Data Quality for Unstructured Data

Data Quality for unstructured data refers to the ability to monitor, measure, and understand the accuracy, consistency, completeness, and compliance of information that doesn’t fit into structured rows and columns in a data warehouse or database.

Accelerating the next phase of Data Quality democratization, we’ve open sourced our Python library to evaluate Data Quality for unstructured data — enabling documents to become part of your observable data ecosystem.

Explore Project on GitHub →

Why Document Observability Matters

Unstructured data — such as PDFs, Word documents, text files, and internal wikis — contains contextual business information and domain-specific details, critical for AI applications and large language model (LLM) training.

But without document observability, errors and inconsistencies in enterprise documents are usually undetected and unnoticed at scale.

Data Quality Issues Associated with Different Types of Enterprise Documents

AI Data Profiling for Documents

Lightup automatically generates data profiles for documents using AI, including:

Document metadata (type, size, creation date)
Content summary
5 auto-generated questions and answers (Q&A) capturing key facts

Profiles can be regenerated or manually edited as needed.

Unstructured Data Quality Metrics

Once documents are profiled, Lightup activates Auto Metrics to track their health and quality over time. Metric collection can be scheduled at custom or regular intervals in a specified time zone. Then, set alerts to notify your team of anomalies.

Document-Level Metrics

Inaccuracy — Detects changes in factual data.
Inconsistency — Highlights contradictions with side-by-side comparisons.
Incompleteness — Flags missing information based on original Q&As.
PII Contamination — Detects names, dates of birth, and sensitive data fields.

Folder-Level Metrics

Detect inconsistencies across documents.
Compare multiple documents in a folder for conflicting information.
Identify version discrepancies.

Custom Metrics

Lightup makes it easy to extract domain-specific facts using plain language prompts, such as creating a custom metric to track net income with a simple statement: “Extract net income from this report.”

Role-Based Access Control (RBAC)

Lightup provides enterprise-grade role-based access control (RBAC), enabling granular user permissions for all Workspaces: Admin, Editor, Viewer, or Observer.

Restrict access to PII Contamination metrics by user role.
Ensure compliance across teams and departments.

Role-Based Access Control for unstructured data

Make Documents AI-Ready

Documents contain rich context and domain-specific knowledge, critical for enterprise AI use. But only if your documents are accurate and trustworthy.

Training Domain-Specific LLMs

Use internal docs to fine-tune large language models for industry-specific results.

Retrieval-Augmented Generation

Feed real-time enterprise documents to AI for context-aware answers.

Automated Document Intelligence

Extract structured data from contracts, reports, or standard operating procedures (SOP) using natural language processing (NLP) and AI.

Resources

Open Source

GitHub Repository — Supports documents in Amazon S3 — expanding to Google Drive, Box, and OneDrive. Connect an LLM and S3 bucket, use the Python library to evaluate document accuracy, and run checks for Inconsistency, Incompleteness, Inaccuracy, and PII contamination.

Blog

Ensure Unstructured Data Is AI-Ready.

Start with our open source Python library or contact us to learn how Lightup can help your enterprise.

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