Data observability is a relatively new term that has been gaining traction in the world of data engineering over the past few years. But what exactly is it? And more importantly, why should you care? In this blog post, I will try to answer these questions and more. We will discuss data observability, how it differs from other approaches, and why it is becoming increasingly important for businesses today by focusing on the ten key points you absolutely need to know about this new category.
By the end of this post, you will have a better understanding of this growing trend and be able to decide whether or not it is something you should invest in.
Let’s first start with a brief definition of data observability.
Data observability is the practice of monitoring data as it flows in order to detect and fix any issues that may arise in the data pipelines. This allows organizations to catch and correct data errors before they cause significant damage, leading to increased efficiency and productivity of the data teams. Some of the benefits of data observability include the following:
Let’s now dive into the 10 things data leaders absolutely need to know about data observability.
The origin of the concept of data observability can be found in control theory, and its mathematical definition is the measure of how well the state of a system can be inferred from its outputs. The concept of observability was then applied to software with the DevOps revolution, in which companies like Datadog and New Relic revolutionized the software engineering world by enabling engineers to get a better view of the health status of the systems.
Software engineers cannot imagine not having the centralized view across their systems provided by software observability tools. These tools allow developers to see how all the pieces of their infrastructure fit together and help them diagnose and fix problems quickly. As the cloud allows hosting more and more infrastructure components — like databases, servers, and API endpoints — it is more important than ever to have these tools in place in order to keep your systems running smoothly.
Today, the data space is undergoing the same challenges that software engineering faced a decade ago. As data volumes continue to grow, so does the challenge of ensuring data quality. Many organizations are finding that they are struggling to keep up with the increasing number of data use cases and products that require high-quality data. Additionally, the higher probability of data breaking means that businesses can no longer afford to take their data quality for granted. In order to ensure that their data is of the highest quality, businesses started investing in tools and resources, as well as putting in place processes and protocols for maintaining data quality. This is leading to the creation of the category of data observability.
There are several parallelisms between software and data observability, one of them being that they are built on three main pillars.
The three pillars of software observability are metrics, traces, and logs. Metrics are used to measure the performance and health of a system. Traces are used to track the path of a request through a system. Logs are used to store information about events that occur in a system. Collecting data from these three sources can help you understand how your system is performing and identify potential problems.
Similarly, the three pillars of data observability are metrics, metadata, and lineage. Metrics help you measure the quality of your data, metadata gives you access to data about your data, and lineage tells you how data assets are related to each other. Together, these three pillars help you understand your data infrastructure and make sure your data is always in a good state.
Now you may be asking yourself: “What about data quality testing and monitoring solutions? How do they fit in all this?”. Let’s dive into it.
Now that companies are dealing with more data, it is becoming increasingly difficult to detect issues and predict problems through testing alone. To cover all of the potential data issues, organizations put hundreds of tests in place. However, there are countless other possibilities for data to break throughout the entire data journey. This means that even with all of the tests in place, there is no guarantee that the data will be perfect. In order to ensure the quality of the data, companies need to be proactive in identifying and correcting any data issues as they occur.
Data quality monitoring is often used interchangeably with data observability, while in reality, it can be considered the first step needed to enable it. In other words, data quality monitoring alerts users when data assets or sets don’t match the established metrics and parameters. In this way, data teams get visibility over the quality status of the data assets. However, this approach ultimately presents the same limitations as testing. Although you can have visibility over the status of your data assets, you have no way of knowing how to solve the issue. Data observability is designed to overcome this challenge.
Data observability provides users with a more complete view of their data by allowing them to not only assess the health status of their data but also understand how that data is being used and what issues may be causing problems through data lineage. This allows users to troubleshoot issues more effectively and improve the overall quality of their data.
There are many reasons that can lead to bad-quality data, but it is possible to identify three main reasons:
Let’s quickly go through some basics. What do we mean by data stack? It can be described as a collection of tools and technologies that help businesses collect, transform, store and utilize data for analytics and machine learning (ML) use cases.
The stack is cloud-based and typically includes the following layers:
Data observability, by integrating into tools at each stage of the modern data stack, is capable of providing data engineering teams, or, more in general, data practitioners, with an overview of everything that is going on with their data. So, data observability should be considered the layer that sits on top of the organization’s existing data pipeline, ensuring that data quality is high at each stage of its lifecycle.
Lineage is the tracing of data assets and their dependencies within an organization. By understanding how these are related, businesses can better manage their data platforms.
So, data lineage is the ability to track data through its lifecycle, from the time it’s entered into the system to when it’s used in reports or dashboards. This information is incredibly valuable for troubleshooting data issues, as it can quickly show you where the data went wrong.
Lineage models can also link to the applications, jobs, and orchestrators that gave rise to anomalous data. This makes it easy to track down the source of the issue and fix it.
Data observability is essential for any organization that wants to make the most of its data. By understanding how data is transformed, produced, and used across the entire ecosystem, businesses can identify and correct any errors that may occur.
Data observability also enhances data discovery and data quality, ensuring that data is accurate and consistent at every stage of its lifecycle. In general, it’s possible to identify three main benefits of data observability in business:
There is no simple answer to the question of whether or not companies should invest in a data quality program. The decision will depend on multiple factors, such as the company’s size, industry, and specific needs.
Some of the factors that companies should consider when deciding whether or not to invest in data observability include:
The company’s size
It can be helpful for companies of any size, but it is often seen as particularly important for large organizations. This is because large companies tend to have more data, more complex data ecosystems, and larger data teams, making it harder to keep track of everything that is going on. Data observability can help these companies get a better understanding of their data and ensure that it is accurate and reliable.
Level of data maturity/Adoption of a modern data stack
Data observability can be helpful for companies at any stage of data maturity. As soon as organizations set up a data stack, they should start monitoring the data passing through all of the different tools to prevent data reliability issues.
Budget
Data observability can be an expensive investment, depending on the size of the company and the complexity of its data ecosystem. Companies should weigh the cost of data observability against the benefits it can provide.
Companies should invest in the data observability tool that best meets their needs. There are a variety of data observability tools on the market, each with its own specificities.
The company’s industry
Data observability can be helpful for companies and data teams in any industry, but some industries are more heavily regulated than others. For example, the healthcare industry is subject to strict regulations around data privacy, while the financial industry is subject to regulations around data security. Data observability can help companies in these industries comply with these regulations by allowing them to monitor their data.
The company’s specific needs
Data observability can be helpful for companies that need to comply with regulations or improve their data quality. Data observability can also be helpful for data teams that want to get more value from their data or improve their decision-making.
Ultimately, the decision of whether or not to invest in data observability will come down to the company’s specific needs.
The right solution for your organization will depend on your specific needs and goals. There are a variety of potential solutions on the market, ranging from open-source to commercial. But even within the category of commercial solutions, we can find important differences in terms of capabilities and integrations with other tools used by data engineers. When it comes to data observability, one size does not fit all. The right solution for your organization will depend on your specific needs and goals.
You can read more on what to look for when choosing a data observability tool in one of my previous blogs.
Companies are starting to realize the importance of being able to track and monitor their data. As more and more data is used for business decision-making, the need for better data observability will continue to grow. Data observability is a growing trend, with more and more companies investing in tools and technologies to help them monitor their data and many companies being created with the aim of developing data observability solutions.
Data observability is a complex and dynamic topic, but hopefully, this introduction has given you a better understanding of what it is and how it can be leveraged to benefit your organization. If you’re interested in learning more, reach out to contact@siffletdata.com!
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