What is Data Observability?

What is Data Observability?

Data Observability is the ability to understand the health and state of your data systems by monitoring key indicators of data quality, freshness, volume, schema, and lineage. Just as software observability tools monitor application performance and alert engineers when something goes wrong, Data Observability tools monitor data pipelines and alert data teams when data is late, incomplete, malformed, or otherwise unhealthy.

The concept emerged from a simple reality: modern organisations depend on data for critical decisions, but the systems that produce and deliver that data are complex, fragile, and prone to failure. A pipeline might silently stop updating, a schema change in a source system might break downstream transformations, or a data quality issue might introduce errors that propagate through multiple reports and models. Without observability, these problems go undetected until a business user notices that a dashboard looks wrong or a model produces unexpected results — often hours or days after the issue began.

The Five Pillars of Data Observability

Data Observability is commonly described through five pillars that together provide a comprehensive view of data health:

1. Freshness

Is the data up to date? Freshness monitoring tracks whether data has been updated within expected timeframes. If a table that normally updates hourly has not been refreshed in three hours, something is wrong. Late data can lead to outdated reports and stale model predictions.

2. Volume

Does the data meet expected quantity thresholds? Volume monitoring tracks the number of rows, records, or events flowing through the pipeline. A sudden drop in volume might indicate a source system outage, a broken ingestion pipeline, or a filtering error. A sudden spike might indicate duplicate data or an upstream change.

3. Schema

Has the structure of the data changed? Schema monitoring detects changes in table structures, column names, data types, and field additions or removals. An unannounced schema change in a source system is one of the most common causes of pipeline failures.

4. Distribution

Are the values in the data within expected ranges? Distribution monitoring tracks statistical properties of data fields — averages, medians, standard deviations, null percentages, and value distributions. If a field that normally ranges from 0 to 100 suddenly contains values of 10,000, something has gone wrong.

5. Lineage

Where did the data come from and what depends on it? Lineage provides the context needed to understand the blast radius of a data issue and trace it back to its root cause. If a table has a freshness problem, lineage shows which upstream pipeline is responsible and which downstream reports and models are affected.

How Data Observability Works in Practice

A Data Observability platform typically operates as follows:

Automated monitoring: The platform connects to your data infrastructure — data warehouses, data lakes, ETL tools, BI platforms — and automatically learns the normal behaviour of your data. It establishes baselines for freshness, volume, schema, and distributions without requiring manual threshold configuration.

Anomaly detection: Using statistical methods and machine learning, the platform identifies deviations from normal patterns. Rather than relying on static rules (e.g., "alert if row count drops below 1,000"), it adapts to seasonal patterns, growth trends, and natural variability in your data.

Alerting and notification: When an anomaly is detected, the platform sends alerts to the relevant data owners through email, Slack, PagerDuty, or other channels. Alerts include context about what changed, when it changed, and which downstream systems are affected.

Root cause analysis: When an issue is detected, the platform uses lineage information to help data engineers trace the problem to its source. Instead of spending hours investigating across multiple systems, engineers can see the full chain of events that led to the data issue.

Incident management: The platform tracks data incidents from detection through investigation to resolution, building an institutional knowledge base of past issues and their solutions.

Data Observability in Southeast Asian Businesses

Data Observability is increasingly important for ASEAN companies:

• Complex data ecosystems: Companies operating across multiple ASEAN markets often have data flowing from diverse source systems in different countries, each with its own quirks, maintenance windows, and reliability characteristics. Observability provides a unified view of data health across this complexity.
• Growing regulatory requirements: Data protection regulations across ASEAN require organisations to ensure the accuracy and integrity of personal data. Observability helps detect data quality issues that could lead to regulatory violations.
• Scaling data teams: As ASEAN organisations build out their data capabilities, observability prevents the common scenario where a growing number of pipelines and dashboards creates an unmanageable maintenance burden.
• Building stakeholder trust: In organisations where data-driven decision-making is still being established, data incidents that go undetected erode trust. Proactive observability demonstrates that the data team is in control and that data can be relied upon.

Data Observability Tools and Platforms

Several platforms provide Data Observability capabilities:

• Monte Carlo: A leading Data Observability platform that provides automated, end-to-end monitoring and anomaly detection across the modern data stack.
• Bigeye: Focuses on data quality monitoring with strong anomaly detection and alerting capabilities.
• Soda: An open-source data quality framework that can be integrated into existing pipelines for monitoring and testing.
• Great Expectations (open-source): A data validation framework that allows teams to define and test data quality expectations programmatically.
• Anomalo: Uses machine learning to automatically detect data quality issues without manual rule configuration.
• dbt tests: The dbt transformation tool includes built-in testing capabilities that provide a basic level of data observability within dbt-managed pipelines.
• Cloud-native options: AWS Deequ, Google Cloud Data Quality, and Azure Data Quality services provide monitoring capabilities within their respective ecosystems.

Implementing Data Observability

A practical roadmap for getting started:

1. Identify your most critical data assets. Start monitoring the tables, pipelines, and dashboards that your leadership team relies on for decisions.
2. Deploy automated monitoring. Use a Data Observability platform or open-source tools to establish automated freshness, volume, and distribution monitoring for critical assets.
3. Set up alerting channels. Route alerts to the right people through the communication tools they already use (Slack, email, PagerDuty).
4. Build lineage connections. Link monitoring to your data lineage to enable root cause analysis and impact assessment.
5. Establish incident response processes. Define how data incidents are triaged, investigated, and resolved, and track resolution metrics over time.
6. Expand coverage incrementally. After proving value with critical assets, extend observability to broader data infrastructure.