What Data Observability Is, What It’s Not, and Why it Matters?

Data observability has emerged as a prominent concept in the contemporary data landscape, generating excitement and discussion among practitioners. However, amidst the enthusiasm, there is often a lack of clarity about its precise meaning. The term is frequently used loosely by vendors and investors, further contributing to confusion.

In an effort to bring clarity, we aim to define data observability in this blog post—explaining what it encompasses, what it doesn’t, and why it holds significance.

What is Data Observability?

Data observability, as we define it, pertains to the level of visibility into your data at any given moment. Dedicated tools like Metaplane collect metadata about data properties and relationships, monitor changes, and provide actionable insights. Think of data observability as analytics for your analytics.

Breaking down our definition into three parts:

1. “the degree of visibility you have”

Visibility is defined by the range of closed-ended questions a data team can address, encompassing yes-no inquiries or those with specific answers. 

Here are several examples of closed-ended questions that can be addressed using an end-to-end data observability tool, particularly regarding assessing your ecosystem:

  • What is the count of dashboards within my company?
  • How many data sources does my company currently maintain?
  • Are all my ETL jobs operational?

For tracing dependencies:

  • Which tables and columns in my data warehouse contribute to this dashboard?
  • How many dashboards does this column support?
  • Which files in my data lake can be deprecated?

Tracking usage:

  • Which users access this dashboard on a weekly basis?
  • How are my data products being utilized?

Ensuring desired state:

  • Is this value or row count within the accepted range?
  • When was the last instance of data retrieval from this API?
  • Are key metrics being updated in a timely manner?

2. “into your data”

Even when your data sources are operational and your data systems are functioning correctly, the substance of those systems—the data itself—may still be inaccurate. (We refer to these as silent data bugs.) Hence, it is crucial to gain visibility into the data content, not solely the data system.

A caveat to note is that the quality of the data inside these systems is often influenced by the performance of the data systems. System downtime, for instance, can lead to both data downtime and the generation of inaccurate data. Nonetheless, these two concepts are generally considered distinct.

3. “at any point in time”

As a data engineer, the ability to zoom in on your data at any given moment is crucial. Without this capability, discerning differences between today and yesterday would be impossible.

Historical baselines hold significance for three key reasons:

  • A historical baseline enables the training of machine-learning models that compare the current state of your data with its previous states. Given that data is in constant flux—remaining relevant and interesting by evolving over time—the models used to comprehend and analyze it must adapt accordingly. Comparing current data to historical data stands out as the most potent approach.
  • A historical baseline facilitates the identification of anomalies within any data set. When an incident is flagged, the initial verification involves checking whether the data point falls within the anticipated range. Understanding this expected range is crucial, and historical context provides data teams with efficient and effective debugging capabilities.
  • A historical baseline empowers the forecasting of the future state of your data. While monitoring current data is essential, it’s equally important to anticipate how it will appear in the future—whether over the weekend, during your vacation, or next month when projecting annual cloud costs. Understanding past trends is the optimal way to gauge future developments.

What isn’t data observability?

Data observability is distinct from data quality, data monitoring, or data testing. The nuances of these concepts set them apart from the essence of data observability. Continue reading to explore these distinctions.

Data observability vs. data quality

Understanding data quality is something most of us grasp, but a gentle reminder is always beneficial.

In simple terms, data quality refers to how well data serves a specific purpose or meets an internal standard.

In the data landscape, where companies providing data observability software constantly discuss ways to enhance data quality, it’s not uncommon for some data engineers to mistakenly consider the two terms interchangeable. The confusion arises because both data observability and data quality share a common goal: building trust among stakeholders in an organization’s data.

A key distinction between the two lies in their nature — data quality represents a challenge, while data observability stands as a solution.

Data team leaders typically don’t lose sleep over concerns related to data observability. Instead, their worries often center around potential data quality issues. The fear of waking up to a perplexing message from a key stakeholder, questioning the reliability of their data, can be a genuine concern.

While data observability technology can effectively address data quality problems, it’s crucial to note that it’s not the sole solution. Other approaches, such as thoughtful implementation of people and processes, or the adoption of data unit testing and regression testing, can also prevent data quality issues.

Furthermore, using data observability tools goes beyond resolving data quality problems to enhance data reliability. In the world of data management and DataOps, these tools find valuable applications in impact analysis, root cause analysis, spend monitoring, usage analytics, schema change monitoring, and query profiling, among various other use cases.

