12 Guidelines for Success in Data Quality Projects


By filling out this or any DATAVERSITY® form, as applicable by local privacy laws, you agree to receive marketing e-mail notifications from DATAVERSITY, listed sponsors, and listed partners. Just click the “unsubscribe” or "Manage Your Email Subscriptions" link in any e-mail to unsubscribe.

Sponsored by:

About the Paper

From years of data quality initiatives, hundreds of case studies, and research by industry experts, a number of common data quality success factors have emerged. This paper discusses key characteristics of data quality initiatives and provides actionable guidelines to help make your project a success, from conception through implementation and tracking your ROI. Readers will learn how to:


            • Quantify the effect of poor data quality on the organization
            • Prioritize projects for faster ROI
            • Gain buy-in, from employees through senior management

Data Education for Business and IT Professionals