Use Machine Learning for Managing IT Operations

Scott Mcneil, Product Marketing Analyst, Oracle Management Cloud 

In today’s fast-paced market, every business is looking for ways to improve efficiencies to gain an edge, whether it’s using technology to help streamline operations or drive innovation. One of the major problems for the typical IT organization is that it’s being asked to deliver more and more services and applications at a faster pace, while at the same time, experiencing rapid growth in their application portfolios. As a result, IT organizations are seeing more emergencies and fire drills with the onset of more web and mobile-enabled applications. 

Trying to maintain and manage the complexity and veracity of these applications is taxing on IT. Here’s an interesting figure from a recent multiuser group survey: More than 85% of customer-facing issues are reported first by users, and 32% of calls/emails about slow application performance come from senior management. 

So how do we address these challenges? Do you add more monitoring tools or IT resources to the equation? According to IDC, close to 92% of enterprise IT organizations currently have one or more monitoring tools in use, but yet 55% of those in the report recognize that they need new solutions designed for the scale and complexity of the digital business, including hybrid cloud and big data. 

As the need for new cloud-era monitoring solutions grows to keep up with the pace of change, traditional methods lack the capabilities to merge and integrate all the segregated log files and operational data that are coming in from the next-gen applications. Why is this integration important? Well, logs and operational data hold the key to unlocking true insight into what’s really happening within your applications and system environments. 

One way is to bring the data silos together under one platform so you can analyze and dissect what’s really happening. This helps to eliminate any gaps you might have, and gives you a complete picture of what’s going on inside your applications environments. 

Once you have a solid foundation of unified data, you can then apply machine learning to uncover potential anomalies and issues that would be hard to detect using traditional methods. 

Machine learning gives you the edge you need to find and detect problems automatically. It’s a natural fit for systems management and security because you can apply some intelligence to separate out the real issues from all the noise. Machine learning can help identify important patterns in your data. Plus, by clustering data, machine learning can help you focus on the most urgent issues. 

You gain a faster time to resolution for troubleshooting problems that impact users and customers. With today’s data growth exploding, you need tools that automate and remediate these issues quickly. 

One advantage to doing all of this in the cloud, is that you can significantly reduce implementation time and deployment delays from months to weeks to even days. According to industry analysts, cloud-based systems management is among the fastest-growing IT Operations and Analytics markets. Watch the video below to learn how one customer is using machine learning to monitor hundreds of thousands of transactions per second. 

Bottom line: Use technology such as machine learning to boost application and infrastructure performance and cut down time-to-resolution of issues so you can advance your business edge. 

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