Prerequisite: None

C. Lwanga Yonke

Founder and President

Padouk Consulting, LLC

This session will include a moderated Q&A featuring questions from the live audience.

Driven by the strong pull for harnessing large language models (LLMs) and generative AI, ensuring high-quality unstructured data is increasingly becoming a priority. While data quality methodologies for structured data are well established, similar methodologies for unstructured data are generally less mature and less common. In this talk, C. Lwanga Yonke will brief data and AI leaders on the strategic dimensions of managing the quality of unstructured data required by modern AI solutions.

Unstructured data—such as documents, emails, social media posts, images, and audio files—holds vast potential but poses unique challenges for businesses. This presentation explores the key similarities and differences in ensuring data quality for structured and unstructured data, emphasizing the business need and value of both. While structured data is highly organized and fits neatly into predefined models, unstructured data generally requires more sophisticated methods for extraction, interpretation, and validation.

Topics include:

• Definitions and characteristics of structured vs. unstructured data
• Key challenges in unstructured data quality
• The core dimensions of data quality of particular importance to unstructured data
• Role of metadata and context in unstructured data quality
• How the core components of structured data quality processes can be adjusted to address unstructured data

Attend this session and learn how to manage the quality of the key unstructured data resources that power your AI solutions.