Because of its rapid progress, it’s worth taking a step back to really understand what machine learning is and start thinking about how you might utilize it in your own business.

Many large corporations have already invested in machine learning and are betting on it to be the next big thing. According to Deloitte’s 2016 Global CIO Survey, 64 percent of 1,200 IT executives who were asked stated that machine learning was one of the new technologies they planned to invest in significantly in coming years.

With that in mind, let’s delve into machine learning to get a better understanding.

What is it exactly?

Machine learning is a part of computer science that can be described as a computers’ ability to learn and react to a task, without being specifically programmed for that task.

It analyzes data by the study of pattern recognition and the construction of algorithms to make predictions. It learns from huge amounts of input data and can progressively improve its performance as time passes and experience grows.

Machine learning has the potential to help businesses become much more efficient by automating more tasks that are traditionally carried out by humans.

What’s the driving force behind Machine Learning?

One of the biggest reasons for the rise in machine learning is because of the explosive growth of data that we’re experiencing. In a world of increasing technology where we’re ‘always on,’ the phrase information overload has never been truer.

Here’s a summary of some key factors contributing to the progress of machine learning:

• Exponential data growth: corporations need tools to help analyze and react to the vast about of data they’re processing, which include both structured and unstructured data, images, videos, and audio.
• Increased processing power: computers have had to get faster to keep up with all the data being produced. Data now races through huge networks in an instant, and coupled with the introduction of cloud computing; it’s got much cheaper to process large data volumes.
• Smarter algorithms: With the aim of simulating the human thought process, algorithms have been developing steadily and are getting smarter. As such, It’s being predicted that ML will be adopted much more going forward.
• Open source software: Open source software groups have enabled a steady growth in the development of ML by making software readily available.

Current trends for ML

Many industries that process large volumes of data have already witnessed firsthand the value of machine learning technology. Having the tools to gather insights and predictions from their data can provide a competitive edge in the marketplace.

Examples of areas where machine learning is already adding value for businesses include:

• Healthcare: wearable devices and sensors mean a patient’s health can be analyzed in real time, which can lead to a more rapid diagnosis.
• Banking: the financial industry uses machine learning to detect fraudulent activity and also identify investment opportunities.
• Insurance: vehicle damage can be recognized automatically by insurance companies to provide an accurate assessment.
• Oil and gas: by analyzing the ground for specific minerals based on previous experience, new energy sources can be found.
• Government: machine learning can mine data from government agencies to detect potential cost saving areas and increase efficiency.
• E-Commerce: websites capture buyers’ purchasing data and analyze their history to promote items and recommendations, making a sale more likely.

This is just the tip of the iceberg, and with machine learning developing constantly there appears to be an almost limitless number of possibilities.

Why should you care?

Machine learning won’t solve all your problems or increase your profit margin overnight, but it does have huge potential to help identify solutions to problems, and generate powerful product ideas to increase revenue.

The power of analyzing such large volumes of data can lead to recommendations for your business that could mean improved operations, cost savings, more efficiency, and a leaner business model overall. With data scientists in place to experiment with machine learning in your business and push the boundaries, the possibilities are endless.

There are of course some tasks that are better suited for regular programming, while others that would benefit more from machine learning. In addition, there will always be tasks that humans simply do better, and that no program or machine will ever be able to replicate. That being said, by not embracing machine learning now, you risk being left behind the competition in the future.

Is that a risk you can afford to take?