What Is the Difference Between Data Observability and Monitoring?

Explore the nuances of data observability and monitoring. Understand their differences, benefits and use cases for better data management strategies.
Last updated
May 2, 2024

Data observability and data monitoring are both useful data management strategies. While people sometimes use these two terms interchangeably, it’s important to keep in mind that some distinct differences separate the two. In this guide, we’ll talk about both data observability and data monitoring and how to decide which is better for your organization.

What Is Data Observability?

Data observability is a data management strategy that encompasses the overall health and quality of the data within your organization. With data observability practices in place, you can focus on identifying and addressing data issues in real time, with the end goal of providing improved data visibility, optimizing data processes and making more data-driven decisions. Next, let’s take a look at some of the key concepts of data observability.

Key Concepts

Data observability involves several key concepts that make up the pillars of data observability. Understanding these key concepts will help you better implement data observability practices in your organization. The key concepts to keep in mind include:

  • Data freshness — You should regularly update your data and ensure recency. Stale data can cause issues and lead to misinformed data-driven decisions. You should have processes in place to keep your data as fresh and relevant as possible.
  • Data distribution — This pillar looks at the distribution of data and ensures that the values collected are within an expected range. In short, data distribution helps determine the reliability of the data you’re collecting.
  • Data volume — Data volume determines if you are collecting the amount of data you expect to collect and that the data that comes in is complete. Ensuring data volume is hitting the expected values means that the data you’re getting isn’t incomplete.
  • Schema — Schema looks at how data changes in your organization and monitors any broken data that may occur in your data processes.
  • Lineage — Data lineage is essential for tracking data from endpoint to endpoint. It allows you to monitor the upstream and downstream flow of your data and identify areas of optimization or breakdowns in your processes.

With these key concepts in mind, you can build robust data observability processes in your organization.


Data observability offers numerous benefits for organizations. These benefits may include:

  • Real-time issue resolution — Data observability helps organizations be proactive about identifying and resolving data issues in real-time rather than dealing with them after the fact. This helps to mitigate downtime for critical operations.
  • Improved efficiency — Data observability can help lead to improved operational efficiency and optimized data processes.
  • Improved decision-making — Data observability can improve the health and performance of your data processes, which means your organization can make more informed data-driven decisions. 
  • Improved data accuracy and integrity — Data observability also helps to maintain data accuracy and integrity in your organization. This gives a higher degree of confidence when making data-driven decisions.

Use Cases

Data observability and monitoring have various use cases that can benefit organizations in managing their data effectively. Here are some of the top use cases of data observability. 

  • Addressing data anomalies — Of course, one of the most common use cases for data observability is detecting and resolving data anomalies. 
  • Data Quality — Data observability is also used to ensure better data quality. By improving data processes, organizations can see their data be more accurate and reliable throughout all data sources.
  • Resource Optimization — Data observability can help teams determine where resources aren’t being used efficiently in data processes. Optimizing these resources can lead to better allocation and better processes overall.
  • Pipeline Design — When an organization has complete visibility into its overall data structure, then you can design better data pipelines. The findings from data observability can help guide the design and structure of these pipelines.

What Is Data Monitoring?

Now that we better understand data observability, let’s take a look at data monitoring. Data monitoring is a process that involves tracking and analyzing the performance of data systems. You must continuously monitor key performance indicators to ensure data systems are functioning optimally. Ultimately, data monitoring helps to ensure data is up to an organization’s quality standards. Next, let’s take a look at some of the key concepts of data monitoring.

Key Concepts

To fully understand the world of data monitoring, it's important to know a few key concepts. These concepts include:

  • Data ingestion — Data monitoring looks at where your data is coming from or where it is ingested. This means identifying your various data sources and ensuring they’re reliable and that your data is being transformed into a usable format. 
  • Data cleaning — When data is ingested, it needs to be cleaned to ensure that your data is accurate, reliable and up-to-date. Data cleansing often involves several processes, such as data validation, data profiling, data replacement and more. The goal is to ensure that the data you make available to users is usable and valid.
  • Data security and governance — Organizations also need to ensure their data is secure and that their governance practices are keeping them in compliance with industry regulations. Data monitoring can help to mitigate the risk of data breaches and ensure data is protected as effectively as possible.
  • Error monitoring — Data monitoring involves identifying and addressing errors in the data pipeline.


Data monitoring provides organizations with a multitude of benefits. Let's dive into some of these key benefits:

  • Improved decision-making — Data monitoring can help improve data-driven decision-making. Tracking and analyzing data can help organizations gain valuable insights that lead to positive business outcomes.
  • Identify data issues — Data monitoring can help you identify and address potential issues before they become a major problem. By detecting these anomalies, organizations can reduce downtime and optimize processes overall.
  • Improved data accuracy — Data monitoring plays a vital role in maintaining data integrity and accuracy. It helps to ensure data is reliable, updated and consistent. 
  • Improved security — Data monitoring helps with governance and compliance, improving data security and mitigating the risk of data breaches and cyberattacks.

Use Cases

Now that we have explored the key concepts and benefits of data monitoring, take a look at some data monitoring use cases. 

Data Quality Assurance — Organizations often implement data monitoring processes for data quality assurance. Data needs to be trustworthy in data-driven organizations, and data monitoring is ideal for helping to maintain quality and accuracy.

Data Anomaly Detection — Data monitoring is frequently used to monitor for data anomalies. Once found, organizations can address these anomalies right away and resolve them without impacting operations. 

Trend Analysis — Data monitoring can help organizations identify trends and patterns in data and capitalize on them.<p>

When To Use Observability and Monitoring

In the world of data management, both data overstability and data monitoring play important roles. There is often a crossover between the roles these strategies play. 

Generally, data observability is best utilized when organizations need real-time visibility into the behavior and quality of their data. It allows organizations to be quick on their feet and effectively stay on top of their data processes in a fast-paced environment. 

Data monitoring is ideal for tracking and analyzing data performance KPIs so organizations can optimize their data processes and ensure data availability.

So, when you need immediate insights into your data health, data observability is your solution. When you need ongoing performance analysis, data monitoring is the way to go. Ideally, you can have a mix of the two.

About Secoda’s Data Management Platform

If you want to optimize both data observability and data monitoring in your organization, Secoda’s data management platform can help. Secoda gives you visibility and insight into your entire data stack by connecting your data quality, observability and discovery processes.

Secoda offers complete end-to-end data quality monitoring to keep a constant pulse on your data quality. You can also easily set monitors and detect anomalies to immediately respond to data errors and issues. With Secoda, you can take your data management strategies to the next level.<p>

Try Secoda for Free

Secoda is your ultimate data management solution. As the first AI-powered data search, catalog, lineage and monitoring platform, you can double your team’s efficiency and enable data discovery throughout your organization.

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