Ensure unstructured data is accurate, consistent, trustworthy, and AI-ready, starting with enterprise documents.
Explore our Python library for assessing unstructured Data Quality, available in GitHub as a free open source project.*
Data Quality for Unstructured Data
Ensure unstructured data is accurate, consistent, trustworthy, and AI-ready, starting with enterprise documents.
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.
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.
Why Data Quality for Documents 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 automated Data Quality checks for unstructured data, errors and inconsistencies in enterprise documents are usually undetected and unnoticed at scale.
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.
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.
Explore Open Source Unstructured Data Quality
Lightup’s open source project supports documents in Amazon S3, expanding to Google Drive, Box, OneDrive, and more.
Get Started Today
- Connect an LLM and S3 bucket to your project.
- Use our open source Python library to evaluate the accuracy and reliability of documents, including PDFs, text files, and markdowns.
- Run computational checks: Inconsistency, Incompleteness, Inaccuracy, and PII contamination.
*Unstructured Data Quality with Anomaly Detection, Alerting, RBAC, and other enterprise features is available for customers and trial users only.