What is data observability?


 Min Read

Due to the increasing complexity of company data stacks and pipelines, observability should be a priority for all data teams. But why do we need data observability? The standard data observability definition states that this process gives data teams the ability to determine if the information in their company’s system is “healthy”— meaning all data is complete, consistent, timely, valid, and unique. 

Using data observability best practices and tools, businesses can detect and resolve issues in their datasets before any adverse impact occurs. Additionally, data observability allows companies to discover what is negatively affecting the health of their data and prevent future problems. At its core, the data observability process is the catalyst for data quality. This is because data observability functions to alert teams when datasets stray from established parameters and data observability metrics. 

In this article, we will discuss the benefits of observability and explain how this process enables model monitoring.

Currently, there are 5 pillars of data observability:

1. Distribution

Data distribution involves determining if your data is: 

  • Operating within accepted ranges.
  • Properly formatted. 
  • Complete and accurate. 

Distribution pertains to deriving insight into your datasets so the data team can determine the integrity of the information. 

2. Freshness

Freshness involves determining if your datasets and tables are: 

  • Current and timely. 
  • Regularly assessed and updated.
  • Including or omitting upstream data (depending on the use case).

Freshness revolves around understanding how up-to-date your data is and deciding how often your data tables should be reviewed and renewed.

3. Lineage

Data lineage pertains to: 

  • Identifying upstream and downstream data assets. 
  • Discovering how data is being generated.
  • Learning how the data is going to be applied to business processes.

Data lineage explains who is accessing the data and how that data is being used. When a company achieves satisfactory data lineage, metadata is collected. This metadata is then used to inform processes like data governance, technical guidelines, and future-facing business decisions. 

4. Schema

The term “schema” can take on many meanings when it comes to data. However, in regards to data observability, the schema concerns the organization of data and involves evaluating:

  • What your current schema is. 
  • How your schema has changed, or should be changed.
  • Who made the changes, or who should implement changes.
  • Why changes have been made, or why they should be made in the future.

Keeping a close eye on how and why your schema is changing allows you to identify incidents of broken data and other errors, ensuring the health of your overall data ecosystem. 

5. Volume

Volume in relation to data observability answers one important question: 

  • Do you have all of the required data in place? 

Volume is all about ensuring that your information is complete. This involves monitoring your data tables in case something goes missing. For example, if you previously had 300,000 rows of data and then suddenly you only have 100,000 rows, you should be made aware of that change in volume immediately. 

What are observability tools?

Data observability tools are designed to observe and assist with the objectives of the 5 pillars. Additionally, these tools are fully automated and are designed to identify and evaluate the overall health and performance of a company’s data ecosystem. This mainly involves preventing bad data from infiltrating the system in the first place. 

A quality data observability tool should be equipped with the following capabilities: 

  • An easy configuration process.
  • Seamless integration with your existing tech/data stack. 
  • Rich context and insight into your data. 
  • Preventative monitoring to identify potential problems quickly and with ease. 

Data observability and AI observability in model monitoring

Data and AI observability go hand-in-hand. While data observability is intended to monitor the overall health and status of an organization's data ecosystem, AI observability is intended to monitor the performance data and metrics of a machine learning (ML) system. In a sense, data observability plays a role in AI observability. This is because AI observability involves performing in depth analysis of ML data in order to successfully investigate, resolve, and prevent model issues. 

For example, an AI observability platform like Fiddler improves on traditional model monitoring tools to provide in-depth model insights and actionable steps by automatically monitoring ML metrics - from raw data to production. Our unique platform offers detailed explanations with model analytics that explain the “why” and “how” behind model behavior. This allows users to easily review real-time monitored output for spotting ML issues or acting on alerts. 

With AI and data observability, users can easily identify the problem drivers and root cause issues surrounding unhealthy data ecosystems and model failures. Both AI and data observability save employees time and increase the accuracy of ML models and data ecosystems. 

AI and data observability challenges

Although there are many challenges AI and data observability aim to solve, this short list covers some of the most pressing data issues companies are facing right now. 

Data integrity

The modern world-of-work is intensely fast-paced and ever-changing. However, much of machine learning happens in a black box. This means that automated data pipelines and the ML models that power them have a difficult time adapting to dynamic business data. Without proper model risk management and observability measures in place, data inconsistencies often go unnoticed, running amok in deployed AI systems.

Data drift

Although ML Models are trained using specific data, they often encounter different data in production. This causes the ML model to make faulty predictions which leads to future mistakes and inaccurate data pipelines. Data and AI observability tools and practices help users identify and remedy model drift before real damage can be done to the data ecosystem. 

Data outliers

Since business data is so dynamic, deployed ML models often encounter data that expands far beyond the training distribution. These data outliers lead to isolated performance issues that are difficult to debug globally. Using AI and data observability tools and tactics allows users to pinpoint these outliers in real-time, providing insights into how each issue should be addressed.

Data bias

New data is always encountered after a model is set to work. This can cause an ML model to become biased after deployment, meaning that a model’s impact on protected groups might change despite model validation. Data and AI observability help users identify model bias quickly and provide helpful insights into how those biases can be remedied and prevented in current and future ML models. 

Fiddler: responsible AI fit for a data-driven world

Inaccurate and biased data leads to disadvantageous outcomes. From identifying drifting data and performance dips to pinpointing outliers, Fiddler keeps your AI on track with AI observability. At Fiddler, we are dedicated to helping your MLOps and Data Science teams develop responsible AI that protects the integrity of your data ecosystem. 

Try Fiddler to learn how to build trust into AI.