What is Data Observability?

Data Observability Explained

Think of data observability like application monitoring, but for data. Just as DevOps teams use tools like Datadog or New Relic to monitor application health, data teams use data observability to monitor data health.

Without observability, data problems are typically discovered when a stakeholder notices something wrong in a dashboard—by which point the issue may have existed for days and impacted multiple decisions.

With data observability, teams can:

• Detect issues proactively before stakeholders notice
• Diagnose root causes faster with full pipeline visibility
• Prevent downstream impact by catching problems early
• Build trust in data through reliable, monitored pipelines

The Five Pillars of Data Observability

Data observability is typically measured across five key dimensions:

1. Freshness

Is the data up to date? Freshness monitoring tracks when data was last updated and alerts when tables go stale. A table that normally updates hourly but hasn't updated in 6 hours is a freshness issue.

2. Volume

Are row counts as expected? Volume monitoring tracks the number of rows in tables over time. A table that typically receives 10,000 rows per day but suddenly gets 500 may indicate a pipeline failure.

3. Schema

Has the table structure changed? Schema monitoring tracks changes to columns, data types, and table structures. Unexpected schema changes often break downstream transformations.

4. Distribution

Are values within expected ranges? Distribution monitoring uses anomaly detection to identify when data values shift significantly. A column averaging $50 that suddenly averages $5,000 indicates a problem.

5. Lineage

Where does data come from and where does it go? Lineage maps the flow of data through your systems, helping teams understand impact when issues occur and trace problems back to their source.

Data Observability vs Data Quality

Data quality and data observability are related but distinct concepts:

• Data quality focuses on whether data meets specific standards—is it accurate, complete, consistent, and valid?
• Data observability provides the visibility and monitoring needed to detect, diagnose, and resolve issues—including quality problems.

Think of it this way: data quality is a goal, while data observability is a capability that helps you achieve and maintain that goal.

Why Data Observability Matters

As data stacks grow more complex, issues become inevitable:

• Third-party APIs change without warning
• Upstream databases get modified
• ETL jobs fail silently
• Schema migrations break transformations
• Data volumes spike or drop unexpectedly

Without observability, these issues often go undetected for hours or days. The cost isn't just engineering time to fix them—it's wrong decisions made on bad data, recalled reports, and eroded trust in the data team.

Data observability shifts teams from reactive ("Why is the dashboard wrong?") to proactive ("We detected and fixed an issue before anyone noticed").

How to Implement Data Observability

There are several approaches to implementing data observability:

Manual Monitoring

Writing SQL queries to check freshness, row counts, and null rates. This works for small teams but doesn't scale—and provides no automatic alerting.

dbt Tests

If you use dbt for transformations, you can add schema tests and custom tests. However, these only run when dbt runs, missing issues between runs.

Data Observability Platforms

Dedicated tools like Sparvi, Monte Carlo, and Bigeye provide automated monitoring across all five pillars. These tools use data profiling and machine learning to detect anomalies without manual configuration.

Getting Started with Data Observability

If you're new to data observability, start small:

1. Identify critical tables—the ones that feed important dashboards or reports
2. Monitor freshness first—stale data is easy to detect and has clear business impact
3. Add volume monitoring—catch partial loads and failed pipelines
4. Expand to distribution—use anomaly detection for deeper coverage
5. Track schema changes—prevent silent transformation failures

Frequently Asked Questions

What is data observability?

Data observability is the ability to understand and monitor the health, state, and behavior of data across your entire data stack. It provides visibility into data pipelines, quality issues, and anomalies before they impact business decisions.

What are the pillars of data observability?

The five pillars of data observability are: Freshness (is data up to date?), Volume (are row counts as expected?), Schema (has the structure changed?), Distribution (are values within normal ranges?), and Lineage (where does data come from and where does it go?).

How is data observability different from data quality?

Data quality focuses on measuring whether data meets specific standards (accuracy, completeness, etc.). Data observability is broader—it's about having visibility into your entire data ecosystem to detect, diagnose, and resolve issues proactively, including quality problems.

Why do data teams need data observability?

Data teams need observability because data issues are inevitable as pipelines grow. Without observability, problems go undetected until stakeholders report wrong numbers in dashboards. Observability enables proactive issue detection, faster root cause analysis, and builds trust in data.