Data Observability vs. Software Observability

Data Observability is an emerging concept borrowed from control theory that measures how well internal states of a system can be inferred from its external outputs. It has gained popularity in software systems and is now spreading to data systems. Software observability tools, such as Datadog, AppDynamics, New Relic, Grafana, Splunk, and Sumo Logic, have transformed the world of software by providing a centralized view across systems, enabling easier debugging, and improving overall system performance. Data Observability is similar to Software Observability in that issues compound over time, are disruptive, require historical data for identification, and strive for reliable systems that prevent issues from occurring at the source and self-heal when issues do occur. However, Data Observability differs in its focus on data, which has weight, structure, and history, unlike software systems that have minimal marginal cost of replication, are interchangeable, and increasingly ephemeral. Data observability tools monitor machine to machine interactions as well as many machine to person interactions, making it more complex to understand the impact of issues and communicate them effectively. The ecosystem of data observability platforms is still in its early stages, with various players approaching the problem from different perspectives.