What is data observability?

As data ecosystems become more complex, involving multiple sources, formats, and real-time processing, the challenges associated with ensuring data quality and reliability also increase. 

Often, the complexity isn't fully appreciated until issues begin to surface, which leads to observability solutions being implemented reactively, rather than proactively.

This is where having a well-thought out data observability system can help. In this article, I’ll discuss what you need to consider when putting together a comprehensive data observability system, everything from the right framework, the relevant pillars, and modern best practices. 

Then, I’ll go over the three key questions that data teams should use as a quick self-assessment in benchmarking their data observability maturity practices. 

Finally, I’ll close with some thoughts on why data observability remains a neglected data tooling category under traditional paradigms that are stuck on reactive approaches, which also provides some clues as to where data observability is heading in near future (hint: it involves data diffs and shifting left). 

Defining data observability

Data observability is the capability to be the first to detect, investigate, and resolve 'unknown unknowns' in your data environment. It involves setting up monitoring systems that not only alert you to data anomalies as they occur but also provide the tools and information necessary to delve deep into these issues to understand their root causes. 

By equipping data teams with the means to preemptively tackle unexpected errors and inconsistencies, data observability ensures that data remains reliable and trustworthy, supporting critical business decisions and operations seamlessly.

Data observability vs. data quality

There is a lot of confusion over how the two interrelate. As we’ve previously discussed, data quality is the overarching goal, and data observability is one of the two tools used to achieve it.

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Data quality is a broad concept focused on ensuring that data is accurate, reliable, and consistent throughout its lifecycle. It covers all practices aimed at maintaining high-quality data, from preventing errors before they happen to detecting issues in real time.

Data observability, on the other hand, is a subset of data quality that specifically deals with monitoring data processes in production. 

While data quality involves both proactive data testing to prevent issues and reactive data observability to detect and fix issues once they arise, data observability is primarily concerned with providing visibility and real-time alerts on the health and performance of data pipelines. As such, they use different tools to achieve different goals. 

Data observability framework

Without a framework, it’s easy for teams to overlook aspects of data integrity, quality, or pipeline performance. A framework ensures that each component of data observability (monitoring, detection, lineage, etc.) is addressed systematically, reducing blind spots in the data pipeline.

The right data observability framework should be empowering: organizations can move from reactive troubleshooting to proactive data management. It should cover:

• Data monitoring
• Data lineage
• Proactive data quality 
• Automation
• Incident management

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The five pillars of data observability

There are five key pillars of data observability: freshness, volume, distribution, schema, or lineage. These five pillars represent the key functional areas that ensure complete observability over data systems. They fit into the overall framework by providing focused, measurable components that, when combined, give comprehensive visibility into data health and quality.

Freshness

Freshness ensures that data is up-to-date and reflects the most current state of information. It monitors how recently the data was updated, helping organizations track whether data is being delivered and processed in a timely manner. If data becomes stale, it can affect decision-making by leading to insights based on outdated information.

Volume

Volume monitoring checks the amount of data flowing through pipelines to ensure it is consistent with expected patterns. Sudden spikes or drops in data volume can signal issues like incomplete ingestions, data loss, backend engineering bugs, or system bottlenecks, allowing teams to address problems before they escalate and affect downstream processes.

Distribution

Data distribution refers to the range, spread, or patterns of values within datasets. Monitoring the distribution helps detect outliers, anomalies, or unexpected shifts in data characteristics, which could indicate data corruption or errors in transformation processes, thus safeguarding the accuracy of analytics.

Schema

Schema monitoring tracks the structure and format of data to ensure it conforms to expected standards. Changes in the schema—such as new columns, missing fields, or data type mismatches—can lead to downstream system failures or data processing errors. Keeping an eye on schema ensures that data remains compatible across systems.

Lineage

Data lineage tracks the journey of data from its origin to its final destination. By visualizing how data moves and transforms across systems, lineage helps teams trace the root causes of issues, understand dependencies, and ensure that data maintains integrity as it flows through various pipelines and transformations.

Data observability best practices

The pillars are a good starting point but many guides often stop there prematurely. 

For data teams, what truly matters extends beyond theoretical frameworks​​—it’s about the practical, everyday implementation and the outcomes these practices drive.

Let’s consider what data observability looks like on the ground and how it directly impacts the operations of a data-driven and data-quality focused organization.

Building modern data observability systems involves a paradigm shift toward these four proactive, integrated, and holistic data quality and reliability best practices:

Proactive issue prevention

Modern data observability systems should focus not just on detecting data issues but also on preventing them from occurring in the first place. This involves integrating data quality checks and observability tools early in the data lifecycle, ideally during the development and testing phases. Such integration can help identify and mitigate potential issues before code and data changes are deployed to production.

Observability goes hand-in-hand with development

Observability tools need to be deeply integrated with the tools and systems used by data engineers and developers. This includes version control systems, continuous integration/continuous deployment (CI/CD) pipelines, and development environments. This integration allows for real-time feedback and automated quality checks during the development process, thus preventing problematic deployments.

Shift-left testing

Data engineers are increasingly adopting software development best practices, and one we really advocate for is"shift-left”. 

