What is business observability?

Business observability aligns IT operations with broader business goals. Enterprises must continuously collect, monitor and analyze data from an array of sources, including infrastructure, software applications, customer interactions and business events.

Unlike traditional monitoring practices, which simply flag problems and report on predefined performance metrics, business observability strategies integrate and correlate data from across the organization to create a fully contextualized, comprehensive view of business operations. With business observability tools, teams can predict disruptions and maintenance issues, automate remediation workflows when issues arise and offer suggestions for optimizing customer interactions.

As such, business observability solutions enable enterprises to transform raw operational data into actionable business intelligence, empowering leaders to optimize business processes, improve customer journeys and make data-driven decisions in real time.

Modern businesses and their computing networks are complex, involving dynamic traffic flows, distributed architectures, cloud-native applications and geographically dispersed business teams.

Observability arose as a formal discipline to help organizations gain more insight into their intricate IT systems. Business observability applies the principles and practices of IT observability to the overall business, with the aim of ensuring that IT resources, operations strategies and team priorities are all working toward overall business goals.

The term “observability” comes from control theory, an engineering theory concerned with automating control of dynamic systems (regulating the water flow through a pipe based on feedback from a flow control system, for instance).

In IT, observability provides deep visibility into modern, distributed tech stacks for automated, real-time problem identification and resolution. The more observable a system, the more quickly and accurately IT teams can determine the underlying cause of network and application performance issues, often without extra testing or coding.

Observability insights help organizations make informed decisions, anticipate future needs, allocate resources more efficiently and fortify cybersecurity defenses. They enable businesses to adapt to changing network demands and confidently manage their digital infrastructure, even as conditions evolve.

Business observability takes observability approaches a step further. Whereas traditional observability is primarily concerned with the technical layer, business observability integrates technical signals with real-time business metrics, such as revenue, conversions, churn and customer experience. It enables business leaders and teams to determine whether IT systems are running optimally and understand how IT system health impacts core business outcomes.

Business observability strategies and solutions are typically tailored to fit each organization’s needs, but they tend to include certain key processes and features, including:

KPIs—quantitative values that indicate progress toward performance goals—help define the business objectives that observability efforts should support.

In terms of business observability specifically, KPIs help align the company’s strategic priorities—such as increasing sales or maximizing customer satisfaction—with both business and technical stakeholders.

To maximize customer satisfaction, for example, the business might use net promoter score (NPS) as a business KPI and mean time to repair (MTTR) as a technical KPI. NPS enables enterprises to measure how likely customers are to recommend the company to other people, and MTTR tracks the average time it takes IT teams to address incidents and service requests submitted by end users.

Determining the appropriate KPIs typically involves identifying the underlying processes, workflows and data pipelines that directly affect these objectives. Establishing KPIs enables teams to trace a clear path from high-level goals to the technical systems and concrete actions that make these goals attainable.

To achieve observability, businesses must collect massive amounts of telemetry (from applications, servers, databases and microservices) and business data for deep visibility into business performance.

Telemetry refers to the metrics, logs and traces that form the “pillars of observability.” Metrics are raw, derived or aggregated quantitative measurements that speak to system performance and health—of a server or an application programming interface (API), for instance—over specific intervals of time.

Logs are timestamped textual records that detail every event and action that occurs within the network. They provide granular information about what occurred, when it occurred and where in the network it occurred, creating valuable context for troubleshooting, debugging and forensic analysis.

And traces capture the flow of data across the network, providing real-time insights into the path and behavior of packets as they traverse multiple devices and systems. Traces enable IT and DevOps teams to see the full journey of a transaction, end-to-end, helping pinpoint routing delays and failures within complex, multilayered environments.

Custom business metrics round out the dataset by capturing product- or domain-specific KPIs (sign-up rates, for example) from data warehouses, enterprise resource planning (ERP) and customer relationship management (CRM) platforms, customer support tickets and point-of-sale (POS) systems, among other sources.

