What Is Data Observability? Everything You Need To Know

A recent study by Gartner predicted that only 20% of analytic insights will lead to business outcomes this year. Given that organizations are collecting higher volumes of data now than ever before, this seems like cause for concern.

So what’s the problem? Where is this prediction coming from?

The problem seems to be that many fail to achieve data agility and spend too much time troubleshooting data errors. In other words, organizations have plenty of data but fail to properly manage it and transform it into something of value they can use for business insights.

Data observability can help, but what is it and how does it work?

We’ll explain all you need to know about data observability, including the pillars that drive it and how a DataOps platform can enable observability for your organization.

What is Data Observability?

The term “data observability” is often used interchangeably with “data monitoring,” but they’re not quite the same. Data monitoring issues alerts based on pre-defined problems. For example, when a data set looks different than it should, based on your parameters, or when the value falls outside the expected range.

Data monitoring tells you when a problem occurs.

However, while data monitoring alerts you to a problem, it doesn’t explain what exactly went wrong. With data monitoring, you know a problem exists in your data system, but you can’t pinpoint the source. On the other hand, data observability provides visibility into your data system and helps you determine what exactly happened, what changes occurred, who made them, and more. It combines artificial intelligence, machine learning, and DevOps best practices to create systems to improve monitoring and debug the data collected. Because it tracks the entire data system, observability helps answer why a problem occurred in the first place. 

So, in short, data monitoring is only part of observability. In addition to monitoring, it also tracks and troubleshoots data issues to minimize disruptions.

An image depicting data observability platform​
Data observability provides visibility and helps answer what exactly happened, what changes were made and who made them

The Five Pillars of Data Observability

Data observability is based on five pillars that help you understand the state of your data at any given time. These pillars combined represent the health of the data in your system.

The five pillars of data observability are: 


Data timeliness or freshness is one of the main concerns for organizations dealing with data.

Are your data tables updated regularly? If they are outdated, you don’t want to be wasting time and money on compiling and analyzing data that is no longer relevant. The freshness or recency of data is crucial for decision-making, and observability helps catch any timeline inconsistencies early on.


Distribution involves checking your datasets to ensure your data is:

  • Accurate
  •  Properly formatted
  • Operating within an accepted range


As the name suggests, this pillar of observability refers to the amount of data in a database. This measurement shows you if your data intake is within the estimated thresholds. It also shows you if there is enough data storage to meet your data demands.

Keeping track of data volume is crucial for ensuring you operate within defined data limits. If the rows in your data tables suddenly shrink, you’re not meeting the data intake thresholds, and something could be wrong with your data sources.


Schema involves changes in the organization of your data, such as adding or removing tables, fields, or columns. Schema changes can affect your data’s health and cause downtime if not managed properly, which is why it’s essential to keep track of who makes these changes and when. This pillar helps ensure your database schema is actively monitored and regularly updated.


Lineage gives you a full picture of the data landscape, including upstream and downstream sources. When a problem occurs, lineage tells you exactly where the problem occurred by showing you which upstream sources were affected. Basically, lineage shows who is generating data, who is accessing it, and how that data is being used.

An image depicting a person looking at data​
Before adopting a data observability platform, consider whether you need one by observing the signs

Signs It’s Time To Invest In Data Observability Platform

Observability enables data teams to be agile and spot and resolve any issues that arise quickly and efficiently.

If you’re unsure about whether your team needs a data observability platform, here are a few signs that tell you it’s time to invest:

  • You’re regularly adding new members to your data team
  •  You’re frequently adding more tables and sources to your data stack, making it more complex
  • Your team spends a significant amount of time troubleshooting data quality problems instead of focusing on core matters
  • You’ve recently moved your data platform to the cloud
  • You’re moving to a self-service analytics model
  • You rely on data for your customer value proposition (CVP)

Key Takeaways On Data Observability

If your organization relies on data to make critical business decisions, that data must be high-quality and accurate. Data observability helps you maintain the quality of the data you’re collecting and identify and resolve data issues efficiently.

Data observability is built upon five pillars that help you get a wholesome picture of the health of your data system:

  • Freshness
  • Distribution
  • Volume
  • Schema
  • Lineage

Data observability is critical if your data team is growing and if your data stack’s complexity is increasing. Meltano can help create the perfect environment for data observability by enabling your team to spot and fix data errors instantly.


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