Data observability has emerged as one of the most discussed concepts in data engineering over the past few years, with platforms like Monte Carlo, Bigeye, and Acceldata building entire products around the idea. It's often described as "data quality monitoring," which leads to a common confusion: isn't that what data quality tools already do?
The distinction matters. Here's how to understand it.
What Is Data Observability?
Data observability is the ability to understand the health and state of your data systems in real time — detecting when something has changed, broken, or degraded before users notice it.
The term is borrowed from software engineering, where "observability" refers to the ability to understand the internal state of a system from its external outputs (logs, metrics, traces). Applied to data, observability means monitoring your data pipelines, datasets, and data warehouses continuously so that anomalies are detected automatically.
Sohovi tracks quality trends across runs and alerts you when a metric — null rate, duplicate count, score — moves outside its normal range.
A data observability system might alert you when:
- A table that normally receives 10,000 new rows per day receives only 200 (volume drop)
- A field that's never null suddenly has 30% null values (freshness or pipeline failure)
- A value distribution shifts significantly — the average order value jumps from $85 to $430 overnight
- A pipeline that normally completes in 20 minutes is still running after 3 hours
These are all signals that something has gone wrong — either in the source data, the pipeline, or the infrastructure.
What Is Data Quality?
Data quality is a measure of how fit your data is for its intended use — assessed against dimensions like completeness, accuracy, consistency, validity, uniqueness, and timeliness.
Sohovi profiles every column in your dataset for completeness and flags the exact rows where values are missing — free to try.
Data quality asks: "Is this data good?" Observability asks: "Did something change?"
The distinction:
- Data quality is often assessed at a point in time, against a known standard
- Data observability is continuous monitoring over time, detecting deviations from baseline
How They Work Together
Think of it this way: data quality is the goal; data observability is the monitoring system that tells you when you're no longer meeting it.
A data quality framework defines your standards: completeness must be above 95%, no duplicate order IDs, phone numbers must match the E.164 format. Data observability tools monitor your data continuously and alert you when those standards are no longer being met — or when patterns shift in ways that suggest they might soon fail.
Without data quality standards, observability tools don't know what to flag. Without observability, quality standards are only checked when someone manually runs a report or audit.
Do Small Businesses Need Data Observability Tools?
Dedicated data observability platforms (Monte Carlo, Bigeye, Anomalo) are designed for organizations with complex data pipelines, multiple data sources, and data warehouses producing hundreds of tables. For most small businesses, they're overkill.
What small businesses do need is the underlying principle: don't wait for a user to notice bad data. Build a process that catches quality problems proactively.
At a small scale, this might mean:
- Running a data quality check on every imported CSV before it enters your CRM
- Checking for significant changes in row counts or null rates before publishing a report
- Setting a recurring reminder to audit your most critical datasets monthly
The goal of observability — catching data problems before they affect decisions — is universal. The tool you use to achieve it scales with your data complexity.
The Bottom Line
Data quality and data observability are two parts of the same solution to the same problem: ensuring that the data driving your business decisions is reliable. Quality defines the standard. Observability detects when the standard is being missed.
For teams working with file-based data or simple databases, starting with strong data quality checks — completeness, validity, uniqueness — gives you most of the protection that enterprise observability platforms provide. Build quality checks into your workflow today, and add monitoring infrastructure when your data scale demands it.
