What is data observability? Learn how it differs from data quality and explore the top tools to find the right data observability platform for you.
Every modern organization depends on data to help them make decisions, improve their products and services, and plan for the future. While data can offer tremendous value and insight, the management of ever-increasing pools of data has become a significant challenge, as the operations necessary to collect, store, process and use data can all introduce complexity and data quality issues. The more reliant on data that your organization becomes, the more you need to ensure that your data pipelines are functioning properly.
Building effective data analysis tools isn’t just about collecting and analyzing huge amounts of data. You also need to continually assess and iterate on these processes, digging into the details of each data source and type to determine the characteristics that make it useful. Because modern data supply chains are large and complex, it’s essential that you have a holistic strategy and tools to ensure the accuracy and relevance of the data passing through them. Data experts and organizations call this data observability, and it is becoming an increasingly important part of the modern data stack.
While data collection and storage tools often have their own data quality tests and safeguards, these are only focused on a single tool, and as such, you cannot rely on them to uncover issues from other parts of the supply chain. For example, if the data source starts to collect erroneous data, the ingestion pipeline and database might not be able to detect this mistake, as their systems see this data as being as valid as any other. This illustrates the importance of data observability, which goes beyond the performance of a single tool or database to continually check on the health and performance of the whole system — especially in distributed environments where cloud observability plays a critical role.
In addition to helping your organization ensure the health of the data you use, data observability solutions are also designed to give you a thorough understanding of the whole supply chain and to help you locate and remediate any issues that they detect. By providing you with an end-to-end understanding of where data is coming from and going, data observability tools can also help you proactively address bottlenecks, security and compliance risks and other potential problems.
This broader, system-level visibility reflects a shift from traditional monitoring to modern observability practices (see observability vs. monitoring).
Data observability’s importance, like many infrastructural innovations, is most noticeable when something breaks. This could be the discovery that a system you relied on to track service uptime has been collecting data at the wrong intervals, causing you to miss service interruptions and lose customers. Another common issue is data inaccuracy, as this can be difficult to catch and might go undetected for long periods of time, leading to sub-optimal decisions.
The immediate effects of these issues are bad, but so too are the knock-on effects, which can include a loss of reputation, internal mistrust of data-based processes and a resource-intensive error mitigation process once you identify the source of the problem. If you rely on AI tools, training them using inaccurate data can lead to poor performance and can be a major waste of computational power.
By using a data observability approach, you can avoid these kinds of mistakes, flagging inaccurate or anomalous data and identifying the source of the problem before it moves further along the pipeline. This allows your team to use data fearlessly to support their projects and drive better organizational outcomes.
Creating a successful data observability strategy requires you to break down the characteristics which make data useful and understand how issues in the data pipeline can limit this usefulness. Here are the five “pillars” data observability solutions use:
Data relevance often depends on it being current, which means systems that don’t update data in a consistent manner can create issues. Not all data needs to be updated in real time to be useful, but ensuring that the cadence of data gathering and processing matches the way that your data is being used is key to ensuring freshness.
A sudden change in the range of a particular stream of data is another sign of potential problems. For example, an increase in signups following a product release is an expected change, but a sudden doubling of signups at a time when there’s no marketing or product activity might merit a closer look.
An observability system might use historical trends and other indicators to set a baseline of expected activity or data volume. By notifying you of a sudden increase or downturn in the number of rows of data at a certain point in the pipeline, the system provides you with an early warning and also an idea of where the problem might be occurring.
Because even minor changes to the organization of your data can break systems down, observability tools track the consistency of your data structures and notify you when changes take place. For example, if a data collection tool adds a new column of data in the middle of the table, this might result in upstream ingestion systems pulling this data erroneously or overwriting data on other systems.
In order to maintain data usability and uptime, you need to be able to quickly identify data accuracy issues and locate their source. You can use lineage tools to quickly pinpoint where in the data supply chain the issue might be occurring and pull relevant metadata about it, allowing you to understand how this issue came about and take steps to correct it.
