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Rules and dimensions

This document lists the data quality rules and classification dimensions available in Snowflake Data Quality Studio.

Predefined data quality rules​

During the private preview, Atlan provides a set of predefined data quality rules, including:

  • Blank & Null Checks

    • Blank count
    • Blank percentage
    • Null count
    • Null percentage

  • Freshness Metrics

    • Data freshness tracking

  • Statistical Insights

    • Average value
    • Minimum value
    • Maximum value
    • Standard deviation

  • Uniqueness & Duplicates

    • Duplicate count
    • Unique count

Data quality dimensions​

To provide better context and insights, Atlan classifies results into key data quality dimensions:

✅ Accuracy: Verifying correctness and reliability ⏳ Timeliness: Validating data freshness and latency 📏 Validity: Checking data formats and constraints 📋 Completeness: Measuring missing or incomplete data 🔗 Consistency: Maintaining data follows the same format and standards across datasets 🔢 Uniqueness: Verifying data records are distinct and free from duplicates