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Data Observability: Definition, Key Elements, and Business Benefits

What is data observability?

Data observability is a comprehensive approach involving the continuous oversight of data well-being and usability.

As businesses increasingly depend on data across all operational facets, ensuring data quality and minimizing downtime has emerged as a critical endeavor. Companies cannot afford to allow erroneous data to persist unchecked; according to MIT, data issues are estimated to cost most companies 15-25% of their revenue.

Why is data observability important?

Data observability is crucial for efficient DataOps, which involves integrating people, processes, and technology to facilitate agile, automated, and secure data management.

It’s vital to note that data observability isn’t just one task or technology; rather, it encompasses a range of activities and technologies that collectively support operations. Key data observability activities include:

  1. Data monitoring
  2. Data alerting
  3. Data tracking
  4. Data comparisons
  5. Data logging

1. Data monitoring

Data monitoring involves ongoing surveillance of an organization’s data instances to uphold data quality standards consistently. Many businesses employ data monitoring software to automate certain aspects of this process and measure key performance indicators (KPIs) related to data quality.

Data monitoring vs data observability

While these terms are sometimes used interchangeably, they are distinct concepts. Data monitoring focuses on identifying predefined issues and presenting data in aggregated forms and averages. On the other hand, data observability entails adding contextual information to facilitate process adjustments and prevent issues from arising initially.

2. Data alerting

Data alerting complements data monitoring, whereby alerts notify users of any deviations from established metrics or parameters within a data asset. For instance, your monitoring tool might trigger an alert if a table, typically containing millions of rows, experiences a sudden 90% reduction.

3. Data tracking

Data tracking involves selecting specific metrics and events, then systematically collecting, categorizing, and analyzing these data points throughout the data pipeline for analytical purposes.

4. Data comparison

Data comparison entails examining related data to detect variations and similarities. When integrated with monitoring and alerting, comparisons can signal anomalies across datasets, alerting data users accordingly.

5. Data logging

Data logging is the practice of capturing and retaining data over a duration to facilitate analysis aimed at identifying patterns and forecasting future events.

Data observability vs software observability

Software observability was developed to enable DevOps teams to monitor the status of different systems, aiming to prevent downtime and anticipate future behavior. Similarly, data observability assists data teams in comprehending the state of data across their systems, minimizing data downtime, and preserving the integrity of progressively intricate data pipelines over time. The underlying principle of data observability is also linked to that of software observability, as both strive to establish systems that forestall issues with potential compounding negative impacts over time.

What are the pillars of data observability?

1. Metrics

Metrics encompass internal attributes regarding the quality of data, such as completeness, accuracy, and consistency. They serve as measures for evaluating both data at rest and data in transit.

2. Metadata

Metadata pertains to external attributes about data, including schema, freshness, and volume. When combined with metrics, metadata aids in pinpointing issues related to data quality.

Metadata and the hierarchy of data observability

The hierarchy of data observability, as coined by Databand, underscores the significance of metadata in achieving contextual understanding. Each level in this hierarchy serves as a stepping stone towards attaining deeper observability.

Level 1: Operational Health and Dataset Monitoring

This level involves fundamental metadata requirements regarding data at rest and data in transit. It provides visibility into operational and dataset health by assessing factors like timeliness of data arrival and pipeline performance impact.

Level 2: Column-level Profiling

At this level, data tables are audited to establish and reinforce rules at the column level. Delving into column-level details is crucial for ensuring data reliability and consistency.

Level 3: Row-level Validation

Building upon the foundational levels, this stage involves scrutinizing individual row values to validate accuracy and adherence to business rules. While often a primary focus for organizations, effective row-level validation relies on establishing robust groundwork through the preceding levels.

3. Data Lineage

Data lineage illustrates the origins of data and its evolution over time. Given the interconnected nature of datasets within an organization, lineage is essential for comprehending data dependencies and anticipating the repercussions of any alterations.

4. Data Logs

Data logs record details about data interactions with external environments. This encompasses interactions between machines (e.g., data replication or transformation) as well as interactions between machines and humans (e.g., creating a new data model or accessing data through a business intelligence dashboard).

Data observability benefits

Enhancing data team productivity is no longer merely advantageous; it’s imperative in today’s business environment for driving innovation and gaining a competitive edge. By minimizing data downtime and ensuring smooth operation of data pipelines, data observability empowers teams to be more efficient. Additional advantages of data observability include:

  • Early detection of data issues before they impact business operations adversely
  • Timely provision of high-quality data for business tasks
  • Heightened confidence in data integrity for critical business decisions
  • Scalable monitoring and alerting systems to accommodate business expansion
  • Enhanced collaboration among data engineers, scientists, and analysts

It’s essential to recognize that achieving the full benefits of data observability requires integrating all components and activities discussed throughout this article. Some organizations adopt a fragmented approach to data observability, where individual teams gather and share metadata only for the pipelines they manage. A unified metadata repository, such as the metadata lake featured in an active metadata platform, is pivotal for providing the entire organization with visibility into data and system health.

How does data observability fit into the modern data stack?

Data observability plays a crucial role in maintaining the enterprise data stack’s seamless operation, transforming it from a disparate collection of tools into a well-coordinated system. As the modern data stack continues to expand and evolve in complexity, data observability becomes indispensable for sustaining data quality and ensuring a consistent data flow across daily business activities.

The future trajectory of data observability remains intriguing, particularly regarding its potential integration into broader categories like active metadata. Consolidating all metadata within a unified platform offers numerous benefits, including facilitating various use cases such as data observability, cataloging, and lineage. Furthermore, an active metadata framework enables the development of programmable-intelligence bots, which, when coupled with open-source data observability algorithms, automate advanced DataOps processes tailored to specific data ecosystems and use cases.

Another emerging domain where data observability finds its place is data reliability engineering. Inspired by the DevOps concept of site reliability engineering, data reliability engineering emphasizes not only enhancing data observability but also establishing clear service level objectives (SLOs). These SLOs define target thresholds for data team performance, similar to the uptime and downtime metrics in site reliability engineering. For instance, an SLO could stipulate less than 30 minutes of downtime per 500 hours of uptime.


Data observability serves as a cornerstone for enhancing data team agility and productivity. The components and technologies encompassed within data observability, including monitoring, alerting, tracking, comparison, and logging, are essential for enabling data users to comprehend and uphold the integrity of their data effectively.