What Is Data Observability?

Data observability is a holistic approach that automates the identification and resolution of data problems, thereby simplifying data system management and improving performance. Its goal is to enhance the reliability and credibility of insights derived from data, all while ensuring data availability. 

Comprehensively, it involves understanding and expertly managing the health and performance of data, pipelines and critical business processes. Data observability gives organizations a detailed view of their data ecosystem, providing insights into data consumption, meticulous protection and precise alignment with relevant policies and regulations.Safe, transparent and traceable

Why Does Data Observability Matter?

Organizations are accumulating vast amounts of data at an unprecedented pace. Critical to today’s business environment, data serves as the lifeblood of decision-making processes, fueling analytics, machine learning and business intelligence initiatives. However, the value of data is directly proportional to its availability and quality. 

This is where data observability tools come into play. Supported by robust data quality measures, data observability can be the difference between actionable insights and unreliable outcomes. It can act as a strategic imperative for organizations aiming to extract maximum value from their data assets. 

Data observability enables organizations to:

  • Identify and resolve data issues quickly
  • Optimize data availability, performance and capacity 
  • Ensure data quality and reliability
  • Mitigate risks and safeguard reputation 

How Data Observability Evolved Over Time

The term data observability is relatively new; however, the concept of data observability has been around for decades and has become increasingly important in the data-driven era. Data observability emerged as a response to the growing complexities of data-driven operations in the early stages. Early adopters recognized the need to monitor data pipelines for performance and data quality issues. However, as technology rapidly advanced, and organizations embraced a more comprehensive data-driven approach, the scope of data observability expanded. With big data, cloud computing and modern analytics, data observability evolved to encompass the holistic monitoring of data ecosystems, including data sources, transformation processes and business context.

Today, data observability is more than just a tool for detecting problems. Organizations use it as a strategic asset to ensure data reliability, compliance, security and operational efficiency. Here are some specific examples of how data observability is being used to improve business processes and outcomes:

  • A retail company uses data observability to identify and fix data quality issues causing inaccurate product recommendations.
  • A financial services company uses data observability to detect and prevent fraud.
  • A healthcare company uses data observability to identify and address trends in patient care. 
  • A manufacturing company uses data observability to optimize production processes and reduce waste.

As data grows in volume and importance, data observability will continue to evolve, adapting to new technologies, regulations and the increasing complexity of data landscapes.

The Role of AI in Data Observability

Artificial Intelligence (AI) is increasingly transforming the field of data observability by enabling a thorough understanding of data and creating more operational efficiency, reliability and security in data infrastructure. Here are a few examples:

Anomaly Detection: An integral part of the transformation derived from AI anomaly detection. Through machine learning algorithms, AI can identify unusual patterns or behaviors within large datasets that deviate from what is considered normal or expected. This capability to detect outliers can help to flag potential data quality issues, ensuring data integrity, avoiding skewed analytics and helping to prevent larger systemic problems. 

Automatic Resolution of Data Quality Issues: AI technology can help with the automatic resolution of data quality issues. By detecting inconsistencies or errors in the data, it can take the necessary steps to rectify these problems or notify users to review them. This process guarantees the dependability and accuracy of the data, which saves time and lowers the need for manual intervention.

Auto-Tuning Data Optimization: The use of AI technology has expanded to include data optimization through auto-tuning features. By analyzing historical performance metrics and data trends, AI can automatically adjust system parameters to achieve optimal performance. This not only enhances system efficiency but also reduces the need for continuous human oversight.

Auto-Scaling: AI can facilitate the seamless scalability of data operations through auto-scaling. This feature monitors the system demand and scales resources accordingly. This ensures the system always operates at the right capacity, thus optimizing infrastructure investment.

Criticial Components of Data Observability

There are three measurement components comprising data observability: metrics, logs and traces. These components are interrelated and collectively contribute to the observability of both data and systems. They offer insights into data health, quality, performance and dependencies. Let’s look at these components in more detail.

Metrics provide quantifiable insights into the health and performance of data, including data latency, throughput, error rates and data quality indicators. For example, monitoring patient records or diagnostic data accuracy in the healthcare sector ensures that healthcare professionals depend on reliable information for medical decisions. Metrics help organizations identify data anomalies, allowing for prompt issue resolution and maintaining high-quality data.

Logs provide a detailed record of data events, changes and interactions, essential for upholding data quality and capturing historical information about data processing. For example, transaction logs are used in the financial industry to maintain a chronological record of financial activities, enabling fraud detection and auditing. Logs are instrumental in pinpointing the root causes of data issues, helping organizations maintain data quality and trustworthiness.

Traces give organizations a detailed view of data flow and dependencies within complex data environments. They are essential for comprehending how data moves through a network of systems and processes. For example, a retail company uses a machine learning model to generate recommendations based on a customer's purchase history. The company traces the flow of data into the machine learning model. This allows the company to identify the data sources and how it was transformed, which are most important to the model's accuracy. Tracing also aids in understanding the interdependencies between different data sources and systems. Traces help organizations gain insights into the intricacies of their data ecosystem and improve data flow for optimal efficiency and effectiveness.

data observability lenses

Figure 2: The three lenses of data observability

Three core lenses, as shown in Figure 2, form the foundation of achieving the goals of data observability.

  1. Data. Focuses on monitoring and understanding the overall health of data, identifying and resolving data quality issues, anomalies and bottlenecks.

