Why Root Cause Analysis Doesn’t Work Without SAP Telemetry — A Developer’s Perspective
This article was authored by Seth Marek,PowerConnect’s Lead Splunk Developer at RHONDOS.
Before I built features for PowerConnect, I used it.
Consider this scenario: A business transaction slows down at month-end. Instead of paging Basis for a manual trace, the slowdown can already be broken out by transaction code, user, and step, correlating with the infrastructure events happening underneath.
I began my career integrating PowerConnect’s uniquely powerful SAP telemetry for a Fortune 100 enterprise, on occasion sitting in the same war rooms many readers of this blog have sat in. I helped build a vast array of monitoring, diagnosing everything from hardware performance hiccups to integration errors with middleware. SAP is simultaneously the beating heart of an organization’s business and its single largest (and often most complex) blind spot, and learning that something is wrong is just the beginning. I have helped compress that journey from days to mere hours—even minutes. That experience is the reason I work on this product now, and it shapes a lot of the design decisions we make for PowerConnect.
Let’s talk about the SAP visibility gap that tends to cost enterprises the most — in time, in cost, and in the credibility of the operations team itself. Let’s talk about Root Cause Analysis.
The RCA problem most enterprises share
An order won’t post. On the infrastructure side, nothing looks wrong. With PowerConnect sending full SAP telemetry in the platform, a failed update task surfaces at the same second the customer hit submit, correlated with the API call, the CPI iflow that delivered the message, and the database lock that caused the failure. What used to be a multi-team investigation could very well become a single view.
Root Cause Analysis (RCA) is fundamentally a correlation problem. You have telemetry from infrastructure, application performance, middleware, network, and the cloud platforms underneath. When something breaks, the work is to align those signals across a timeline and find the originating event. Modern observability platforms — Dynatrace, Splunk, Elastic — are very good at this when the telemetry is complete.
The trouble is that for many large enterprises, SAP is hardly in the picture. The systems that actually run the business — order to cash, procure to pay, finance, manufacturing — sit behind a wall the rest of the stack can’t see through, burdened by cumbersome, limited monitoring. When an incident happens, the infrastructure team can identify symptoms quickly: a CPU spike, a slow API, a queue backlog. It simply is not the full picture. They have to go deeper.
Look again: API latency that doesn’t add up. The application team sees the latency clearly. Usually, that’s where the trail ends. With SAP context, the latency often correlates with a long-running background ABAP report consuming system resources — something the API team would never see on their own, and something the Basis team can act on immediately.
Whether it is a stuck update task, a swarm of IDoc processing errors, an ABAP runtime issue, or a CPI iflow timing out — without telemetry providing immediate visibility behind the curtains of the SAP environment, the team is left correlating by hand. They sift through SM50 traces, check ST22 dumps, run custom queries against HANA, and try to align all of it against their SIEM timeline manually. It does work, but it takes time — sometimes days — and by the time they find the actual cause, the business impact is usually already done. They assess the damage, write their reports, manage the expectations of the broader organization, and try to improve their processes for the next incident.
What changes when SAP telemetry is in the platform
PowerConnect removes the blind spots and facilitates data correlation faster than ever before. It collects and reveals a veritable gold mine of SAP information — application logs, ABAP runtime data, performance metrics across ECC, S/4HANA, BW, and HANA, transaction data, business events tied to specific orders and documents, CPI and BTP integration telemetry, and operational data from work processes, batch jobs, IDocs, RFCs, and update queues — directly into the observability platform the rest of the enterprise already uses.
A middleware bottleneck is slowing the business. Queue depth is climbing, but where? PowerConnect timestamps every IDoc and tRFC as it moves through SAP, making it instantly clear whether the bottleneck is in the source system, the integration layer, or the receiving application. The conversation stops being “who owns this” and starts being “fix this.”
Every record is timestamped and lines up cleanly with the infrastructure, network, and application data already in the platform. That alignment is the foundation of every RCA improvement that follows. Once SAP signals carry the same timestamps and structure as everything else in the observability stack, RCA stops being a reconstruction project. You can get to the real investigation faster.
A world of difference
The number we hear most often from Fortune 500 customers is around 80% — that’s the typical improvement in their RCA timelines once timestamped SAP telemetry is flowing into their observability platform. The work doesn’t just move faster; it moves more accurately, because you’re no longer reconstructing the timeline from memory and inference. The data is already there, in the right order, with the right context.
Why AI-assisted RCA needs SAP data
I’d add one observation about AI, since it comes up in almost every conversation we have right now at RHONDOS. The platforms doing AI-assisted RCA — Davis AI in Dynatrace, ML-driven correlation in Splunk, anomaly detection in Elastic — are only as good as the data they’re reasoning over. When SAP is excluded, the model is working with an incomplete picture of the enterprise. It will identify the symptom reliably and get the cause wrong often enough to erode trust in the system.
When PowerConnect’s SAP telemetry is part of the model, that changes. Davis can explain SAP incidents in context. AIOps can connect a business event to an infrastructure event without an engineer doing the linking by hand. Connecting your SIEM to leading AI technologies via an MCP server can piece information together at unprecedented speed. These tools finally have visibility into the system the business actually runs on, and the quality of their conclusions shifts accordingly.
Closing thoughts
The reason I work on PowerConnect is that I spent enough time integrating it to understand what the SAP visibility gap actually costs. It isn’t just slower incident response or cumbersome half-measures — it’s the gradual erosion of confidence between operations, application, and SAP teams, each of them looking at different data, none of them seeing the whole picture. Closing that gap doesn’t make monitoring better in some abstract sense. It makes operational decisions faster and more accurate, and it keeps mission-critical processes running when they would otherwise stall.
If any of this sounds like the environment you’re working in, PowerConnect may have the data you need to change your baseline reality - Request a Demo Here.
QRFCs are one of SAP's most frustrating troubleshooting black holes — logs only exist while the problem is happening, so by the time you notice dropped orders or failed calls, the evidence is already gone. PowerConnect flips that equation with full-fidelity, time-stamped data capture that lets you rewind, see exactly when and where errors fired, and pull the RCA logs you need to actually fix it. In the video below, the great and powerful Ben Dare walks through a hands-on example of how PowerConnect feeds this data into your big data platform to make real-time RCA a reality.
Watch here: https://www.youtube.com/watch?v=2LT6JYsjNYM