Modern organizations' reliance on data can create major risks, as bad data can lead to inaccurate insights and break internal and customer-facing systems, forcing you to expend significant resources to undo the damage. Data observability allows you to address these issues quickly, improving efficiency and reinforcing trust in your organization. Here are some of the key benefits:
Depending on the data, downtime can be a minor irritation or a system-breaking issue. A successful data observability strategy will catch every issue quickly, allowing your team to proactively implement a solution before it reaches end users, customers or systems. Reducing the time spent on these issues means fewer person-hours spent on fixes and a lowered risk of reputational damage.
Being able to detect, locate and address inaccuracies or data pipeline anomalies greatly reduces the amount of time your data team spends putting out fires. This in turn allows them to spend more time proactively building more robust data collection, analysis and reporting features, which can have positive downstream effects for your whole organization.
Trust is easy to lose and difficult to win back. By taking a proactive approach, you can improve your team’s confidence in the data reporting they use, allowing them to effectively utilize that data to improve their performance and efficiency. Even if your product is not focused on providing data to users and customers, they still need to have the confidence in your ability to manage their data to feel comfortable using your services. This makes observability important for customer satisfaction and trust in all types of organizations.
In addition to early warning and lineage features, a holistic data observability strategy can also help identify data pipeline inefficiencies and bottlenecks. By mapping out and visualizing the flow of data, your team can build new data ingestion processes and rules with a deep understanding of how those changes might affect systems downstream. This allows them to add and iterate with confidence, increasing efficiency and making changes without worrying about breaking internal systems or trust.
In today’s modern data stack, data observability is no longer optional — it’s foundational. As data pipelines grow more complex and organizations rely more heavily on data-driven decision-making and AI, capabilities like AI observability are becoming increasingly important, further reinforcing the need to maintain the health, accuracy and reliability of data across the entire supply chain.
By adopting a comprehensive data observability strategy built around freshness, distribution, volume, schema and lineage, organizations can detect issues earlier, resolve them faster and build lasting trust in their data. The result is not just fewer disruptions, but a stronger, more resilient data ecosystem that empowers teams to innovate and move forward with confidence.
Selecting a set of observability tools or a platform starts with an internal assessment where you map out how data is gathered, ingested and stored at your organization. Identifying the most important data sources both in terms of value and in terms of potential risk will help you find a platform suited to those data types and pipelines.
Once you’ve made this assessment, you should seek out a platform that can help with these key data pipelines but which can also grow with you as your products and data collection processes become more complex. You should also analyze the pricing and ease of use of the different platforms available; if you are locked in to a platform which becomes pricey or difficult to use at scale, this can lead to significant issues later on.
Organizations are starting to apply observability strategies to AI training and integration, as the complexity of LLMs and the volume of data they use for training can create major risk factors. Just like a standard observability strategy, AI observability focuses on tracking and measuring each step of the AI training process as well as its output.
Because organizations often use unstructured data to train AI, observability tools will need to account for this complexity, as unstructured data may be more difficult to assess for accuracy and freshness. Additionally, AI observability needs to extend to any technical work done on the model and the way you have integrated AI tools into your system and products. If an AI agent doesn’t respond to a prompt, it may be an issue with the code and not the data, so observability needs to account for both.
Although there is significant overlap between the two, data quality involves a more granular focus on the accuracy and usability of a given dataset and whether it is able to meaningfully fulfill a particular task or purpose. Data observability, on the other hand, includes a holistic strategy and a set of tools to observe the collection, ingestion, processing and storage of data. Some of these observations may point to quality issues, but they aren’t focused on the data quality as much as the performance of the system as a whole.
Think of it like a restaurant which sources, stores and prepares ingredients. If the restaurant sources low-quality ingredients but prepares dishes rapidly and efficiently, the system is working even if the quality might not be up to your standards.
