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).
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.
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.