These signals go beyond technical health, embedding business context directly into observability workflows, helping teams monitor, correlate and optimize actions that drive business impact.

Data contextualization enriches IT and business metrics, logs and traces by providing additional information about the business and network ecosystem (topology, device roles and application dependencies, for instance). Without context, raw data lacks actionable meaning.

Context enables IT teams to correlate network events with specific applications, users and business decisions, eliminating data silos, facilitating targeted troubleshooting and enabling informed decision making.

For example, a sharp drop in monthly sales might be alarming in isolation. But contextualization helps businesses understand how traffic patterns, regional events and holiday benchmarks impact sales figures. If the drop coincides with a holiday weekend where customers typically travel out of town, it can indicate that the sales dip is a temporary—but unavoidable—fluctuation, instead of a systemic issue that requires a targeted solution.

In the analysis phase, observability platforms aggregate and correlate telemetry data and business performance data from across the enterprise.

Correlation connects the dots between metrics, logs, traces and contextual data to present a holistic view of the IT environment and the enterprise. It helps IT teams identify relationships between events and across different layers of the business, revealing the underlying patterns that shape operational and business outcomes.​

Connecting seemingly unrelated data points through correlation also enables faster root cause analysis and more effective responses to network issues and business challenges. Correlation can, for example, help business and DevOps teams trace cascading IT failures back to specific business decisions.

Imagine a new baggage handling system fails at a major airport. Business observability tools can map the failure back to the airport executives’ decision to fully automate baggage handling in all airport terminals without implementing a centralized change control system for its baggage tracking software or establishing a centralized decision-making apparatus for executing the automation strategy.

Specifically, different sub-teams across the airport—all of which report to different leads—made conflicting baggage management decisions. The decentralized ecosystem allowed thousands of discrepancies to accumulate in the tracking software, which led to thousands of misrouted and lost luggage issues.

Machine learning (ML) and artificial intelligence (AI) technologies play a significant role in the analysis process.

AI-driven observability tools enable continuous analysis of outsized telemetry datasets from on-premises data centers and cloud environments, providing broader visibility into network activity.

Teams can also leverage ML algorithms to help observability solutions learn operating baselines, detect anomalies, predict failures and provide remediation guidance. These capabilities enable enterprises to predict potential issues before they cause operational disruptions or affect the user experience.

Business observability tools often provide dashboards and visualization tools that present complex data in an intuitive format. Visualizations such as heatmaps and data flow diagrams can help teams quickly assess IT systems and progress toward business objectives.

Alerts are automated notifications triggered by specific conditions or thresholds. Many observability solutions even provide intelligent (AI-powered) alerting mechanisms that can distinguish between critical incidents and minor anomalies, reducing alert fatigue and helping business and IT teams focus on the most impactful issues.

Business observability enables enterprises to get granular, actionable insights on how to optimize IT environments and operations alongside and in support of business processes. It can prove invaluable for a range of use cases, including:

• Better decision making. Business observability practices provide business teams with relevant, real-time data (instead of outdated reports), enabling leadership to remain agile and make data-driven business decisions.

• Increased operational efficiency. Business observability automates reporting on business performance, enabling granular measurement of business processes and helping teams streamline operations.

• Proactive anomaly detection and issue resolution. Continuous observability data streams help enterprises proactively find anomalies and performance bottlenecks and identify opportunities for improvement, before inefficiencies affect the bottom line.

• Comprehensive root cause analysis. The advanced analytics observability tools provide helps teams understand the “why” behind ITOps and business performance issues, enabling faster troubleshooting and decreasing system downtime.

• Faster innovation. Observability insights help leaders and teams focus on growth and innovation, instead of firefighting operational problems, so companies can innovate faster and launch new products with greater confidence.

• Improved customer experience. By detecting customer friction points and transaction issues in real time, companies can resolve problems swiftly and with minimal—if any—disruptions to the user experience and maximize customer retention.