When it comes to crisis and incident management in the cloud/digital era, HOPE IS NOT A STRATEGY! A properly setup Incident Management process should identify the incidents, provide you with Root Cause Analysis (RCA), propose possible fixes, and escalate the issue to the right SRE, DevOps, SME in a matter of minutes.
AIOps is a discipline, set of tools, and set of use cases that can help eliminate such situations and get to the root cause of a problem quickly. At the core, AIOps is expected to identify issues that experienced human IT specialists are able to, but in a time frame that is multitudes shorter than what a human is capable of. Constellation Research identified the following offerings to be pure-play AIOps solutions that provide at least the bare-minimum functionality we define in the selection criteria below.
Unplanned downtime is a nightmare for every IT executive. Long-drawn war rooms drain valuable resources and businesses lose opportunities and risk brand damage. Particularly with many choices and alternatives for any service, reducing churn by providing reliable services is a top agenda for any digital business. Having siloed teams, siloed monitoring/observability tools, multi-cloud operations, hybrid locations, blend of legacy, shortage of skilled IT analysts, and new tools all add to the issue. Constellation evaluates more than 40 solutions categorized in this market. This Constellation ShortList is determined by client inquiries, partner conversations, customer references, vendor selection projects, market share and internal research.
In digital economy, you must move fast to survive. Not in six-month release cycles. But moving with fast release cycles, continuous releases, a mature CI/CD pipeline is only a portion of the solution. If you continue to break your systems at a faster rate but are unable to fix them faster as well, you are setting up for unplanned disasters that will hurt your business sooner than later. I discuss some of the fixes in this blog.
What are the criteria for selecting a good AIOps solution? How do you compare and measure the solutions one against another? Especially when there are so many solutions out there all claiming to solve the problem better than the others! In this article, I outline the top 5 criteria that all buyers should keep in mind when considering an AIOps solution. Let me know if you have more.
The network is the foundation for all applications. With the increase in distributed applications and their hybrid nature, the network has become even more important. Delegating more and more tickets to AI will not only help reduce pressure on support team resources, but also fundamentally shift operations from being focused on reactive troubleshooting to proactive remediation.
The shine on AI and ML projects has never been brighter. The C-suite is excited about the prospects of these projects and is throwing money at them. Why then, with all this enthusiasm, can’t AI/ML projects gain more traction across the business? Why are so many of them failing? The short answer: your business executives are not getting timely insights or sometimes even the right insights.
Modern complex systems are easy to develop and deploy but extremely difficult to observe. Their IT Ops data gets very messy. If you have ever worked with modern Ops teams, you will know this. There are multiple issues with data, from collection to processing to storage to getting proper insights at the right time.
I was having a conversation with a CxO level customer as part of an AIOps/Observability workshop, and from what I could tell, most are confused about how to properly operationalize cloud-native production environments – especially the monitoring/observability portion. Here is how the conversation went.
Every business now depends on IT. Efficient IT Operations is mandatory for all businesses, especially those operating in a hybrid mode – a mix of existing data centers and multi-cloud locations. As with any business process, IT operations can be augmented with machine learning-based solutions. IT is particularly fertile ground for AI as it is mostly digital, has seemingly endless processes requiring automation and there are gigantic amounts of data to process.