AI Data Quality Challenge: Strategy + Ten Steps to the Rescue
Tuesday, November 17, 2026
04:45 PM - 05:30 PM
All Levels
As organizations accelerate the adoption of artificial intelligence, one challenge becomes impossible to ignore: AI can only be as reliable as the data behind it. Poor data quality can amplify bias, weaken trust, increase risk, and undermine the value of AI initiatives. Prioritizing limited resources becomes more relevant than ever. This session combines Marilu Lopez’s Data Strategy PAC Method, from Data Strategies for Data Governance, with Danette McGilvray’s proven Ten Steps methodology from Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information™, as a practical approach to addressing data quality challenges in the age of AI.
The session will connect business priorities to an effective data strategy and then to core data quality principles aligned with today’s AI governance needs. It will show how organizations must define business impact, assess data quality, identify root causes, improve processes, and build sustainable practices to achieve reliable AI outcomes. It will also celebrate the forthcoming Spanish edition of Danette McGilvray’s book, translated by Marilu Lopez and expected in November 2026, which will expand access to this important methodology for Spanish-speaking data professionals.
Marilu Lopez and Danette McGilvray will show a combined approach to:
- Explain why data quality is foundational to trustworthy AI.
- Describe what it takes to create an effective Data Strategy to drive Data Quality priorities.
- Connect data quality practices with AI governance, risk management, and business value.
- Identify common data quality issues that can affect AI outcomes.
- Build sustainable practices for quality data and trusted information
Marilu is the owner of SEGDA®, a firm focused on data management education and assisting organizations in defining their data strategies and implementing their data management operating models. Her recent book, "Data Strategies for Data Governance," translated into multiple languages, outlines a comprehensive and pragmatic approach that involves people from all levels of the organization. She is a founder and former president of the DAMA (Data Management Association) Mexico Chapter, a former DAMA Regional Coordinator for Latin America, the former chairperson of the DAMA International Presidents' Council, and the former Chapter Services VP. Currently, she serves as the DAMA International® Corporate Secretary. With over 30 years of experience in the financial sector working for one of Mexico's leading banks, she has dedicated more than 15 years to data management practices. Marilu holds a CDMP (Certified Data Management Professional) Master from DAMA International®, DCAM 3.0 certification from the EDMA, and a CIMP (Certified Information Management Professional) in Data Governance, Data Quality, and Information Management from eLearning Curve. Additionally, she is a TOGAF-certified professional, certified by The Open Group.
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, AI, and compliance. Danette guides leaders and staff as they connect business strategy to practical steps for implementation.
As president and principal of Granite Falls Consulting, Inc., Danette is committed to the appropriate and effective use of technology and addressing the human aspect of data management through communication, change management, and engaging with people.
Danette is the author of Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information™, 2nd Ed. (Elsevier/Academic Press, 2021), often noted as a “classic”, one of the “top ten” data management books, and used as a textbook in university graduate programs. Chinese and Japanese translations are available, with the Spanish translation underway.