by Navdeep Singh Gill | 22 November 2021
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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 What is Data Observability?
Data Observability can be understood as:
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.
Data and analytics teams would be flying blind if they didn't have insight into data pipelines and infrastructures, i.e., they wouldn't be able to fully comprehend the pipeline's health or what was happening between data inputs and outputs. The inability to grasp what's going on across the data lifecycle has several drawbacks for data teams and organizations.
Organizations' data teams grow in size and specialization as they become more data-driven. Complex data pipelines and systems are more prone to break in such settings due to a lack of coordination, miscommunication, or concurrent changes made by team members. Data engineers don't always get to work on revenue-generating tasks because they're continuously resolving one data or pipeline issue or attempting to figure out why a business dashboard looks out of whack. I understand that this can be a pain in the neck at times.
It is mentioned below how data observability increases scalability and realizes cost-effectiveness:
Here are some examples of how data observability may aid organizations in scaling data innovation by removing friction points in design, development, and deployment.
Analytics obtained from data, processing, and pipelines can provide a wealth of information that can be used to improve resource planning, labor allocation, and strategy.
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:
Read more about Observability vs Monitoring
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.
Best data analytics rules can be formed that helps the Data Analytics Platforms in:
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.
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