AIOPS
The importance of visibility in AIOps for digital transformation The concept of digital transformation is a highly complex one. It means many things to many people, and involves a variety of complex environments and infrastructures from hybrid to multi-cloud, containers and more. To do this at scale, requires automation. To have any hope of managing digital transformation properly, forward-thinking organisations are also l ooking to machine learning and AIOps. BY PAUL BARRETT, CHIEF TECHNOLOGY OFFICER, ENTERPRISE, NETSCOUT AIOps, however, is not a panacea for all of an organisation’s digital transformation ills, and what’s crucial to making it work as intended is the concept of observability, which I like to think of as the degree to which you can understand the internal state of a system.
Good AIOps and bad AIOPs What separates good AIOps from bad AIOps is the quality of the data that you’re feeding into it. If you’ve got a system that has good visibility, one can generate high quality data about what’s happening, which can then be used to underpin machine learning and automation. But if high quality data isn’t driving machine learning and automation, one can’t expect good results.
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The old IT adage of ‘garbage in, garbage out’ is still as relevant now as it has ever been. Ultimately, automation has been defined by a human being whether they put instructions into a user interface or whether they wrote a configuration file or a script. If we give the wrong instructions, or there’s errors in instructions that we give our automation system those errors will get replicated at scale. Without visibility, and the high-quality data that enables, things can go wrong fairly quickly.
Loss of visibility increases the risk of unintended consequences One example I always like to go back to in my discussions on automation is the stock market crash of October 19th, 1987. One of the primary causes