Predictive Analytics For IT

Manufacturing, financial, retail, and pharmaceutical companies have long used predictive analytics for everything from anticipating parts shortages to calculating credit scores. But across industries, the software can also warn IT organizations about potential system failures before they affect the business.

For enterprise IT, we're not advocating all-purpose analytic workbenches that demand deep, and expensive, data mining and statistics expertise. Rather, we're recommending that organizations consider a focused analytics suite, such as Hewlett-Packard's Service Health Analyzer, IBM's SPSS-powered Tivoli product, Netuitive's eponymous offering, and systems from the likes of CA and EMC. All these products come with ready-made dashboards, reports, alerts, and key performance indicators set up for IT system and application measurement and prediction.

Just tap into your data sources, tune the dials, and start seeing the future.

Poised For Growth

"Everyone has expected an explosion in 'analytics for IT' for some time," says David Stodder, director of business intelligence research at TDWI (The Data Warehousing Institute). "But from what I see, the usage is still fairly selective." However, embedding analytics in IT management consoles will help get these capabilities into more shops, Stodder says, as not only HP and IBM but also specialized application performance management providers incorporate analytics.

David Menninger, research director at Ventana Research, agrees that adoption of predictive analytics systems--for IT or any other purpose--is still modest. Of the 2,400 organizations in Ventana's Business Analytics benchmark research, only about one in five is using predictive IT analytics.

So most IT organizations have lived just fine without these suites until now. What's changed?

The push toward private clouds and service-oriented IT, combined with an unprecedented number of data sources and the attendant complexity. A highly virtualized infrastructure, big data, and predictive analytics go together. In fact, big, clean, fast-moving flows of real-time information are the lifeblood of predictive IT analytics systems.

Unfortunately, IT hasn't always been good at collecting operational data. In the age of single-server, single-application architectures, infrastructure fragmentation made it prohibitively costly to build data silos filled with accurate transactional information on which to perform analysis. But virtualization and the cloud model have changed everything. Centralization is back, and better than before.

However, a cloud architecture--public, private, or hybrid--also brings complexity, which is the enemy of uptime. And this is the main reason we think predictive analytics will become a must-have for enterprise IT sooner rather than later. Consider that in our most recent InformationWeek Virtualization Management Survey, respondents ranked high availability No. 1 among a dozen features. Similarly, the most-cited driver for private clouds is improved application availability.

Predictive IT analytics can get us to higher availability by helping us cut through the complexity inherent in modern cloud infrastructures, which are built one layer at a time, with each layer dependent on the one before it. This complexity makes it difficult for even experienced network architects to understand the interrelations among infrastructure components. It also makes failures substantially more difficult to troubleshoot. We'd better get a handle on this problem now, because complexity will only increase as enterprises adopt more advanced converged architectures.

Strategy: Predictive Analytics for IT

Just Like Magic
Our full report on predictive analytics for IT is free with registration.

This report includes 17 pages of analysis. What you'll find:

• A deepdive into the inner workings of predictive IT analytics systems
• How to know if your network is ready to support analytics

Get This And All Our Reports

To read the rest of the article,
Download the Mar. 5, 2012 issue of InformationWeek