IT Brief Australia - Machine learning poised to transform Australian IT

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Machine learning poised to transform Australian IT

Machine learning software is set to transform the way IT professionals manage their infrastructures in large Australian organisations, by seeking out potential problems before they affect any single user, Bede Hackney, ANZ managing director at Nimble Storage, says.

Machine learning replaces traditional IT systems that require constant monitoring of each component, which means technicians don't have to waste time working out where the fault is and forming a solution.

According to Hackney, the 'app-data gap' is the challenge IT management faces when gaps between application and data stores become a problem because of the many differing IT infrastructure components.

“A major app-data gap can often disrupt data delivery, degrade worker productivity, create customer dissatisfaction and damage a company’s overall speed of business. However, it can be difficult to quickly find a solution because the factors leading to application slowdowns can come from a range of issues across the infrastructure stack”, Hackney says.

Usually the problem is diagnosed as a storage issue, but that may not always be the case, according to Nimble Storage Labs Research Report.

The report, which analysed whether machine learning could prevent IT downtime, found that 54% of all problems were not caused by storage. Instead, the study found that 28% were configuration issues, 11% were interoperability issues, 8% were non-storage best practices that affect performance and 7% were host, compute or VM-related issues.

The 46% of cases that were storage issues were attributed to hardware and software problems, software update assistance and performance setbacks. The report studied 12,000 cased of app-data gap-related issues in the Nimble install base.

“To actively close the app-data gap that is occurring inside many organisations, we believe IT teams need to leverage predictive analytics that incorporates both data science and machine learning to optimise the performance and availability of their applications," Hackney says.

Hackney believes machine learning will help with early detection and management of poor performance, solve problems and help with continuous improvement for the user experience. They will also allow IT professionals to focus on more important issues than manual troubleshooting.

Machine learning can:

  • Predict downtime and IT lag long before they happen
  • Prevent negative effects of any problem instead of reacting to them
  • Prescribe clear resolutions for IT professionals when machine learning can't fix problems
  • Provide root cause analysis if automatic solutions cannot be found
  • Collect and predict information across the IT network through the use of cross-stacking analytics application software
  • Provide advanced analytics for IT support engineers on the front line, level 1 and level 2 tiers, with the possibility of consulting level 3 engineers if the right systems are in place.

These, according to Hackney, will improve performance of crucial IT systems, closing the app-data gap through predictive analytics. Hackney concludes by saying “as their usage grows within organisations, the benefits to IT teams - and the users they support - should become widespread. We believe machine learning will quickly become the future of effective infrastructure management.”

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