How Data Observability Drives Data Analytics Platform?

How Data Observability Drives Data Analytics Platform?


Table of content

Introduction to Data Observability

Two Teams, i.e., DevOps and Data Engineering, visualize the problems by setting up the metrics first and then defining the role of the metrics to monitor. These days AI teams and Business owners predict the plan and enhance the solutions based on what observations data has made. To see and let the problem pass is not Data Observability. They are understanding the root causes and steps to fix defines Data Observability.

So, Data Observability helps ensure that any downstream platforms for end-users such as Data Analysts work reliably and efficiently. For this instance, we can consider the Data analytics platform as the end-user product.

Click to explore about What is Data Observability?

What is Data Observability?

Data Observability can be understood as:

  1. Developers can investigate the problem on their own without any stress to deploy the code to regenerate the problem.
  2. Real-Time metrics help the teams to share the information quickly.
  3. Businesses gets more confident in product development and marketing.

Here, data observability should not be considered monitoring because the fixes needed to be applied with observed issues that should not break the running pipelines. These pipelines can either be downstream or upstream.

What is the importance of Reliability and Efficiency in Data Analytics?

Let us consider one scenario where a Business derives the product value from the customer feedback and data collected through several marketing channels from time to time. Now, if the Analytics done through Traditional Application Monitoring tools is not effective and reliable, then the product's actual value will not be able to derive.

The effectiveness of a tool can be measured by how accurately a system works in certain situations, such as failures. Also, the more effective a platform is, the more reliable the results will be. Data Analytics plays a vital role in building accurate models that can help develop new products, Align the processes, motivate the frequent changes in the organization, and so on. Hence, the Data Analytics platform should be more reliable and efficient for the same.

Data Analytics needs to be reliable and efficient because:

  1. Organization restructuring and goal setting is dependent.
  2. Cost and Efficiency metrics are bounded.
  3. Application Monitoring and Infrastructure management is focused.
  4. Data Quality and Lineage needs to be addressed.
  5. Business owners and Analysts made decisions and planned processes based on data.

Read more more about Observability vs Monitoring

Data Observability Vs Data Analytics Platform

Let us consider an example,

A space company needs to launch a satellite to Mars, and they rely completely on what data they have about the research of Mars, its orbits, and atmosphere. Now, if Data Analytics is only done based on the knowledge of known rules, then this can vanish the complete mission.

To make better decisions, Space companies need to set up a data Observability platform that can help identify the pipeline failures they previously had, making some rules on data, garbage data, Outage, and failure information.

This information can be very helpful when Analysts plan the business rules and make the decisions based on what ML models predict for them.

It will be good to call that Data observability will be the next push for Data Engineers. Data Observability triggers the Data Analytics, which covers the Infrastructure, Data, and Application.

  1. Data Observability helps AI teams to diagnose the problems and remediate them into pipelines
  2. Data Observability helps in orchestration, automation, and monitoring the data metrics.
  3. Data Observability helps the application developers to discover the changes and trace the root cause of the issues.

Through all these, the Best data analytics rules can be formed which helps the Data Analytics Platforms in:

  1. Setting up the accurate ML data models that help in planning the business decisions.
  2. Tracing the issues in the running system because data will be centralized with quality and lineage.
  3. Targeting Domain oriented goals.
  4. Addition of new data sources to the systems.
  5. Building specialized teams on Data.
  6. Increase of the Data pipelines complexity.
  7. Getting Useful information out of the data, which was previously considered as “garbage data.”
    Prevention of Application downtime.


It has been identified that businesses make decisions based on what data they have. Suppose the data itself provides some information through an automated process [Or data observability]. In that case, it helps the business owners and Analysts run better marketing campaigns, target the best audience, and make more accurate ML models that can predict the specific metrics. That concludes that Data Observability improves the Efficiency and Reliability of Data Analytics.

  1. Discover more about What is a Data Pipeline? Benefits and its Importance
  2. Click to know about Composable Data Processing with a Case study

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