What is Data Observability?

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In the modern data world there are many capability categories, each with their own specialised terms, technologies and three letter acronyms.

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In this article we describe what is a Data Observability.

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What’s is a data observability?

Data observability is the practice of monitoring and measuring the behaviour of data. It is the ability to understand how data is flowing through your systems, identify any anomalies or issues that arise, and take action to address them. It requires a holistic view of the entire data factory, from data ingestion to data processing and analysis.

In other words, data observability is about making sure that the data you are using is accurate, complete and reliable. It is about ensuring that your data is trustworthy and that you can rely on it to make informed decisions.

Why is Data Observability Important?
Data observability is becoming increasingly important as organisations rely more and more on data to make critical business decisions. It is not enough to simply collect data and analyse it. You need to be able to trust the data you are using, and that requires a comprehensive approach to data observability. Without data observability, you run the risk of making decisions based on inaccurate or incomplete data. This can have serious consequences for your organisation, from lost revenue to reputational damage. Data observability helps you identify and address issues before they become major problems, ensuring that you can make decisions based on accurate and reliable data.

Examples of Data Observability

To better understand data observability, let’s look at some examples of how it can be applied in practice.

Monitoring Data Quality
One of the key aspects of data observability is monitoring data quality. This involves tracking the quality of your data, identifying any issues or anomalies that arise, and taking action to address them.

For example, you might set up automated alerts to notify you when data quality drops below a certain threshold. This could be due to data that is missing, incomplete, or incorrect. By monitoring data quality, you can quickly identify and address these issues, ensuring that your data is accurate and reliable.

Tracking Data Lineage
Data lineage refers to the ability to track the path of data as it moves through your data factory. This includes where the data came from, how it was processed, and where it is currently located.

By tracking data lineage, you can quickly identify any issues that arise in your data pipeline. For example, if data is missing or delayed, you can trace it back to the system of capture and take action to address the issue.

Analysing Data Performance
Data observability also involves analysing the performance of your data data rules and data movement. This includes monitoring data processing times, data transfer speeds, and other performance metrics.

By analysing data performance, you can identify bottlenecks or other issues that are slowing down your data rules. This can help you optimise your rules for better performance, ensuring that your data is available for consumption in a timely manner.

Ensuring Data Security
Data observability is also important for ensuring data security. This involves monitoring your data platforms for potential security threats, such as unauthorised access or data breaches.

By monitoring your data platform for security threats, you can quickly identify and address any issues before they become major problems. This helps ensure that your data remains secure and protected.

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