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HP Software's community for IT leaders // July 2013

The 3 stages of analytics maturity

Despite the easy availability of tools, few IT shops use analytics to power ops. But you should.

Running IT has never been a more complicated mission. Ops is told that it must serve a strategic purpose in the business, not merely keep the lights on. But both responsibilities are becoming more difficult as people connect to enterprise networks using a multitude of devices, from a multitude of places, generating more data at a faster pace. Users are also demanding a higher-quality user experience in functionality and performance, so monitoring must become more robust.
In most IT organizations, “monitoring” still means watching what happens and reacting to it. But for ops to be as effective as possible, it needs to not only monitor the flood of data users and systems are generating, but also become sophisticated at analyzing and acting on it. Although many different tools exist, most IT organizations have not yet implemented advanced and optimized analytics. Here are the three stages of data analytics, and what you gain by evolving through them.

Stage 1: Basic analytics

Most companies that make use of any analytics are in the early stages: they have simple reports on the performance of applications, systems, and the infrastructure. As a result, they generally know what’s performing—but they only find out something’s not performing when a user complains. Issues are eventually addressed, but not before they have caused problems that impact productivity or paying customers—i.e., the business. An ops team that tends only to respond to issues can never get out of the persistent reactive mode, meaning that it’s stuck keeping the lights on—and on most days not doing an optimal job of it.

Stage 2: Transformational analytics

In the second stage, transformational analytics, ops teams can take the massive amounts of data being generated and correlate it to incidents. This ability to tie cause to effect marks a big leap forward, because it gives ops the ability to solve problems much faster when they occur. In this stage, tools are aggregating the user, application, and system data for faster root cause analysis, and using the dependencies to correlate between the components and issues. With transformational analytics, you’re no longer in the dark until a user calls to tell you a problem occurred 10 minutes ago; your response to that user’s call is instead, “We’re aware of this problem, and we’re already working to fix it.”

Stage 3: Optimized analytics

In stage 3, you’re using tools that don’t just spot problems quickly—they actually alert you to potential problems before they erupt into situations that affect users. You’re tracking patterns and developing a deeper understanding of cause and effect, so that you can prevent the problems that once sidelined your entire department for a whole day. In stage 3, the frantic calls from users will be significantly lower because you’ll be predicting and remediating potential crises hours before anyone might have experienced a problem.
In addition, your data isn’t just aggregated and analyzed. It is presented in a meaningful, useful way, so you don’t need specialists just to examine, interpret, and act on performance data.

Move up the maturity ladder

Most IT operations teams have not been able to get the advanced level of operational analytics implemented because they’re working hard just to keep IT running. But the buzz around big data and analytics should serve as a big reminder: Optimized analytics matter; tools that help keep the business performing to its fullest potential can be key to helping IT ops move into a more strategic position within the business. What’s more, the tools you need to thrive in stage 3 are now available. And given the benefits of such technology, it’s time to take advantage of them.
Increase IT reliability and your value to the business by evolving your approach to ops analytics. Learn more at


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