> ## Documentation Index
> Fetch the complete documentation index at: https://www.datalegion.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Data Quality Overview

> How Data Legion ensures data accuracy through quality scoring, validation, and rigorous data standards across 188M+ person profiles and 71M+ company profiles.

Data Legion ensures accuracy through quality scoring, validation processes, and rigorous data standards across both person and company datasets.

## Key Quality Features

### Full Coverage

Person profiles span career history, contact information, and social profiles across 100+ data points. Company profiles span firmographics, workforce analytics, and growth signals across 50+ data points. See [Person Dataset Statistics](/docs/person-data/stats) and [Company Dataset Statistics](/docs/company-data/stats) for field-level coverage.

### Quality Scoring

Data points receive quality scores (high, moderate, or low) that help you filter and prioritize data. See [Quality Scoring](/docs/data-quality/quality-scoring) for details.

### Data Freshness Tracking

Records include freshness indicators such as `last_seen` dates and `current` flags on person contact data, helping you prioritize the most up-to-date information.

### Email Validation

We're working on validating deliverability for current professional (work) emails. `emails[].validated` indicates whether validation was performed, and `emails[].validation_status` (nullable) may be `valid`, `invalid`, `risky`, or `catch_all`.

### Production-Ready Quality

Higher confidence in data accuracy means fewer wasted outreach attempts, better match rates, and reliable data for mission-critical operations across sales, marketing, recruiting, investment research, and data science use cases.

## Learn More

For detailed information on each quality feature:

* **[Data Verification](/docs/data-quality/data-verification)** - How we verify data quality
* **[Quality Scoring](/docs/data-quality/quality-scoring)** - Understanding quality scores and how to use them
* **[Data Freshness](/docs/data-quality/data-freshness)** - How freshness tracking works and how to use current flags
* **[Data Standardization](/docs/data-standardization/field-formats)** - How data is normalized and standardized