Artificial Intelligence (AI) is only as effective as the quality, accessibility, and trustworthiness of the data that powers it. Many organizations face challenges with fragmented data sources, inconsistent definitions, limited interoperability, and insufficient metadata, making it difficult to fully realize the value of AI initiatives.

This hands-on lab is designed to help data professionals, architects, engineers, analysts, and business stakeholders understand and implement the foundational data management capabilities required for AI readiness. Participants will gain practical experience in preparing enterprise data through data integration, interoperability, metadata management, data quality, and governance practices.

Throughout the session, participants will explore how to connect and harmonize data from multiple sources, establish common data definitions, improve discoverability through metadata, and create trusted data assets that can be effectively utilized by analytics, machine learning, and generative AI applications.

Key Topics

• Data Integration for AI-ready environments
• Data Interoperability and standardized data exchange
• Metadata Management and data discovery
• Data Governance foundations for AI
• Building trusted and reusable enterprise data assets
• Preparing data products for analytics and AI use cases

Learning Outcomes

By the end of this lab, participants will be able to:

• Understand the critical role of data management in AI initiatives
• Identify data readiness challenges that impact AI performance
• Apply practical approaches to integrate and harmonize enterprise data
• Leverage metadata to improve data accessibility and understanding
• Establish governance mechanisms that support trusted AI outcomes
• Design a foundational framework for AI-ready enterprise data

This interactive session combines concepts, best practices, and practical exercises to help organizations move from fragmented data environments toward a trusted, interoperable, and AI-ready data ecosystem.