The primary challenge with big data? Figuring out what to do with so much data, of course. Collecting them, storing them, and even moving them around — no problem. Squeezing value out of them is another story.
IT operations analytics (ITOA) data is a case in point. We’ve now figured out how to generate and collect vast quantities of data, from log files to infrastructure metrics to bug-tracking data to real-time business data. Somewhere in all that dross are the gems of insight — the golden nuggets of information that can help us crack the toughest ITOA nuts, from identifying root causes of complex issues to predicting what’s going to happen next to actually preventing such problems in the first place.
Over the last few years, a number of big data analytics innovations have entered the marketplace to be sure — and perhaps the most promising of all is machine learning. The idea behind machine learning is software that can learn from experience — software that can get better with practice at whatever task we’ve set out for it, as though it were learning to play the piano.
Machine learning has an unmistakable appeal, as it gives techies a break. They no longer have to know how to solve particularly difficult problems directly. They simply have to be able to teach a piece of software how to learn how to solve the problems. Free the software to crunch your data, and lo and behold, the solution soon appears as though from thin air.
Read the entire article at https://www.moogsoft.com/blog/aiops/machine-learning-limits/.
Copyright © Intellyx LLC. Intellyx publishes the Agile Digital Transformation Roadmap poster, advises companies on their digital transformation initiatives, and helps vendors communicate their agility stories. As of the time of writing, Moogsoft is an Intellyx customer. None of the other organizations mentioned are Intellyx customers. Intellyx retains final editorial control of this article.
