No Visibility into your Critical Business Processes? Here’s what you need to do.

Many businesses run on complex processes, but the people running them often don’t have a clear view of what’s happening inside. Data comes in from many sources, yet it isn’t obvious when that data is incomplete, delayed, or not processed at all. By the time someone notices, customers may already be affected, and reports may be wrong.

This case study lays out a simple, repeatable approach to catching and fixing these hidden problems. It explains how using observability and clear key performance indicators (KPIs) can help any team see what’s going on and keep important workflows running smoothly.

The visibility challenge

Modern applications generate a constant stream of logs, metrics, and events, and specialised platforms are used to collect and analyse this telemetry. Tools such as Splunk, Datadog, and Elasticsearch are commonly chosen because they ingest large volumes of data and make it searchable for monitoring and troubleshooting.

Blind spots often show up when a business process depends on the ingested data. If the data is late, missing, or badly formatted, there is no obvious warning. Teams usually only notice something is wrong when quality scores drop or customers complain.

The result is frustration and guesswork:

It’s hard to know whether the data even arrived and whether it contained the fields the process needed.
There is no easy way to tell if each step in the process ran properly and produced the right number of results.
Reports to external systems may be incomplete or inaccurate, making internal quality metrics unreliable.

The Solution

When you face this challenge, the goal is to shift from being reactive to proactive. The solution is to implement a structured monitoring strategy using a tool purpose-built for this scenario, such as Splunk IT Service Intelligence (ITSI).

This approach involves modeling your business process as a ’service’ in ITSI and breaking down your monitoring into a logical and layered framework of Key Performance Indicators (KPIs).

A process is only as reliable as the data it uses. Your first step is to establish KPIs to ensure the health of your data pipeline.

Data Ingestion Volume: Set up a KPI to monitor if data has stopped flowing entirely. This can be done by defining the expected data volume for a specific time frame, such as hourly, for each function.
Data Delay: Create a KPI to detect when data arrives so late that it could negatively impact the process’s performance.
Data Quality: Implement a KPI to check that the incoming data contains the necessary fields that your process depends on to run correctly.

After confirming your data is sound, you need to verify the internal process itself is functioning as designed.

Execution Success: This KPI should monitor the success rate of the reports or steps that the process uses. It helps determine if the process has received the necessary input to operate.
Input vs. Output Analysis: Compare the number of input records to the number of cases or results the process actually produces. This KPI helps identify if the function is processing all the work it is supposed to.
Function Success Rate: Measure the overall success rate of the function to understand its reliability. It’s understood that some failures may be expected, and this KPI helps establish a baseline for normal performance.

Often, the most critical element is the final output. The focus here is on ensuring the results are successfully delivered.

Reporting Success Rate: This single KPI should track how successfully the completed cases from your process are reported to an external system. The assumption here is that all successful cases must be reported without any exceptions.

How to Implement This Solution in Splunk ITSI

With this KPI framework, you can build your monitoring solution. Here is what to do:

Define Services: Within Splunk ITSI, model each of your internal business functions as a separate ”service.” A service represents the end-to-end flow: relying on specific input data, producing results, and reporting them.
Assign KPIs: Once the services are created, assign the relevant KPIs you defined from the three layers to each service.
Configure Thresholds: For each KPI, configure thresholds that define acceptable performance. Some thresholds can be dynamic to adapt to changing volumes over time, while others can be fixed based on your operational requirements.
Enable Alerts: Finally, set up alerts to trigger notifications when a KPI’s performance drops below a threshold. These alerts can be configured to create tickets in an external system or send email notifications to designated teams, enabling a quick response.

By following this approach, you can transform a black-box process into a fully transparent operation. This allows you to detect any issues in near real-time, pinpoint the root cause accurately, and proactively manage the health of your most critical business functions.

How WeAre Can Help

WeAre enables technology teams to manage business-critical digital services with confidence. Our Observability as a Service (OaaS) offering provides real-time insights across your entire technology stack, ensuring systems remain reliable, optimized, and resilient. By proactively preventing problems, we help you focus on your business goals without compromising performance.

You can learn more about our Observability as a service (OaaS) from here, and let us show you how observability can help you run resilient businesses that are built for real-world success.