Level: Beginner to Intermediate

Prerequisite: None

C. Lwanga Yonke

Founder and President

Padouk Consulting, LLC

Data governance for structured data and information governance for unstructured data have long been treated and managed as separate, parallel disciplines. But today, the emergence of large language models (LLMs) and retrieval-augmented generation (RAG) requires these two disciplines to join forces so organizations can unlock breakthrough insights from their data and information assets.

This course reviews the history of data governance and information governance, shows why traditional silos for data and information governance are no longer viable in the era of generative AI, and provides a framework for success. Students will learn specific steps for developing an integrated governance approach that delivers generative AI business outcomes that are more accurate, relevant, secure, trustworthy, effective, and compliant.

You Will Learn

• The differences between structured, semi-structured, and unstructured data, and their implications
• The definitions of data governance and information governance
• Where data governance and information governance intersect, and where they don’t
• How generative AI is reinforcing the need for critical synergy between structured data and unstructured data management
• The risks of misaligned or siloed governance approach in generative AI work
• The importance of data architecture, data classification, and metadata
• The important data quality dimensions for structured and unstructured data
• The fundamental principles common to data governance and information governance
• How to close the organizational gaps between internal data governance and information governance programs

Geared To

• Data governance and information governance leaders
• CDOs and CIOs
• Compliance officers
• Data architects
• Records management leaders
• AI projects leaders or sponsors
• Business, data, and IT leaders