Focus on SAP Performance: When Processes Meet Observability

S/4 Hana and ECC run in parallel; Rise projects are changing operating models; and BTP and interfaces tightly integrate SAP with non-SAP systems. These dependencies make end-to-end transparency all the more important.

SAP performance issues are not traditional defects, but rather system-wide effects. A bottleneck arises where transactions, batch runs, and integrations intersect—often precisely at the times when the business is most sensitive. Month-end closing, shipping waves, price updates, or campaign workloads alter the patterns according to which systems operate. This additional pressure can cause instability in the critical process path.

Added to this is the technical complexity that surrounds SAP today. The database and application layers are only part of the bigger picture. Identity and authorization systems, network latencies, API gateways, middleware, and cloud services also influence response times. Anyone looking to identify the root causes must therefore examine the entire transaction path, and this is precisely where modern observability comes into play.

Monitoring Hybrid Landscapes

SAP monitoring has proven its worth over decades. It reliably shows whether a system is available and where bottlenecks exist. In hybrid environments with numerous integration points, however, any single view reaches its limits. Not because the monitoring is inadequate, but because dependencies across system boundaries provide the crucial context. The fundamental problem is data isolation: teams each see only a partial view, and the process context is missing. This makes it difficult to consistently link cause and effect throughout a transaction. Today, integrations are often the decisive factor—iDocs that aren’t processed, jobs that fail, or BTP services that become unstable under load. This is where disruptions arise that slow down business processes. In practice, this often manifests as symptoms that initially appear to be an internal SAP problem. A transaction slows down because an external service responds with a delay when called. A batch window shifts because an upstream job is running too long and blocking subsequent processes. Or iDocs pile up because retries cascade and interfaces go into an error state under load. Without an end-to-end context, the only option left is to search within individual views.

Transactions in a Process Context

In hybrid SAP environments, looking at individual systems is no longer enough. Observability must make transactions visible along their actual paths and correlate technical signals. Only when metrics, logs, traces, and business events are analyzed together can bottlenecks be reliably pinpointed. A key factor here is having an up-to-date view of dependencies and their impact paths. When it becomes clear which database, application server, interface call, or job is slowing down a transaction, troubleshooting becomes transparent. Teams then no longer focus on the symptom but on the root cause within the workflow.

For this to succeed, it takes more than just a collection of individual data points. A modern observability pipeline must consolidate signals from SAP, the integration layer, and the infrastructure, and correlate them in such a way that technical anomalies can be traced back to a clear cause. The key is the ability to analyze transactions along their paths, keep dependencies up to date, and integrate process signals in such a way that prioritization is based not on the volume of alerts, but on the impact on critical processes.

AI-Powered Observability

In hybrid SAP environments, traditional thresholds are often insufficient due to dynamic load profiles and shifting dependencies. A static threshold may be too low at the next scaling level and too high in the next batch window. AI-powered analysis therefore does not primarily rely on fixed limits, but rather on behavioral patterns. It detects deviations from the normal state, correlates signals, and accelerates root cause analysis. Prioritization based on business impact is key here. Not every deviation is an incident, and not every technical anomaly affects a critical process path. Relevance only emerges once it becomes clear which transaction is affected, whether the disruption impacts process metrics, and whether a trend is emerging. Particularly in SAP environments, where dialog transactions, background processing, and integrations operate simultaneously, this classification determines whether teams take targeted action or get lost in parallel hypotheses.

Correlation Does Not Imply Causation

Correlation is a valuable indicator, especially in distributed environments where many anomalies can occur simultaneously. The crucial step, however, is to infer causal relationships from temporal coincidences. An increased response time in the SAP dialog, a peak in the database, and an error rate in a connected service can be temporally correlated without all three having the same cause. Relying solely on correlations here risks false escalations. This wastes time and creates friction between teams. Causal analysis focuses on dependencies and causal relationships. It asks which component is actually on the critical path and which downstream changes are affected. This is the difference between an alert that merely describes that something is unusual and a diagnosis that explains why the process is failing. Technically, a unified tracing model is essential for this—for example, one based on OpenTelemetry. This allows SAP transactions to be viewed alongside non-SAP components within the same logical framework, enabling the tracing of root causes along the path.

A Common Language for Observability

An often-overlooked aspect of modern SAP observability is the question of the target audience. Traditional monitoring speaks the language of IT: CPU utilization, response times, error rates. This is relevant for Basis teams and SREs, but it’s of little help to the business unit, which wants to know whether the order-to-cash process is running smoothly.

Business Events bridges this gap. Technical telemetry data is supplemented with business-relevant events and metrics. This makes it possible, for example, to see how many orders were processed within a given time window, how many iDocs are in error status, or how the turnaround time of a posting process has changed compared to the reference period.

This process view can be integrated with technical signals so that teams focus on addressing the root causes in the transaction flow rather than just the symptoms.

This changes the basis for decision-making. When the core team, SRE, SAP support, and the business unit all access the same data, a common language emerges. Priorities become more objective because the business impact of an outage is visible. And the question „Is the system healthy or unhealthy?“ is replaced by the more relevant question „Is the process working or failing?“.

Revised Incident Process

Many organizations have refined their root cause analysis over the years. In hybrid SAP environments, however, this approach reaches its limits: An incident triggers a response in the war room, multiple teams check their tools in parallel, and the result is often a hypothesis that is only verified after the fact.

A shared observability view changes the approach. Instead of isolated screenshots, a unified event timeline emerges from technical and process-related signals. This reduces the need for follow-up questions and speeds up handoffs between teams. Post-incident reviews also become more robust because cause and effect can be traced along the transaction path. In this way, incident management gradually evolves into a learning operational model.

Modernization Consultant

Rise projects shift dependencies and operational responsibility. That’s why observability before, during, and after migration is a critical success factor. Before the move, the focus is on baseline values and critical paths. Which transactions are business-critical, what are normal response times, and which interfaces determine process throughput? Without this reference, any deviation will later become a topic of discussion rather than a diagnostic tool.

During the parallel phases, comparable measurements are essential. If parts of the landscape are already running in the cloud and others are still on-premises, discrepancies in latency, error rates, or throughput must be identified early on. Otherwise, a problem won’t be noticed until it reaches the business unit.

After go-live, observability remains critical because cloud environments change more rapidly. Scaling, new dependencies, and changing integration patterns can give rise to new classes of errors. End-to-end visibility helps to identify these effects before they become entrenched as process risks.

Enablers of SAP Modernization

SAP monitoring is not an end in itself. In hybrid environments, looking at individual metrics is not enough. Observability helps explain the causes of problems and prioritize them within the context of the process. Anyone looking to refine their monitoring strategy should therefore not start by seeking more metrics, but rather by identifying reliable correlations.

Significant progress occurs when technical telemetry, transaction paths, and process signals are brought together in a single view. Then, the question of „Is the system healthy or unhealthy?“ becomes one that truly impacts operations. Is the process working, and if not, what is the cause? (Source: Dynatrace)