In general, observability is the extent to which you can understand the internal state or condition of a complex system based only on knowledge of its external outputs. The more observable a system, the more quickly and accurately you can navigate from an identified performance problem to its root cause, without additional testing or coding.

In IT and cloud computing, observability refers to software tools and practices for aggregating, correlating and analyzing a steady stream of performance data from a distributed application along with the hardware and network it runs on. That lets you better monitor, troubleshoot and debug the application and the network.

While observability often refers specifically to the observability of IT systems, workloads, networks, and infrastructure, data observability is another form of the technology.

With data observability, the focus shifts to the data layer. The idea is to move data quality assurance further upstream, to troubleshoot and mitigate any issues at an early stage before problems corrupt a data pool or cause systemic data quality issues. Data observability ensures confident decision-making and enables AI-driven automation by providing quality data products for trusted business outcomes.

Observability is a critical topic. This guide offers you a fundamental understanding of enterprise observability and its strategic role in managing our increasingly complex operations. You’ll find an explanation of terms, see how your efforts align to your industry peers, discover the role of observability within your company, and explore IBM® observability solutions. While this guide focuses mainly on application observability, it's important to understand that observability impacts data and networking, too, and the lines between the three are fading.

Because observability is a relatively new term, it is often used alongside monitoring and application performance monitoring (APM). All three represent ways to identify the underlying cause of problems but function differently.

Monitoring is a way to track and analyze the progress or quality of something, such as telemetry data, over a period of time.

APM tools collect metrics, traces and logs, and typically focus on infrastructure monitoring, application dependencies, business transactions and user experience.

Observability takes monitoring and APM a step further by applying context between all the assets. Hyper-intelligent agents perform an automatic discovery process for all the services and infrastructure of a distributed microservices application. This helps you understand the relationship between all the infrastructure components and the performance of the application.

Modern cloud-native applications are comprised of containers and microservices architectures, multi- and hybrid-cloud strategies, and continuous application integration and deployment CI/CD pipelines.

APM platforms were designed to accommodate code-centric, service-oriented architecture (SOA) message-based implementations. Cloud-native, containers and microservices upended those implementations, however. Why? Because they changed the focus of what needed to be measured and how it needed to be orchestrated. In other words, they lacked complete visibility and manageability.

Compared to previous application architecture generations, these cloud-native and microservices caused three fundamental shifts. They:

1. reduced direct control over application infrastructure;

2. flipped applications from being code-centric with a moderate amount of network communications to network-centric with much smaller, containerized services; and

3. created a scalability philosophy, meaning that new services and infrastructure needed to be rapidly added to accommodate high-volume application access demand, then be scaled back when the demand is reduced.

Because of architectural and implementation limitations of the original APM platform design, many APM vendors are unable to adapt to these cloud-native microservices applications. They’ve fallen short in four main areas: telemetry, tracing, automation, and scalability. But a cloud-native observability platform is designed to handle the demands of network-centric microservices architecture. It uses advanced telemetry streaming and storage architectures that observe highly distributed applications with precision.