Data Observability definition

What is Data Observability?

Data observability is concerned with the health of your data, which is obtained from a variety of sources. It entails much more than just keeping an eye on things. As companies are gathering data from a variety of sources, they have become much more reliant on their data for everyday operations and decision-making. As a result, it is vital to guarantee that data is delivered in a timely and high-quality manner. When a large amount of data is transported around an organization, typically for analytical purposes, data pipelines serve as the central repository for the data. Data observability contributes to the assurance of a dependable and effective flow of information.

There are five pillars of data observability that must be met. Let us take a quick look at them.

Freshness is vital for the business since we need to know how up-to-date your data is to make informed judgments about our products and processes. When it comes to decision-making, freshness is extremely crucial. As we all know, stale and obsolete data is a waste of time, money, and human resources.

Specifically, volume describes the completeness of your data tables and gives critical information about the health of your data sources, i.e. when garbage or poor data is sent out, it indicates that the data sources are out of current and that they must be updated. A vast number of organizations rely on data-processing platforms such as Google Big Query and Snowflake to process their data and generate meaningful insights for their business decisions. Data scientists can analyze massive amounts of data in real time using BigQuery, which is totally self-contained. 

The accuracy of data is crucial for the development of high-quality and trustworthy data systems for an organization. When we talk about distribution, we are talking about the measure of diversity in the system. If the data in the system wildly differs from one another, it is possible that there is a problem with the data accuracy. The quality of data produced and consumed by the data system is the main focus of the distribution. With distribution as part of your data observability stack, you can keep an eye out for anomalies in your data values and prevent erroneous data values from being injected into your system.

Schema updates are unavoidable because every organization is always growing and adding new features, which in turn affects the application database. Changes to the schema, on the other hand, that are not thoroughly tested and controlled can result in downtime for your application. The schema in the data observability ensures that database schemas, such as data tables, fields, columns, and names, are accurate, up to date, and subject to regular auditing and validation.

To effectively manage and monitor your data system's health, you must have a complete picture of your data ecosystem. The ease with which data can be traced via our data system is referred to as lineage. A unified picture of your data, or a blueprint of your data, is created with the help of lineage.


  • Cost-effective: Because of the elastic nature of the cloud, which stores and processes your data, if you need to load data faster or run a high volume of queries, you can scale up your data warehouse to take advantage of more processing capabilities, which will lower your costs. After that, you can minimize the size of the warehouse and only pay for the time that was spent in it. As a result, it becomes more cost effective as well. Once you have analyzed the data, you can eliminate the false positives to save even more resources.
  • Insights: Data observability identifies circumstances that you aren't aware of or that would otherwise go undetected, allowing you to avert problems before they have a significant impact on the business. Observability of data can be used to track the relationships between individual issues and to offer context and relevant information for root cause analysis and repair.
  • Alerting: Data observability ensures that teams are notified when there is a data issue, allowing them to resolve it swiftly and save everyone from the consequences of a data outage. These alerts should ideally be configured to send a notification as soon as an anomaly is detected, which will save a significant amount of time and resources in the long run.
  • Cross-Operability: Because of the system's architecture, users are able to share information with one another. It is very simple for businesses to exchange information with every data consumer who uses them. The fact that these data availability tools are mostly used in the cloud, that they are dispersed across availability zones of the platform on which they operate—either Amazon Web Services or Microsoft Azure—and that they are designed to function continuously and to suffer component and network failures with minimal impact, the impact on customers is kept to an absolute bare minimum.


  • Data Vigilance: There are a variety of tools available that may provide you with unlimited storage or allow you to pay only for what you use. Another concern that may develop in this situation is the use of bad data. In the event that you continue to provide data into the system, particularly without confirming it, this could prove to be rather costly for you. Therefore, rules must be established to review and authenticate data before it is uploaded to the cloud for storage.
  • Process Determination: Before starting, it is also vital to establish the method we should use to process the data, such as extract, transform, load (ETL) or extract, load, transform (ELT). The transformation will be performed before the data is loaded, whereas with ELT, the transformation will be performed after the data has been loaded.
  • Performance: When dealing with a large amount of data, performance is essential because the faster you can load the data, the faster you can complete operations. Even when dealing with large amounts of data, performance cannot always be guaranteed due to the nature of the material being handled.
  • Complexity: The complexity of data may develop as an organization begins to collect information from a number of diverse sources, each of which may have a unique set of characteristics. A large number of organizations do not comprehend the complexity of the situation, and those organizations, as well as their instruments, are not prepared to manage the situation.