Operational analytics - Data Analytics Lens

Operational analytics

Operational analytics refers to inter-disciplinary techniques and methodologies that aim to measure and improve day-to-day business performance in terms of increasing the efficiency of internal business processes and improving customer experience and value.

Traditional analytics like Business Intelligence (BI) provide each Line of Business (LOB) with insights to identify trends and take decisions based on what happened in the past. 

But this is no longer sufficient. To deliver a good customer experience, organizations must continually measure their workload performance and quickly respond to operational inefficiencies for a better customer experience.

By using operational analytics systems, they can initiate such business actions based on the recommendations that the systems provide. They can also automate the execution processes to reduce the human errors. This makes the system go beyond being descriptive to being more prescriptive and even being predictive in nature.

On the other hand, IT infrastructures are becoming increasingly distributed adding more complexity to the workloads in terms of identifying the operational data that captures the system’s state, characterize its behavior, and finally rectify potential issues in the pipelines.

Several tools and methodologies have emerged that help companies keep their systems reliable. Every system or application must be instrumented to expose telemetry data that provides operational insights in real or near real time.

The telemetry data can be of different form of signals: logs, traces, and metrics. Traditionally this data came in the form of logs that represent a record of an event happened within an application, server or a system operation. It can be of different types such as: application logs, security logs, system logs, audit trails, and infrastructure logs. Logs are usually used in troubleshooting and generating root-cause analysis for a system or application failure at a specific point in time.

Trace signal captures the user request for resources as it passes through different systems all the way to the destination and the response back to the user. It indicates a causal relationship between all the services being part of a distributed transaction. Organizations used to develop their own trace mechanisms but it is recommended to use existing tools that support a standard trace-context propagation format. The trace-context holds the information that links the producer of a message to its downstream consumers.

Metric data provides a point-in-time measure of the health of the system, such as resource consumptions in terms of CPU utilization. Metric signals offer an overview of the overall system health while reducing the manual effort to build these metrics and store them. With metrics, system operators can be notified in real time about anomalies in production environments and establish automated recovery process in case of a recurrent incident.

The signals mentioned above have different ways to be instrumented and provide different approaches to implement operational analytics use cases. Therefore, organizations must have an operational objective in mind from which they can work backwards to identify what data output they need from their system, which tool is better fit for their business and IT environment and finally what insights are needed to better understand their customers and improve their production resiliency.