Building a private cloud? Better get an early-warning system first.

Jasmine McTigue, Principal, McTigue Analytics

March 2, 2012

4 Min Read

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Predictive Analytics For IT

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

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About the Author(s)

Jasmine  McTigue

Principal, McTigue Analytics

Jasmine McTigue is principal and lead analyst of McTigue Analytics and an InformationWeek and Network Computing contributor, specializing in emergent technology, automation/orchestration, virtualization of the entire stack, and the conglomerate we call cloud. She also has experience in storage and programmatic integration.


Jasmine began writing computer programs in Basic on one of the first IBM PCs; by 14 she was building and selling PCs to family and friends while dreaming of becoming a professional hacker. After a stint as a small-business IT consultant, she moved into the ranks of enterprise IT, demonstrating a penchant for solving "impossible" problems in directory services, messaging, and systems integration. When virtualization changed the IT landscape, she embraced the technology as an obvious evolution of service delivery even before it attained mainstream status and has been on the cutting edge ever since. Her diverse experience includes system consolidation, ERP, integration, infrastructure, next-generation automation, and security and compliance initiatives in healthcare, public safety, municipal government, and the private sector.

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