Data observability tools focus on diagnosing critical aspects of data quality and reliability. These aspects are often categorized into the five pillars of data observability, which provide a framework for monitoring and assessing data health.

1. Freshness

Freshness, or timeliness, measures how recent a dataset is. Up-to-date data is critical for making accurate decisions, as data quality deteriorates over time. For instance, customer details may become outdated due to address changes, new email accounts, or lifestyle shifts. Using stale data risks inaccurate analyses and poorer results.

2. Distribution

Distribution refers to the expected range or spread of data values. When data points fall outside the acceptable range, it signals potential quality issues. Monitoring data distribution ensures consistency and highlights anomalies that may need attention.

3. Volume

Volume tracks the flow of data through pipelines, ensuring consistency over time. Significant deviations in data volume—either surges or drops—can indicate issues with data ingestion or processing. This pillar helps maintain reliable data flow and alerts teams to potential pipeline problems.

4. Schema

Schema defines how data is structured, including fields, tables, and relationships in a database. Observing schema changes can uncover broken data caused by unauthorized or accidental modifications. By keeping schemas intact, organizations prevent disruptions to downstream processes.

5. Lineage

Data lineage traces the journey of data, capturing its source, transformations, and destinations. This pillar provides a comprehensive view of how data moves through systems and processes, enabling teams to pinpoint where and why issues occur. Lineage is essential for data governance, regulatory compliance, and ensuring data trustworthiness.

Beyond the Five Pillars

While the five pillars form the foundation of data observability, other critical factors also play a role:

• Data Quality: Ensures data accuracy, consistency, and reliability.
• Data Completeness: Verifies that all required data is captured and available.
• Data Security and Privacy: Safeguards sensitive information against breaches.
• Data Compliance: Ensures adherence to industry regulations and standards.

These additional concerns highlight the overlap between data observability and data monitoring, as both strive to achieve reliable, actionable data for organizations.