Data Observability vs. Data Monitoring; What’s the Difference?
Data is the currency of the modern world. And Consistent collecting and interpreting quality, trustworthy, and up-to-date customer information can mean the difference between winning and losing in business today. But, with around 2.5 quintillion bytes of data collected by organizations every day, sifting through this unimaginable ocean can be challenging – not to mention overwhelming. Moreover, poor , or data that is partial or incorrect, can result in wasted time and resources.
For companies that recently migrated to the cloud, have complex data stacks, or constantly experience data quality issues, it is time they implemented a data observability strategy.
What is Data Observability? Data observability is a DataOps process that helps data teams ensure high data quality, giving them the transparency to identify issues and the control to resolve them at the point of occurrence rather than further downstream when damage is irreversible. Observability tools and procedures enable data experts to detect, resolve and prevent data anomalies. From a broader IT context, observability is a company’s ability to understand and manage the performance of all of its systems, servers and applications.
Fundamentally, this is a measurement of the state of internal systems based on the available information from external outputs.
The term observability has existed for many years. The modern interpretation comes from software observability, where businesses gather data from program execution, the internal states of modules, and communication between components to determine if everything is performing as expected. Simply put, observability is an evaluation of the state (good or bad) of internal systems based on information obtained from outputs.
Data observability and monitoring are frequently used interchangeably with each other. While it is not entirely accurate to say that they are the same, there are overlaps between the two, and sometimes they work in conjunction. Likewise, data monitoring and observability serve similar functions as they facilitate the collection of diverse data sets to help businesses recognize problems within their IT landscape.
Nevertheless, there are important differences. Namely, data monitoring can only alert a data specialist to an issue; it can only tell someone if something is broken, but not necessarily how to fix it. Observability, however, can detect and provide details about the source of the problem. During the emergence of early IT systems, businesses did use data monitoring. But because of the ever-expanding and increasingly sophisticated nature of software and infrastructure, companies needed better visibility capabilities, leading to the reliance on data observability.
