Automating data quality for unstructured data with Ataccama and Snowflake Document AI

A new approach to trust in the most chaotic corner of the enterprise 

The data quality blind spot

Most data teams have mature workflows for quality-checking structured data. Tables in the data warehouse are routinely profiled, scored, tagged, and traced. However, significant gaps remain when it comes to unstructured data such as PDFs, contracts, invoices, policy documents, and quarterly reports. This content often exists in a separate realm- outside pipelines, beyond governance, and largely devoid of visibility.

Unstructured data now constitutes the majority of enterprise information and is growing rapidly, with IDC estimating a 55% annual increase. Yet 95% of organizations report that it remains the hardest data type to manage, use, or trust. Much of this information, despite its strategic value, remains locked away in static storage.

The nature of the black box

The core challenge is not extraction. It’s what follows. 

While LLMs and OCR systems can effectively extract fields from documents, two persistent limitations remain: 

• The inability to validate outputs for accuracy and completeness 
• A lack of mechanisms to detect anomalies, inconsistencies, or broken relationships

Without a robust quality and governance framework, extracted content remains raw and unverified. It cannot be reliably used in regulated or production environments. This is the fundamental blocker that stalls many unstructured data initiatives before they progress beyond the proof-of-concept phase.

Automating the trust layer 

Ataccama approaches this challenge with a simple principle: unstructured data should be subject to the same governance rigor as any other enterprise source. That includes automated profiling, quality validation, lineage tracking, and policy enforcement.

Crucially, these capabilities must be scalable and integrated into the data pipeline without relying on custom scripts or manual reviews.

Process overview: 

1. Extraction
   LLM-based tools, such as Document AI, parse unstructured documents and generate structured outputs written directly to cloud tables. Manual tagging is no longer required.
2. Validation
   Ataccama ONE connects to the resulting datasets and applies quality rules, profiling, completeness checks, and semantic detection to ensure data integrity.
3. Governance
   The platform captures lineage, classifies sensitive fields, and preserves document metadata, including filenames and source paths, for full traceability.
4. Automation
   This entire process operates continuously, with no manual handoffs or operational workarounds.

What this unlocks

With automated data quality in place, unstructured data becomes a reliable source of insight and action. 

• Business intelligence and analytics
  A global manufacturer can extract renewal dates and pricing terms from thousands of supplier contracts. Instead of manual review, procurement teams assess contractual exposure and upcoming milestones in minutes, enabling faster decisions and stronger vendor management.
• Risk and compliance
  A regional insurer monitors policy documents for missing or non-compliant language. Quality rules flag issues before they escalate into audit findings or regulatory penalties, reducing risk and improving operational oversight.
• AI enrichment
  A financial institution integrates validated data from earnings reports and ESG disclosures into generative AI pipelines. Because the data meets internal governance requirements, it can be confidently deployed in production environments.

These are not theoretical applications. They reflect a growing imperative to treat unstructured data as production-grade infrastructure, not peripheral content.

Why now

The technological landscape has reached an inflection point.

LLMs can now extract meaningful structure from complex, messy documents. Ataccama provides the controls required to validate and govern that content at enterprise scale. With cloud-native platforms such as Snowflake, this entire process can be executed in place, without data movement or complex integrations.

Separate workflows for structured and unstructured data are no longer necessary. A unified trust layer, automated, continuous, and embedded, is now within reach.

Get started today

The Ataccama and Snowflake integration is available now via Snowflake Marketplace. See it in action at Snowflake Summit, June 2-5, 2025, or connect with our team to explore how we can help you unlock value from unstructured data with trust built in.