Data observability vs. data monitoring

While closely related, data monitoring and data observability carry nuanced distinctions in their connotations. Furthermore, their practical applications diverge.

Data monitoring involves assessing whether specific pieces of metadata fall within their expected range. This process requires proactive effort from data teams to define and specify the metrics deemed important enough for monitoring.

In contrast, data observability takes a different approach by systematically collecting and tracking all metadata over time, creating a comprehensive historical record. This method eliminates the need to predetermine which metrics are significant upfront. The advantages of data observability lie in its capacity to provide a baseline for monitoring when necessary. 

Additionally, it offers coverage across the entire data stack, acknowledging that data issues are interconnected. If a problem arises in one part of the data pipeline, it often affects downstream components. The extensive metadata provided by data observability facilitates root cause analysis and debugging, enhancing the overall understanding and management of data issues.

Data observability vs. data testing

Distinguishing between data observability and data testing is crucial due to their considerable overlap.

Data testing is commonly employed to assess data quality. For instance, if there’s a need to confirm the timeliness of a specific dataset, a unit test, such as a freshness check, can be conducted.

Consider a scenario where a dashboard, expected to update every hour, was last updated three days ago. If no one reviews the dashboard, it becomes an unknown data quality issue. Once a business user notices and brings the issue to the data team’s attention, it transforms into a known issue.

Data testing is tailored for evaluating known issues—whether from the past or with a strong suspicion of recurrence in the future—against predetermined expectations within a specific segment of your data pipeline.

However, the challenge arises as data issues are not isolated, and future issues may not replicate past occurrences.

In contrast, data observability is designed to address unknown issues within constantly evolving data. It involves testing these unknown issues against evolving expectations across your entire data stack. This approach acknowledges the dynamic nature of data challenges and provides a broader scope for proactively identifying and addressing issues.

Why is data observability important?

Understanding the importance of data observability is crucial, considering that data plays a pivotal role in enhancing revenue, reducing costs, and managing risks for businesses. It enables informed decision-making and effective actions. The significance of data observability lies in its capacity to empower data teams in delivering the high-quality data essential for achieving these business goals.

The practical aspect of data observability is where it becomes intriguing. While data teams supply other departments with the data required for decision-making and operations, they often lack the necessary data to efficiently perform their own tasks. This data, termed metadata as it pertains to information about data, can be challenging to collect and track without a dedicated data observability platform.

Data teams equipped with data observability tools leverage historical metadata to fulfill critical responsibilities, including:

  • Data quality monitoring: Assessing whether the current state of data meets external use case needs and internal standards.
  • Root cause analysis: Identifying the upstream dependencies responsible for a data quality issue and expediting problem resolution.
  • Impact analysis: Evaluating the downstream consequences of a data quality issue, determining which teams should be notified.
  • Spend monitoring: Tracking expenditure and its distribution across the data stack.
  • Usage analytics: Understanding who, when, how much, and in what manner data assets are utilized by stakeholders.
  • Query profiling: Optimizing both data assets and stakeholder queries to minimize time and cost.
  • Data governance: Gaining an overall view and control of data health for faster development by data engineering teams and enhancing trust for decision-making.

In essence, data observability assists data teams in fulfilling their role of delivering high-quality data crucial for business growth.

A recap of what we’ve learned

In summary, data observability refers to the level of visibility you have into your data at any given moment. It stands apart from related concepts such as data quality, data monitoring, and data testing.

While data quality addresses issues, data observability encompasses a set of solutions that not only enhance data quality but also assist data engineers in various tasks like root cause analysis, impact analysis, spend monitoring, usage analytics, and query profiling.

Unlike data monitoring, which necessitates upfront specification of monitored metrics by data teams, data observability provides a historical record of metadata and adds monitoring on top of it.

In contrast to data testing, which focuses on testing known issues against predetermined expectations in a specific part of the data pipeline, data observability is geared towards testing unknown issues in constantly changing data against evolving expectations throughout the entire data stack.

The significance of data observability lies in its ability to empower data teams to supply businesses with high-quality data, facilitating better decision-making and effective actions. This, in turn, plays a crucial role in enhancing business profitability and ensuring a company’s long-term success.

 

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