To understand service health, IT needs to examine availability, security, performance, and log data from all layers of the ecosystem. Accomplishing this task requires observability driven by artificial intelligence (AI). Simply put, observability is the ability to look at data from all of an organization’s tools to understand the state of an application or service.
But observability is essentially impossible to achieve with legacy tools. A modern enterprise’s IT infrastructure can create tens of millions of data points every minute, and, without the right tools, this never-ending avalanche of data remains opaque, providing no insight into the state of the IT environment. The problem is not a lack of tools. IT has plenty of tools and approaches, but they are typically siloed with the mainframe, network and cloud environments, each having its own set of solutions.
So, while IT does have the ability to observe many tools, IT does not gain a holistic view. For example, when there’s an issue, the cloud team only knows what’s happening on their side. They have no idea if the application has a networking issue. In today’s extremely complex enterprise IT environments, outages may result from a combination of factors across multiple environments.
Think of it this way. To measure human health, doctors have thousands of tools at their disposal to measure body temperature, weight, blood pressure, heart rate monitors, oxygen saturation and much, much more. You may get a sense that someone is ill because their temperature is elevated, but that measurement alone will not be enough to diagnose the patient – the physician will need to bring together multiple data points to understand exactly what’s happening. Observability works the same way, bringing thousands of data points together to create a holistic view of a service’s health.
There are two main approaches to achieving observability. IT can collect data from multiple sources and then aggregate it on another platform. Alternatively, IT can standardize on a single platform to collect all data. Both approaches will work, and BMC can help either way.
BMC Helix was recognized as a leader in The Forrester Wave™: Process-Centric AI For IT Operations (AIOps), Q2 2023. The platform’s ease of integration with any data source allows Enterprise IT to connect BMC Helix to existing data and monitoring tools to bring their findings to a consolidated place. BMC Helix can also replace the tools IT is using to act as a single platform that brings all this data in. In either case, integration is the key to achieving observability, because bringing the data into the same database provides no value unless it’s possible to identify when multiple datapoints are describing the same event.
The built-in AI capability within BMC Helix enables observability of the entire environment and reduces noise so that IT doesn’t waste time on false alarms. In fact, BMC Helix can automatically resolve a significant number of issues, so IT doesn’t need to spend any time at all addressing them. With complete observability, IT can rapidly identify the root cause of issues — and potential issues — so the organization can achieve the fastest possible time to resolution. As a result, the business doesn’t miss a beat because the resources people require to accomplish their goals are available and performing well.
Interested in learning how BMC Helix can help you automate up to 30% of incident resolution? Click here to see it in action.
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