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DESCRIPTION:Click for Latest Location Information: http://dgiq-aigov2026.da
 taversity.net/sessionPop.cfm?confid=166&proposalid=16867\nAs organizations 
 rush to adopt enterprise AI, they face a critical roadblock: up to 80% of c
 orporate data lives in unstructured formats like documents, PDFs, and logs,
  completely untouched by traditional data quality tools. Generative AI mode
 ls are only as good as the data feeding them. Poor data quality in unstruct
 ured pipelines leads to hallucinations, compliance risks, and failed initia
 tives.\n\nThis session tackles the missing link in modern data governance&m
 dash;how to transition from structured data profiling to automated unstruct
 ured data quality engineering. Drawing from real-world enterprise framework
 s, we will explore how to establish automated metadata extraction, define m
 easurable completeness scores, and manage data lineage across unstructured 
 pipelines. Attendees will leave with a practical roadmap to transform chaot
 ic, unorganized text and files into high-integrity, trusted datasets, estab
 lishing the foundational data governance and reliability required to safely
  accelerate and scale enterprise-grade AI.\n\nKey Takeaways / Bullet Points
 : \n\n
 The Unstructured Gap: Why traditional data quality frameworks fail when app
 lied to LLMs and generative AI pipelines.\n
 Defining "AI-Ready" Metrics: How to quantify and measure data com
 pleteness, accuracy, and bias within unstructured text.\n
 Automating Governance at Scale: Leveraging modern metadata management and a
 utomated readers to profile unorganized files.\n
 Lineage & Compliance: Maintaining strict data traceability and complian
 ce frameworks from raw unstructured input to AI output.\n
 A Practical Implementation Blueprint: Step-by-step strategies to modernize 
 your existing data infrastructure (like Ataccama or Collibra) for unstructu
 red workloads.\n
DTSTART:20261117T120000
SUMMARY:Engineering Unstructured Data Quality: The Foundation for AI-Ready 
 Data
DTEND:20261117T124459
LOCATION: See Description
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