Most enterprises today are not set up to handle IT-related incidents, or crises, in real time. The classic legacy enterprises are set up to deal with IT incidents in old-fashioned ITIL ways, without considering the cloud, software-as-a-service (SaaS) nuances, or the social media venting by customers. Newer digital-native companies do not put much emphasis on digital incident management. Read this report to understand how digital leaders are changing the game with modern Incident Management systems.
With the COVID-19 pandemic still affecting many areas of the globe, work from home is more of a mainstay than a luxury and digitizing business is more of a survival strategy than an option. Most enterprises are struggling with both of these concepts while digital-native companies are thriving. This report outlines the primary SRE trends that Constellation has observed for 2022 and beyond, based on recent and ongoing conversations with many digital CxO-level executives, SRE practitioners, and incident management team members.
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.
Most of the AIOps companies are doing the process right, some use AI and ML properly, but most fail on how to automate data processing, or DataOps, on how to get the right data to AIOps tools at the right time. In this eBook "Data Done Right for AIOps," I discuss this in detail and offer some possible solutions including Robotic Data Automation (RDA).
I am very honored to be part of the Edgevana podcast series talking to the legendary Mark Thiele on various edge, AI, AIOps, total observability at edge, and other related topics.
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.
Summary I did a deep dive vendor research report on Zebrium which specializes in automatic root cause analysis using machine leaning. Quick summary from the report: Zebrium is an Observability/AIOps platform that uses unsupervised machine learning to auto-detect software problems and automatically find root causes, reducing manual labor and speeding […]
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.