What data observability is and why it’s urgent
When data breaks, AI models make wrong predictions, dashboards show incorrect figures, and leadership teams make decisions on information that doesn’t reflect reality. The problem isn’t always visible right away: a pipeline can run without technical errors while serving stale data, anomalous values, or duplicate records.
Data observability is an organization’s ability to understand, in real time, the health of its data across its whole lifecycle: from the sources to the AI models and the final dashboards.
Organizations with data observability in place catch pipeline failures far sooner than those that rely on end users to report problems, according to comparable implementations documented in the industry.
The five pillars of data observability
The industry’s reference definition sets out five dimensions any data observability system should cover:
1. Freshness: when was the data last updated? Does the sales dashboard reflect today’s transactions or those from three days ago? An observability system detects when a table hasn’t been updated within the expected window.
2. Volume: is the data arriving in the expected quantity? A sudden drop in a table’s record count can point to a problem in the source, the ingestion pipeline, or the upstream system itself.
3. Distribution: are values within normal ranges? It’s essential to catch a “price” field with negative values or an “age” field with values of 999 before that data reaches an AI model or an executive report.
4. Schema: has the data structure changed without warning? A data type change or a renamed column in a source system can silently break entire downstream pipelines.
5. Lineage: where does each piece of data come from, and which systems or models depend on it? When something goes wrong, lineage lets you identify within minutes which reports, models, or processes are affected.
The main tools
Monte Carlo: a leading ML-based data observability platform that detects anomalies automatically, without your having to define every rule by hand. It integrates with the main data warehouses and cloud platforms.
Great Expectations: an open-source framework for defining and running data quality test suites. Very flexible and well suited to teams with development capacity.
dbt tests: dbt includes native testing (uniqueness, not-null, accepted ranges, referential relationships) that runs as part of the data transformation process. Ideal for teams already using dbt.
Soda: a platform that combines declarative testing with continuous monitoring and alerts, with a gentler learning curve than Great Expectations.
Data observability for AI models
When data feeds machine learning models, observability gains a critical extra dimension: data drift. Models are trained on historical data, but if the distribution of production data shifts away from the training data, the model’s predictions degrade even though the technical pipeline runs without errors.
A well-configured data observability system monitors not just the technical health of the pipeline but also the statistical properties of the data feeding each model, and raises an alert when the distribution drifts from expected behavior.
Implementation
Data observability doesn’t require replacing your existing infrastructure. Modern tools integrate with the data systems you already have — BigQuery, Snowflake, Databricks, SQL Server, dbt — and start delivering value within the first few weeks.
The standard process starts with an inventory of critical pipelines, defining data SLAs (which data needs to be updated and how often), and rolling out monitors progressively, in order of criticality.
Frequently asked questions
Are data observability and data quality the same thing? They’re complementary but different. Data quality defines standards and measures them periodically. Data observability monitors data continuously and proactively, catching deviations in real time before they hit the business. Observability includes data quality but also covers freshness, volume, schema, and lineage.
What happens when there’s no data observability and the data breaks? Most often, end users catch the error — an analyst who spots an impossible figure on a dashboard, or an executive who notices the numbers don’t add up. That kicks off a manual investigation that can take hours or days to find the root cause. All that time, decisions are being made on bad data.
Do we need data observability if our data team is small? Small teams, which don’t have enough people to watch every pipeline by hand, get more value from automated observability, not less. A team of two or three people can’t manually monitor dozens of pipelines and tables — they need the system to alert them when something breaks.