  2. Pipeline. Centers on monitoring and understanding the health of data pipelines, identifying and resolving performance issues, capacity issues and errors.

  3. Business. Emphasizes the monitoring and analysis of how businesses consume and use data, and pertaining to the identification and resolution of compliance, security and governance issues.

Capabilities of a Data Observability Tool

Let’s now explore some of the crucial features needed from a data observability tool to deliver on the above data observability lenses. 


Proactive detection of data quality issues and anomalies: Address issues preemptively by employing automated data quality checks and anomaly detection algorithms before they can impact downstream processes.

Alerts based on scorecard: Set up alerts based on predefined data quality scorecards. If data quality metrics fall below acceptable thresholds, alerts are triggered, notifying stakeholders of potential issues. This proactive approach can help ensure data remains reliable and fit for intended use without requiring constant manual monitoring.

Impact analysis: Understand how changes in data sources, schema modifications or data pipeline adjustments affect downstream processes and analytics with data lineage capabilities.


Observe infrastructure for critical jobs: Monitor the underlying pipeline infrastructure to ensure they function and perform properly.

Connection and integration observability: Monitor data connections and integrations to help identify and address connectivity issues, ensuring pipeline stability and that data flows smoothly between systems.

AI-powered self-heal, auto-tune, auto-scale: Leverage machine learning and AI capabilities to self-diagnose issues, auto-tune configurations for optimal performance, auto-scale resources as needed and intelligently shut down processes or systems during periods of inactivity. This automation reduces manual intervention and enhances the efficiency of data pipelines.


Automated delivery, fulfillment and observability with governed workflows in a data marketplace: Enable users to easily access and consume data assets while maintaining control and oversight over the data workflows.

Package and consume assets with governed data sharing: Help to ensure data sharing follows predefined rules and regulations, promoting data security and compliance.

Improve the Efficiency and Effectiveness of Data Pipeline Management with FinOps: Aid in monitoring and optimizing resource consumption, ensure efficient data processing and control costs associated with data pipelines, enhancing the financial effectiveness of data operations.

These expanded capabilities empower organizations to maintain data integrity, optimize resource utilization and proactively address data-related challenges.

Benefits of Data Observability

Is a data observability tool the right fit for your organization? Assessing the need for such a tool involves evaluating your data landscape's complexity, the data's criticality to your operations and your data quality requirements.

A data observability tool becomes increasingly valuable if your organization deals with diverse data sources and intricate data pipelines and relies heavily on data-driven decision-making.

In this context, data observability tools can help you to:

Gain visibility into your data landscape: See how your data flows through your systems and identify potential bottlenecks or problems.

Improve data quality: Identify and fix data quality issues, such as missing values, inconsistencies and outliers.

Reduce downtime: Catch and resolve issues quickly before they cause downtime.

Improve operational efficiency: Detect and fix operational inefficiencies, such as redundant jobs and inefficient data processing.

Reduce costs: Collect data-related metrics and performance indicators, enabling organizations to monitor resource consumption, such as computing and storage, and accurately attribute data-related costs to specific departments or projects.

Evaluating Data Observability Tools

Observing data pipelines alone is not sufficient; while monitoring and optimizing them are crucial for smooth data flow, they represent just one part of the broader data observability concept. Data observability encompasses the technical aspects of data movement, its quality, usage and impact, requiring a multidimensional approach for comprehensive coverage in the data ecosystem.

A quick online search reveals various vendors and analysts offering different views on data observability types, pillars and lenses. Consequently, there are diverse data observability tools, each with its own focus—some monitor pipelines and infrastructure, others detect anomalies and outliers, or identify data quality issues. Some tools offer insights into resource utilization for informed decisions on resource allocation and costs.

The table below summarizes key capabilities required from a data quality and management platform for effective data observability. While it's essential to access all these capabilities, starting with the one most crucial to your needs allows for a gradual expansion of data management and observability efforts.


Focus / Perspective


Data Health and Issue Resolution

Data quality monitoring, error tracking, data profiling, real-time dashboards and reporting, issue resolution workflow and data lineage

Data Flow Observation

Job monitoring, dependency mapping, real-time tracking, job status alerts and performance metrics

Availability, Performance and Capacity

Resource allocation, performance tuning, capacity planning, scalability metrics and availability monitoring

Data Agility and Compliance

Data governance, data catalog, data access control and data sharing 

Resource Consumption Tracking

Resource usage analytics, cost optimization, historical resource data, auto-scaling and resource forecasting

Data Quality and Data Pipeline Synergy 

Healthy data pipelines are essential for ensuring data availability, reliability and performance. However, pipeline health becomes less relevant if the data they carry is unfit for purpose. While pipelines may function well technically, poor data quality—manifesting as inaccuracies, incompleteness, or inconsistencies—can undermine the overall value of the data ecosystem. These issues persist irrespective of pipeline health, leading to incorrect insights, flawed decision-making and compliance concerns. 

Recognizing that data quality and pipeline health are intertwined is crucial; neglecting one diminishes the effectiveness of the entire data infrastructure. To unlock the full potential of data, a holistic approach is necessary, combining pipeline health observability with robust data quality measures. 

Additional Resources

Watch our on-demand webinar “Data Observability: The Key to Successful Data and Analytics” and download a copy of our eBook, “Data Observability: The Key to Successful Data and Analytics.”