In the agentic era, AI systems don’t just read data, they act on it. An agent making thousands of decisions an hour inherits every flaw in the data beneath it. A duplicate record, a stale address, or a mismatched identifier no longer surfaces as a bad report. It compounds into bad actions at machine speed, and no one is reviewing each one before it lands. 

Data quality underpins trusted AI. AI-powered profiling, rule-based standardization, validation, fuzzy matching, and continuous monitoring help ensure agents work from data that is accurate, complete, consistent, and trustworthy across cloud and on-premises sources. Data quality automation continuously identifies anomalies, enforces quality rules, and supports remediation as data moves through the enterprise, rather than waiting for issues to surface downstream. 

See how modern data quality capabilities help detect, diagnose, and address data quality issues before they impact AI outcomes, and walk away with a practical blueprint for building trusted data foundations for AI and autonomous agents. 

Speaker: Taryn Stebbins, Community of Practice Director, Salesforce 

Taryn Stebbins helps customers develop data strategies that connect business priorities to data capabilities, identifying use cases that support regulatory compliance and analytics. 

She brings 20 years of experience in enterprise data strategy, governance, and master data management across the financial services, legal, energy, and aerospace industries. Her work has included building enterprise data models and governance frameworks, leading global data communities of practice, and driving multiyear data transformation programs, all focused on making data a trusted asset that supports risk management and responsible AI adoption. 

Website: https://www.informatica.com/