Data observability should also "shift left" — moving data testing and monitoring to earlier stages in your data pipelines. 

Upstream detection and resolution

Modern observability tools need to operate "upstream"—that is, they should monitor and analyze data flows starting from the point of ingestion or even during the data creation phase in operational systems. By detecting issues at the earliest possible stage, these tools can prevent faulty data from ever reaching critical storage and processing systems, thereby safeguarding downstream analytics and decision-making processes.

A unified data observability system

Together, the data observability framework, pillars, and modern best practices make a comprehensive data observability system that enables organizations to monitor, manage, and maintain the health and reliability of their data pipelines. 

This unified approach ensures real-time detection of issues, complete transparency into data flows, automated responses to anomalies, clear data lineage for root cause analysis, and seamless collaboration across teams:

Data observability benefits

What can you expect after adopting our suggested data observability practices? The benefits go beyond operational improvements—your organization will see enhanced data quality, faster issue resolution, and stronger trust in the data across all stakeholders.

Improved data quality

Data observability helps identify and quickly resolve data issues such as missing, incorrect, or duplicated data. By monitoring data pipelines in real-time, teams can detect anomalies early and maintain high data quality across systems.

Faster issue resolution

With comprehensive visibility into the data lifecycle, including lineage and transformations, data observability allows for quicker identification and resolution of issues. This reduces downtime and minimizes the impact on decision-making and business operations.

Increased stakeholder trust in data

By ensuring transparency in how data flows through the pipeline, data observability builds confidence among stakeholders. Teams can rely on timely, accurate data, leading to better insights and more informed business decisions. 

3 questions to benchmark data observability 

Now that we've covered the theoretical foundations for building a data observability system, it's time to  assess how your organization is putting these concepts into practice.  

Implementing data observability effectively requires not just understanding the framework but also ensuring it's applied consistently across your data lifecycle. Here are three critical questions to benchmark your current data observability practices:

1. How early in the data lifecycle are data tests and monitoring implemented, and are issues detected upstream at the data ingestion or creation stage?
2. Are your core source and production tables consistently monitored for key indicators like freshness, schema changes, and anomalies?
3. When data incidents occur, does your data team have the knowledge and authority to resolve issues quickly and directly at the source?"

The past, present, and future of data observability

Data observability is often relegated to an afterthought in many data stacks. Teams tend to prioritize building out data warehousing and enhancing analytics capabilities, sidelining observability until a significant data quality incident underscores its necessity.

Why? 

Historically, the focus in data management has been on storing, retrieving, and processing data efficiently. Observability, or the broader concept of monitoring the quality and integrity of data, wasn't a primary concern as long as the data systems functioned as expected. As a result, many older systems were designed without built-in observability features.

Also, data observability is sometimes seen as a cost center rather than a value driver, leading to it being deprioritized or tacked on only when data issues become too disruptive to ignore.

There’s a growing awareness about the importance of data observability, but it's a relatively new field compared to other aspects of data management. Many organizations may not fully understand the benefits, or they lack the in-house expertise to implement it effectively. This gap in skills and knowledge can lead to delayed adoption.

What traditional data observability tools get wrong

Traditional data observability tools primarily focus on detecting issues in production environments, such as data discrepancies, anomalies, and integrity problems, after they have occurred. They monitor data through its lifecycle and alert teams about discrepancies and anomalies. 

However, these tools lack the proactive measures needed to prevent issues before they affect the production environment, often resulting in delayed responses to data integrity problems.

Where data observability is heading

The reactive approach is important but incomplete. Addressing issues upstream—as early as the development or testing phase–is the only way to prevent data incidents. This is why shift-left testing is gaining momentum, emphasizing the need to prevent data quality issues before they reach production. Tools like Datafold's Data Diff, for example, allow teams to compare dev/test environments with production data, ensuring that code merges don’t introduce data quality issues.

The data stack continues to evolve at breakneck speed. We think that the demand for real-time, proactive observability solutions will grow, and AI will play a big role in advancing data observability tools. The future of data observability is likely to be characterized by more proactive, integrated, and user-friendly tools that are essential for managing modern, complex data ecosystems.

As data volumes and complexity grow, manually monitoring and addressing data issues becomes increasingly impractical. Expect to see more advanced automation in data observability, using AI and machine learning to not only detect anomalies but also predict and prevent them before they occur.

And finally, as more stakeholders across business functions rely on data observability, tools will evolve to be more user-friendly, with better visualization capabilities and simpler interfaces. This will make data health insights accessible to a broader range of users, not just data professionals. 

Implementing a modern data observability system

How do you know if you have the right data observability solution? Setting up a unified data observability system can be daunting. 

We can help. If you're interested in learning more about data observability tooling and how Datafold’s data diffing can help your team proactively detect bad code before it breaks production data:

• Book time with one of our solutions engineers to learn more about Datafold enables effective data observability through comprehensive monitoring, and which one(s) make sense for your specific data environment and infrastructure.
• If you're ready to get started, sign up for a 14 day free trial of Datafold to explore how data diffing can improve your data quality practices within, and beyond, data teams.