Data Monitoring v Data Observability

When it comes to managing data, there are countless strategies that one can use to ensure that your data stack remains reliable and accurate. Two of these strategies are data monitoring and data observability. While these terms are sometimes used interchangeably, they are, in reality, two unique techniques. In this blog post, we will compare and contrast these two strategies and explain how they can be used to ensure the quality of your data and the reliability of your data stack.

The Importance of Data Quality

Before we dive into the difference between data monitoring and data observability, we must first consider the importance of data quality. Poor data quality can have a significant impact on business operations. Decision-makers need to rely on accurate data to make informed choices. Still, if data quality is subpar, the wrong decisions can be made. Inadequate data quality can stop team velocity, resulting in extended deadlines, poorer teamwork, and a loss of trust in the data team.

Many people schedule ad hoc data quality tests in response to incidents. But that alone is an insufficient strategy. This reactive approach can still lead to additional errors as the tests may not cover all potential issues, and need to manually update test thresholds.

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What is Data Monitoring?

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Data monitoring is the process of automating data quality checks. These data quality tests verify metrics such as accuracy and consistency of data coming into and leaving a system over time to ensure that it is correct and conforms with the user's expectations, and updates these metrics using the behavior of your data as time goes on.

What is Data Observability?

Data observability refers to a holistic approach to data management that also includes data monitoring and troubleshooting tools. It goes a step further than data monitoring by providing teams with the necessary tools for gaining insight into what's happening with any data issues, particularly with data lineage. You can quickly identify issues, troubleshoot and debug the cause of problems to prevent them from occurring in the future.

The Difference Between Data Monitoring and Data Observability

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While both data monitoring and data observability serve the same goal in ensuring quality data, they differentiate on when they are used and how they're used. Data monitoring primarily helps identify potential issues or disturbances in the data stack. In comparison, data observability delves into providing tips for solving the issue, along with preventing future data incidents.

Data Observability vs Data Monitoring Examples

Using Metaplane as an example, we can compare how Data Observability expands beyond finding data issues:

• Data Stack Integration: Data observability tools typically aims for 100% modern data stack coverage, expanding coverage outside the warehouse, to help you discover the impact of issues. For example, Metaplane here would help you find dashboards and reports in your BI tool affected by data incidents.
• Finding Issues: We use data monitoring to find issues within the warehouse, but also offer coverage for select integrations, such as dbt runtimes.
• Triaging Issues: With automated lineage and root cause tools in the platform, data teams are able to significantly reduce time spent finding the query, process, or table where issues originated.
• Preventing Issues: By integrating with Github, we're able to show users impacted tables and how data quality tests might be affected prior to merging changes to your data model.

The Role of Data Observability in the Data Stack

Data observability plays a crucial role in the modern data stack. The modern data stack consists of ELT, warehousing, and BI tools required to turn data into useful business insights. Data observability provides users with valuable insights into critical areas of the modern data stack by analyzing the constantly moving data flowing to provide feedback about daily ad hoc decisions.

Example of Using Data Observability in the Modern Data Stack

Data observability is essential in the case of automating marketing e-mails. Imagine we're populating a "high intent users" list in our marketing e-mail tool through the activated_users table in our warehouse. If the data quality of this table is compromised, it can lead to a subpar marketing decision and a potential loss of business. Data observability engenders trust in the data team through ongoing data quality improvements throughout the data stack guarantees that the data reflects what is happening in the business.

Summary

Data monitoring and data observability are two crucial approaches for ensuring data quality and the reliability of your data stack. Data monitoring provides an overview of the data quality and helps identify possible issues, while data observability goes a step further by troubleshooting the root cause and helping prevent future issues from occurring. By utilizing data observability tools, users receive valuable insights into the modern data stack, identifying areas that need improvement and presenting solutions for polishing data quality.