project
  • An enterprise-wide commitment to deliver consistent, reliable data
  • A substantial investment in time and money for the data consolidation project

    By following these guidelines, any organization can roll out a successful data consolidation project.

    Data quality management has its share of best practices as well. Maintaining clean data isn't always easy, but data quality tools and other techniques can be helpful. Expert William McKnight suggests approaching data quality management by making it the "absence of intolerable defects." In his data quality management tip, McKnight explains a few ways to handle data violations and improve customer data quality.

     

      More customer data integration and data quality resources  

    Visit the data quality learning guide.

    Find customer data integration news, tips and other resources.

    Get tips for using customer databases to provide good customer experiences.

     

This was first published in August 2008

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