Machine Learning - What Is It, and Why Use It?

Ever pondered just how Google determines which ads show up on the first page of your search results, or what method Amazon uses to work out which ads are most relevant to show you? Have you wondered exactly what makes self-driving cars work?

The answer is perhaps more straightforward than you think: machine learning.

Machine learning drives a surprising number of the products we encounter every day. It’s a way of taking enormous amounts of data and producing models that encode the relationships within it. All the big players in the technology world, and many smaller businesses, are already harnessing it to power their offerings.

But what is so great about machine learning, and what are the benefits of embracing it? Let’s take a closer look.

What exactly is machine learning?

In a nutshell, machine learning uses complex algorithms that analyse data using computers and software. But what makes it different from other technologies is that the algorithms iteratively learn from the data. Computers can then find hidden insights without being explicitly programmed to do so, and use existing data to forecast future behaviours, outcomes and trends.

Impressive, right?

Machine learning is transforming the way organisations use unstructured data and build data-driven business models. Businesses can use it to determine supply and demand,  predict customer behaviour, measure brand exposure, detect fraud and automate finance systems, to name just a few.

Machine learning in the real world

So what does machine learning look like in practice? Here are a few examples…

Machine learning is one of the leading weapons in a bank’s anti-fraud arsenal. Trawling through millions of records, machine learning tools interpret data on the fly and learn to identify fraudulent transactions as they happen.

Outdoor clothing brand The Clymb has increased revenue by 175 percent per thousand emails sent by applying machine learning to customer communications.

On a smaller scale, machine learning can be used for the simple purpose of making it easier and faster to perform tasks relevant to your job. For example, Microsoft recently announced plans to roll out machine learning to automatically highlight patterns it detects in Excel, making it easier to explore and analyse data. And from next year, Word users will have access to Acronyms, a machine learning-driven tool that helps team members to understand the acronyms most commonly used in their organisations.

Why use machine learning

Breakthroughs in machine learning, big data and predictive analytics are revolutionising how organisations forecast trends, identify new markets and drive revenue.

With Gartner predicting that machines will author 20 percent of all business content by 2018 and that half of the world’s fastest growing companies will eventually have fewer employees than smart machines, the time for businesses to act on machine learning is now. While machine learning is not new, the ability to apply complex calculations to big data is a recent development.

Every organisation can benefit from using machine learning to:

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• Quickly produce analytical models
• Analyse large, complex data
• Make accurate predictions
• Drive better business outcomes

When it comes to deploying predictive analytics solutions, Antares recommends Microsoft’s Azure Machine Learning Studio. This platform provides all the essential tools for harnessing data in a trusted and secure cloud environment. It provides insights and data-driven decision making that is informed by the full picture.

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Machine learning is not just a tool for shifting to smarter analytics. When it comes to maintaining a competitive advantage, machine learning is essential. With an ability to move fast, Azure Machine Learning Studio can work collaboratively with your organisation to adapt to new challenges and embrace the possibilities of machine learning.

To find out more about how machine learning can benefit your organisation, and for tips on how to get started, download our white paper: The shift to smarter analytics with machine learning.