Mitigating the Costs of Poor Data Quality Using Analytics
Many businesses, regardless of size or industry, can be affected negatively by poor data quality through various measurable costs like customer attrition in addition to effects that are evident but more difficult to measure like organizational mistrust. Data existence, completeness, consistency, accuracy, validity and timeliness are all aspects of data quality that can be tested using IDEA Data Analytics software. Mario Perez, Audit Analytics Consultant for Audimation Services, will explain these concepts and demonstrate practical techniques that can minimize the many risks associated with poor data quality. He will demonstrate the steps to import, cleanse, format and profile data to extract maximum value and deliver meaningful insights efficiently.