Incident response versus Incident prediction
We have focused on responding to incidents when they happen, and that needs to change. The traditional role of IT Operations is to set up, fix, and monitor the applications and systems a particular organization needs. While the responsibilities include proactive work, it is understood that IT Operations is responsible for resolving issues that go wrong. As a result, the job has been focused on monitoring and response to problems.
Incident response is the traditional action and role we think about when we define traditional ITOps roles.They are the firemen of the organization. An incident response organization places its primary importance on observability and alert tools. Which tools can detect and surface a problem the most quickly? How accurate is the alert tool? Is there a lot of noise, or false alarms, which causes incidents to be overlooked? The function of all these tools are meant to help you pay attention and respond quickly when something goes wrong.
However, today’s reactionary response mentality for IT is no longer a sustainable strategy for businesses. Why is this the case? First, the architecture of applications has become increasingly complex. Operational challenges, such as CI/CD acceleration and microservices have created tooling complexity. Today, there are more interconnected systems, making it harder to have clear visibility into what is happening across the entire environment. When they go down, it can take hours and often days to find where and when the problem occurred. Many times, reactive analysis often fails to find the true root causes and the same incident is more than likely to happen again in the future.
With increasing complexity comes the need to monitor the different systems and applications that are controlling your business. Observability needs have grown as well as tools to help create visibility within these systems. While more information can be good, it comes at a cost. With new data comes data silos. Although more insights are available, they most often exist in silos. Therefore, when a problem occurs and is flagged in one system, it takes time to understand how the problem impacts all systems, how to untangle it, and where it started.
Businesses need to use a tool that has overall visibility to all tools and applications, and understand how they work together. By leveraging the right AI system to oversee and analyze all the data and information, businesses can now be proactive because their data can be used to predict incidents. The new approach, or proactive ITOps, unites and analyzes all data sources, and therefore can detect root causes before they progress into incidents. Because root causes are flagged at an early stage, the incident can be predicted instead of observed when it happens. As a result, businesses can fix problems in advance of them taking a huge impact on the bottom line.
Monitoring, by definition, is reactive. Contextualization, correlation, and analytics are needed in order to make use of the massive amount of data and observability information now available. Once these elements are applied to massive amounts of information, you can be proactive about your ITOps focus.
Other Helpful Resources
Unified Intelligence Engine (UIE): A Technical Deep Dive Paper
InsightFinder utilizes the industry’s best unsupervised multivariate machine learning algorithms to analyze a large amount of production system data.
Root Cause Analysis
A major credit card company’s mobile payment service experienced severe performance degradation on a Friday afternoon.