Today, everyone’s environment is a complex pathcwork of platforms, protocols, clouds, home-grown apps, new APIs, containers …

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At the same time, you’re being asked to do more with less, while keeping everything up and running smoothly. Unfortunately for most, adding more people or more specialist skills simply, isn’t feasible and simply won’t keep up with the growing complexity.

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Downtime costs organizations anywhere from hundred and forty thousand dollars to 2.5 milliion dollars per hour, so it’s important we shorten that time, that it takes to fix issues.

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Let’s take a look at each challenge and see how machine learning can be a compelling solution.

1)First the problem, it’s time. it just takes too long. How do we quickly identify the problems and resolve across complex mainframe systems, legacy apps, new platforms, clouds … We can with lots of data and people (require too many people) but it takes tremendous amount of time and effort to manually access, collect, analyze from all those disparate systems and derive some meaningful insight.   

So what if we could do it faster. Collect all that data, analyze it and surate it to a pinpoint root cause, faster across multi-source data feeds.

With machine learning algorithms that data crunching happens much more quickly, leading to root cause identification five times faster.

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2) The next problem is that the complexity demands too many skilled people. Today you probably call upon different types of experts and generalists to collect, interpret, diagnose performance and operational issues. In some cases, I’ve heard of a war room of 30 plus people, that’s a lot of people and effort. Imagine what we should be doing with highly skilled resources in our organization.

With machine learning, you can capture known patterns and develop repeatable remediation patterns that can be automated. We have shown that you can reduce the manual effort by up to 40% by only engaging generalist to triage those problems and then reduce the reliance on your experts.

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3) We are too slow to react the growing complexity in that patchwork of multiple systems and multiple network operation centers. Result more chaos, we get notified of a potential issue too late to avoid the business impacting incidents.

 

So, what you really need is proactive insight. I think you’ll agree that knowing a potential problem two hour before it becomes an actual problem would be very helpful.

Machine learning offers embedded intelligence that dynamically alerts to abnormal patterns of operation to give you that proactive warning.

In a nutshell, machine learning can help reduce that overall time, effort and skills challenges that are plaguing our IT operations.