The rise of machine learning in mobile strategies

By Jonathan Crowl

As a technology poised to transform the enterprise, machine learning is booming. One recent report from IDC estimates the market for machine learning will hit $12.5 billion by 2019. Adopting businesses are leveraging machine learning for purposes ranging from asset management to cybersecurity, even to more complex processes such as risk management.

This advanced technology has a number of applications where mobile strategies are concerned. Despite this opportunity, most business leaders have yet to embrace this advanced form of computing, which is capable of using data to learn and make decisions without being programmed to perform specific functions. According to TechRepublic, 67 percent of technology leaders report no plans to implement machine learning for their businesses. Concerns about supporting the technology play a significant role: A Tech Pro Research survey of technology workers and leaders found that 42 percent felt their companies were unequipped with the skills needed to support machine learning.

Adequate employee expertise and training is a legitimate concern, but enterprises only hurt themselves when they turn their backs to machine learning. Where enterprise mobility is concerned, machine learning offers unprecedented processing power alongside state-of-the-art cybersecurity protection. Like any technology, machine learning has its limits, but the benefits outweigh the drawbacks. It’s only a matter of time before the majority of companies get on board.

Automated data management

Most companies looking to use machine learning are hoping this computing solution can improve how they handle their data by managing incoming data, analyzing data and continually learning from this flow of data to become more intuitive and effective at data management. Machine learning can automate aspects of data management that save time while improving efficiency and productivity.

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According to ZDNet, machine learning is a powerful tool being harnessed by enterprise mobility management (EMM) providers, offering unprecedented capabilities in threat detection and response. Through its analysis of patterns and behavior, machine learning also makes it possible for security systems to effectively respond to threats they have never encountered before. This gives businesses a better chance at combating threats they aren’t even aware of.

On the analytics side, machine learning provides greater computing power than any analytics solutions you are likely using now, enabling your company to improve everything from its enterprise device management to its daily operations or sales and marketing performance.

Enhanced EMM security

The ongoing challenge of cybersecurity is that no company is completely bulletproof. Even with the best minds on your side, security breaches are possible. The best any company can do is make sure its security front is as state-of-the-art as possible.

In this regard, machine learning can be a boon for your cybersecurity efforts. Though EMM platforms are designed to offer more security to a company’s mobile network architecture, machine learning can be leveraged to add another strong layer of protection over that EMM. As TechCrunch explains, machine learning security solutions flex their value most when covering a complex IT architecture where potential breaches and threats can be harder to detect.

In some cases, machine learning can beat back security threats that otherwise might have compromised your EMM, while detection of active threats can happen faster, giving the system administrators a head start in responding to the threat. Machine learning powers security solutions that are faster and more responsive than other leading security products.

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Acknowledging machine learning’s limits

Machine learning has many applications where data management and system security are concerned, but it does have limits enterprise leaders must be mindful of. According to ZDNet, machine learning’s intuitive analysis of data doesn’t translate well to the development front, where both machine learning and AI have struggled to write code that serves the parameters set by humans.

Narrow scopes of responsibility are effective channels for using machine learning, since this computing system is effective at outputting solutions to specific problems. However, it struggles to effectively make decisions and lead creation when those parameters open up. It performs poorly with a blank slate, which is why development has found it so hard to leverage. Another illustrative example is how machine learning supports sales and marketing. While it can analyze performance metrics to indicate where you are struggling and why, it can’t actually implement those changes.

Still, machine learning is a valued tool at the enterprise level, and its varied applications make it a worthy investment that boosts productivity, supports internal intelligence, upgrades data management and strengthens security for mobile strategies. Machine learning is no cure-all for the challenges mobile enterprises face, but you can expect it to become an integral part of EMM.