Data Quality Management in Practice
Instructors: Danette McGilvray, John Talburt
Course Overview
This course includes 5 sessions. Sessions 1-2, taught by John Talburt, cover the history and development of DQM methodologies, the ISO 8000 DQM reference model, the role of data governance, and a data quality (DQ) case study. Sessions 3-5, taught by Danette McGilvray, cover the methodology Ten Steps to Quality Data and Trusted Information™ (outlined in the book Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information™ 2nd Ed. (Elsevier/Academic Press) by Danette McGilvray. This methodology provides a practical approach to creating, improving, and sustaining the quality of data critical to any organization’s success.
Instructors
John Talburt
Director, University of Arkansas at Little Rock
John R. Talburt, PhD, IQCP, CDMP, is the Acxiom Chair of Information Quality and Director of the Information Quality Graduate Program at the University of Arkansas at Little Rock. Previously, he led the Data Research and Development group for Acxiom Corporation where he implemented their Total Data Quality Management program. He is a member of the U.S. Technical Advisory Group (TAG) to the International Organization for Standardization (ISO) in data quality, and Lead Data Governance Consultant for Noetic Partners. He is an inventor for several patents related to customer data integration and the author of numerous research papers on information quality and entity resolution. His books include "Entity Information Life Cycle for Big Data: Master Data Management and Information Integration" (Morgan Kaufmann, 2015), "Entity Resolution and Information Quality" (Morgan Kaufmann, 2011), "Data Engineering: Mining, Information and Intelligence" (Springer, 2010), and "Information Quality and Governance for Business Intelligence" (IGI Global, 2014).
Danette McGilvray
President and Principal Consultant, Granite Falls Consulting Inc.
An internationally respected expert, Danette McGilvray is known for her Ten Steps™ approach, used by multiple industries as a proven method for increasing the value of data through quality and governance. It applies to operational processes and focused initiatives such as security, analytics, digital transformation, and AI. Her book, Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information™ (Morgan Kaufmann/Elsevier), is used worldwide. Danette has worked with organizations across many industries and countries to help them understand and improve their data and the processes that create and use that data. She has been a speaker at numerous conferences and events, including the MIT CDOIQ Symposium.
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