
This course, instructed by data management expert Irina Steenbeek, brings together knowledge from prior modules to build a comprehensive, integrated implementation plan for metadata management capabilities. It emphasizes the alignment and prioritization of data and AI use cases, identification of critical data elements and chains, and design of connected business and data models. Core deliverables include defining metadata requirements, documenting data lineage, establishing AI governance, and specifying tool requirements such as data catalogs, lineage tracking, and knowledge graphs.
Through hands-on exercises and real-world examples, participants learn to coordinate dependencies across multiple capabilities, enhance data quality, and ensure robust governance. The course also covers risk identification and adjustment of enterprise risk frameworks, providing a structured methodology for integrating organizational, technical, and strategic perspectives into a single actionable plan.
Course Highlights
Pricing and Credits
By the End of This Course, You’ll Be Able To:
Why Take This Course?
This course consolidates learning across the full program into actionable strategies. Gain practical expertise in integrating all metadata management capabilities into a cohesive plan that supports governance, quality, and AI initiatives.
Is This Course Right for You or Your Team?
This course is ideal for:
No technical experience is required. This course is designed for professionals looking to integrate core data, metadata, and AI management capabilities into a structured, enterprise-wide implementation plan.
This Course Includes: