AI Success Depends on Unstructured Data Quality

See why the quality of your document and files determines whether AI delivers real business results that impact the whole company.

“Your unstructured data quality is what ensures successful AI projects and enables measurable business value. Get key statistics, definitions, and an actionable roadmap to manage vast amounts of unstructured data at scale and learn how top performing organizations are turning scattered content into trusted, high‑impact AI fuel. ”

[ Overview ]

About the Brief

This IDC analyst brief, sponsored by Shelf, cuts through the hype and shows that enterprise AI outcomes, especially in GenAI initiatives, rise or fall on the strength of unstructured data quality. You’ll learn what unstructured data is, why its accuracy and freshness drive model performance and trust, and how leading organizations govern, enrich, and retrieve it at scale. IDC outlines core management considerations, classification and taxonomy, metadata and lineage, security and privacy, retention, and human‑in‑the‑loop quality controls, highlighting the crucial role of data teams in curating and maintaining unstructured data quality. The brief also covers modern architecture patterns, such as retrieval‑augmented generation (RAG) pipelines and vector search, as key approaches for integrating unstructured data in GenAI initiatives, and emphasizes leveraging technology advancements to improve unstructured data quality. The report closes with a practical checklist to start improving data quality today and a maturity path to stay ahead as volumes and regulations grow.

“With 90% of enterprise data being unstructured, companies can no longer ignore unstructured data management and quality. It is foundational to successful AI. ”

What’s inside

What unstructured data is and where it lives across your enterprise (emails, chats, documents, wikis, tickets, call recordings, images, files, databases), highlighting the diversity of formats and the challenges of managing data across these sources.
Why unstructured data quality is the leading indicator of AI accuracy, relevance, and risk, including how it influences hallucinations and retrieval. Unstructured data is often error prone due to inconsistent formats, making accurate data essential for effective AI outcomes.
Key statistics and benchmarks on enterprise unstructured data volume, growth, readiness, and the ongoing challenge of managing unstructured data issues while working to ensure data quality.
Management considerations: governance models, taxonomy and classification, metadata enrichment, deduplication and versioning, and end‑to‑end lineage, with a focus on identifying and addressing data quality problems that have been identified.

IDC Analyst Brief, Sponsored by Shelf, AI Success Depends on Unstructured Data Quality, Doc. #US52600224, September 2024

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