How Data Growth Can Help Predict IT Problems Before They Happen

By: Pam Baker| - Leave a comment

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Predictive analytics and data growth provide a competitive edge in IT operations. These methods can accurately predict how long hardware will continue to perform, and with this advantage, enterprises can minimize downtime and maximize every penny of their investment.

Consider the immense savings this capability represents: According to an IHS study, financial loss from downtime ranges from $1 million a year for midsize companies to over $60 million for larger enterprises.

Advanced Analytics Prevent Waste

Before the accuracy of predictive analytics and data growth became widely available, companies followed a replacement cycle that caused them to toss a lot of hardware in the trash as business expanded over the years.

In that heap of discarded hardware were machines and components that still had plenty of life left in them. This waste wasn’t necessarily accounted for, but it was a staggering cost nonetheless. Many businesses dealt with the dilemma by donating old IT equipment or took depreciation on their taxes. While tax breaks eased some of the financial strain, they didn’t provide the full return on investment most companies sought.

Predictive analytics gives organizations a much more precise read on how much life hardware still has left.

“The idea is to make decisions based on actual data about the health and reliability of individual components in systems as they are running in the field,” The Next Platform notes.

Before the advent of advanced analytics, companies wasted equipment to avoid the financial loss of downtime, failures and the security vulnerabilities of older machines. No company can survive long if it’s losing customers over frequent equipment failures, and it’s not helpful to suffer profit loss from lost or inaccessible information.

Data Growth Requires Tighter Control

Those who work in IT also know the high cost in man-hours dedicated to troubleshooting performance issues and application outages. Those resources could be better used to drive profits if they weren’t bogged down in keeping the lights on.

Fortunately, predictive analytics can rapidly and accurately mine mountains of machine log and management data for far less cost than human IT workers can. When you add the Internet of Things to the mix, costs drop even further, as efficiencies in IT operations management climb. Sophisticated sensors can collect the data IT needs to discern operational capacity and detect evolving performance problems.

When machine learning adds muscle to the strengths of predictive analytics, as is the case with IBM Watson Analytics, the ability to predict IT problems before they occur is greatly enhanced. The machine learns usage patterns, user demands, traffic forms and peak periods unique to your business and incorporates that information into prediction algorithms. In other words, the results improve dramatically as the machine becomes more experienced in the automated management of your business needs and operations.

As entire industries face the threat of disruption and obsolescence, improving IT operations becomes increasingly crucial to survival and profitability. In this competitive market, predictive analytics can provide improved availability, better customer service and happier management.

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About The Author

Pam Baker

Freelance Writer

Pam Baker is an award-winning freelance journalist based in Georgia. Her published credits number in the thousands, including books, e-books, e-briefs, white papers, industry analysis reports and articles in leading publications, including Institutional Investor, CIO, Fierce Markets and InformationWeek, among many others. Her latest book, "Data Divination: Big Data Strategies," has been met with rave reviews, was featured in a prestigious National Press Club event, is recommended by the U.S. Chamber of Commerce for business executives and is currently being used as a textbook in both business and tech schools in universities around the world. Baker is a "big-picturist," meaning she enjoys writing on topics that overlap and interact, such as technology and business. Her fans regualrly follow her work in science, technology, business and finance.

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