The observability of data is critical to maintaining confidence in the quality of that data. Tracking the data throughout its journey can help identify any issues and rectify them before they cause problems. By extending data observability, organizations can increase their confidence in the data and avoid any negative impacts on the business.
Data quality is essential for making sound decisions and taking appropriate actions. However, data does not always come from trustworthy sources, and it can be tough to tell if the data has been tampered with or if it’s accurate. This is where data observability comes in.
Extending data observability means tracking the data as it moves through different systems. By observing the data, organizations can identify any issues and take steps to correct them. This is especially important when dealing with sensitive data, such as financial information or personal health records.
There are a few different ways to extend data observability. One way is to use a centralized logging system. This system collects logs from all over the organization and stores them in a central location. This makes it easier to track the data and identify any issues.
Another way to extend data observability is to use a distributed tracing system. This system tracks the journey of a request as it moves through different services. This allows organizations to see how different parts of the system are interacting with each other and identify any bottlenecks.
Extending data observability is crucial for maintaining confidence in the quality of that data. By tracking the data throughout its journey, organizations can identify any issues and take steps to fix them before they cause problems. There are a few different ways to extend data observability, such as using a centralized logging system or a distributed tracing system. Whichever method you choose, extending data observability will help you increase your confidence in the quality of your data. https://dqo.ai/