William Cappelli

The path to digitalisation, however rewarding the goal, has proven to be a difficult one for many enterprises. One thing that is certain, however, is that visibility into application performance is vital not only to an effective technology strategy but to business success. The use case study to follow outlines a series of events experienced by many companies and sets down some guideposts to help you ensure that you will know the kind of digital service your customers are getting and will allow you to respond rapidly to outages and brownouts and, as a result, minimise the impact on revenue and brand.

Splunk’s observability strategy has always put AI functionality at the centre. We have always recognised that, in order to make actionable sense of full fidelity data metric, event, log, and trace data streams, human cognition requires an automated assist which is precisely what AI brings to the table. As a result, throughout our observability portfolio, customers will find a variety of machine learning and pattern discovery algorithms being put to work, separating signals from noise, surfacing patterns of correlation, diagnosing root causes, and enabling remedial responses to incidents. AI, itself, is, of course, evolving at a rapid clip and with AI Assist, Splunk adds Generative or linguistic AI functionality to the mix. But what is linguistic AI and how does it relate to the non-linguistic or Foundational AI that Splunk has deployed in its products to date?

Application Performance Monitoring (APM) as a discipline and as a collection of supporting technologies has evolved rapidly since a distinct recognisable market for APM products first emerged in the 2007 - 2008 time frame. While there are many who would argue that APM has mutated into or been replaced by Observability, it makes more sense to see APM as one of many possible use cases now able to exploit the functionalities that Observability brings to the table - particularly when combined with AI.

Let's take a look at the general relationship between Observability and ML and how ML can aid in the conversion of raw telemetry into genuine signals of events or state changes.

When considering the management of telecommunications networks, it might make sense to directly consider the observable data streams available to the practitioner instead of a model-based approach. Learn more in this blog.

In the second part of this blog we will explore how increased entropy forces us to rethink what is required for monitoring.