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IT Operations Analytics (ITOA) provides real time monitoring of large volumes of data

Ray Solnik | July 8, 2014
This vendor-written tech primer has been edited by Network World to eliminate product promotion, but readers should note it will likely favor the submitter's approach.

This vendor-written tech primer has been edited by Network World to eliminate product promotion, but readers should note it will likely favor the submitter's approach.

Now that you have initiatives to migrate to the cloud, extend to the mobile web and integrate all the pieces from the bring your own device (BYOD) movement, the next hurdle is going to be keeping an eye on all these moving pieces.

IT Operations Analytics (ITOA, what some people call Big Data analytics or Advanced Analytics) has been dubbed the solution to bring order to this rapidly growing complexity. Some are asking "What does this term really mean, what's the big deal?"

Here's the big deal: the advantage to ITOA is it allows real-time monitoring of huge volumes of data and makes sense of it for you. You can now:

  • Know what's going on with your full IT / application stack at a glance.
  • Prevent incidents and crises before they happen.
  • Shorten response times to incidents when they do happen through automated responses.

While predictive analytics have been promised by the industry before, the promise is finally being met. Conventional analytics and problem-solving responses generally only respond to events that have occurred. The new generation ITOA solutions, rather than just spewing out mountains of system stats that you have to spend hours combing through after an event has occurred, can send a targeted message, in real-time, warning that a specific KPI (key performance indicator) is deviating from norm.

Will Cappelli, a leading IT operations management software analyst and Research Vice President at Gartner, explains that at the heart of these ITOA systems are pattern matching technologies and analytics engines that can use complex event processing (CEP), machine event and log indexing and search, behavior learning engines (BLE), architecture mapping and discovery, and multidimensional analysis databases.

These technologies (application behavior learning engines in particular) enable the capture and analysis of multiple variables to generate patterns and identify deviations from those patterns - the keys to predictive analytics in the IT application data center stack. An example of these variables include:

These metrics and usage patterns determine how your system is supposed to behave and then compares that assessment with what is actually going on at any given moment.

Interestingly, once you prevent problems from happening, you can creatively "raise the bar on your IT offense" as well.

Consider the rout of the Denver Broncos by the Seattle Seahawks in the Super Bowl last February. Seattle was known for their best-in-class defense. It was this defense that enabled the Seahawks to shut down Denver, build the confidence of the entire team, and take more risks on offense.

For consumer marketing, social networking, data centers, financial systems the proper use of ITOA is going to have a huge impact in the next 10 years, as companies learn that they will be able to head off problems before they blow up into full-on crises. They will also leverage analytics to enable data center and application operations to have an increasingly direct impact on business results.

 

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