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What is Machine Learning And How is it Changing the Enterprise?

In Thought Leadership

Current forecasts for cognitive and artificial intelligence (AI) systems are likely to peak somewhere near $60bn within four years, so we take a closer look at what’s driving this investment and how it's helping business.

Machine learning is one of those terms we are seeing used more and more frequently by businesses today. As those within the enterprise seek further answers to many of the questions data poses, such new technologies are enabling them to not only find these, but also to reduce risk in its many forms by shaping future decisions and optimising business processes.

For CIOs and IT directors on the frontline, Machine learning (ML) is the key to this as it allows computers to put existing data to better use by forecasting future behaviours, outcomes and trends. The clever part is that by using machine learning modern day computers learn without having to be explicitly programmed. So in its simplest form, machine learning can be thought of as the automated use of analytics.

As such, the market for machine learning is growing at a rapid pace. Indeed, International Data Corporation (IDC) forecasts that spending on AI and ML will grow from $12B in 2017 to $57.6B by 2021.

That growth can be closely linked to the burgeoning cloud space that offers so many possibilities to enterprises today. As cloud allowed businesses to change the way they operate in terms of business infrastructure and moving towards an on-demand scenario, machine learning in conjunction with cloud computing, now offers CIOs and IT directors the power to effectively utilise things like the Azure cloud to run machine learning algorithms that optimise their business processes at almost any scale.

When you think about the use cases for machine learning it applies to almost any business scenario and will enable IT leaders and CIOs to make automated decisions in real-time, offering significant new opportunities and ways to drive businesses forward. Of course, the more historical data enterprises can gather on their operations, then the greater their ability to make better algorithmic decisions going forward. This could be data from within the business and its assets or that of your customers – one of the very reasons that the Internet of Things (IoT) provides an ideal scenario where it can be used.

Consider for instance how the two could work together in the field to enable predictive maintenance for firms. By using machine learning businesses are able to use generated data in various algorithms to spot when things are about to go wrong. How bigger impact could the use of predictive maintenance have upon your organisation? What if you were able to spot probable causes of failure in advance or prevent the outage of key networked devices? When you start to factor in how much further you can go in terms of optimising IT assets, you begin to see the kind of bottom line savings that can be brought about by a reduction in time spent around maintenance.

Taking that one step further, think about how machine learning on Azure could then be applied to give you an accurate update on the data you hold and are using. In real-time you would be able to spot problems in the data as and when they occur, enabling the rapid reporting of anomalies.

Applying this to vertical markets it’s clear to see the problems this kind of technology solves in banking, financial services, healthcare and life sciences. These industries are creating huge amounts of data twenty-four hours a day and machine learning and predictive analytics enables such businesses to extract new levels of data that gives them critical business analysis. As we move forward this will be crucial in terms of driving profitability for those with fine margins and other industries such as retail and fast-moving consumer goods (FMCG).

A common use already in play today sees machine learning shaping the future of travel via vehicle telemetry and this is now being more widely adopted by those in the transportation and logistics industries. It’s also helping data scientists automate their process when it comes to things like data analysis, feature engineering and predictive modelling. That is making them more productive and helping to dramatically reduce time spent on projects. This is just one reason that within the the Business Intelligence (BI) & analytics market, data science platforms that support machine learning are predicted to grow at a 13% CAGR through 2021, with their value increasing from $3B in 2017 to $4.8B in 2021, according to Gartner.

Findings from a MIT Technology Review study echo that shift, suggesting that around 60% of firms are already at varying stages of machine learning adoption, with nearly half saying the technology has led to more extensive data analysis & insights. Interestingly 35% also revealed that the technology is already enhancing their R&D capabilities when it comes to next-generation products.

Furthermore, a significant number of CXOs around the globe are indicating that machine learning and AI will be the key initiatives and areas for investment over the coming 12 months. That journey requires careful thinking and the implementation of a solid data management strategy. Put that in place, quantifying your expected results and machine learning goals and you will begin to harness the benefits it can bring.