As AI agents become more powerful and widely adopted, enterprises face a new challenge: how to build them on a foundation of trustworthy, AI-ready data that includes both structured and unstructured data. Unstructured data introduces new complexity for organizations already contending with large and growing volumes of data that is often distributed and disconnected, and from more information sources. In this episode, Stephanie Valarezo, Program Director, Product, from IBM Data Integration, shares how organizations can simplify and scale the integration, access and governance of unstructured and structured data. Explore how IBM is simplifying the enterprise data stack by empowering teams to integrate structured and unstructured data, using batch, real-time streaming, or replication techniques, while extending governance beyond the data layer to the AI agents themselves. Whether you're modernizing legacy infrastructure, accelerating agent development, or building robust governance strategies, this session will give you a blueprint to: Unlock the value of unstructured data for enterprise-grade AI Accelerate data intelligence through built-in observability and governance Simplify your tech stack while improving trust and traceability in AI outputs Learn more about watsonx #sponsored   Register for free to be part of the next live session: https://bit.ly/3XB3A8b   Follow us on Socials: LinkedIn YouTube Instagram (Mavens of Data) Instagram (Maven Analytics) TikTok Facebook Medium X/Twitter

As AI adoption accelerates, unstructured data has emerged as a critical—yet often overlooked—asset for building accurate, trustworthy AI agents. But preparing and governing this data at scale remains a challenge. Traditional data integration and RAG approaches fall short. In this session, discover how IBM enables AI agents grounded in governed, high-quality unstructured data. Learn how our unified data platform streamlines integration across batch, streaming, replication, and unstructured sources—while accelerating data intelligence through built-in governance, quality, lineage, and data sharing. But governance doesn’t stop at data. We’ll explore how AI governance extends oversight to the models and agents themselves. Walk away with practical strategies to simplify your stack, strengthen trust in AI outputs, and deliver AI-ready data at scale.