To put it very simply, machine learning can be described as a way of algorithms using vast amount of data to find patterns. What makes it special? It can learn and self-improve. It sounds straight out of a Sci-Fi movie, but it’s true. It runs the world and most of us use it every day without even knowing. It powers services such as Google search, Siri, health diagnostics, oil drilling, Uber, online shopping, or cyber security – just to name a few.
You can’t talk about machine learning without mentioning data
Headlines such as “data is the new oil” or “data is the future” are increasing in popularity. Most of us agree with the underlying message which can be summarized into the lines of “data is power”, but we rarely ask ourselves the question: “Why is this only recently gaining traction? We had access to vast amount of data for decades.” The answer to this question is worth reading twice: Over 90% of data available today was generated in the past 2 years. The statistics are truly mind-boggling and hence the reason why AI or machine learning are becoming more popular and powerful. The more data you feed into an algorithm, the better it becomes at recognizing and predicting patterns.
There must be a catch…
Given its ability to quickly screen through vast volumes of data, machine learning systems can enable businesses to detect unusual behaviours, predict events and trends and fix issues before and outage occurs. Poor data is one of the biggest nemesis of machine learning. However, there are also many challenges on the human side as machine learning systems require highly skilled data scientists. Due to the high demand for these professionals, hiring and retaining talent can be a real challenge for organisations using machine learning in their operations.
Future business implications of machine learning
Even with the technology in its early stage, businesses understand the imperative of embracing machine learning and those who enhance the value of their big data are the ones who will be able to use it to their competitive advantage.
According to a recent research by McKinsey, machine learning accounts for 60% of all investments made in AI focused businesses, and the same proportion of respondents who took part in the 2017 Harvard Business Review Analytic Services Survey agreed that the future of their organisation depended on the successful implementation of machine learning. The message is clear. Machine learning is here to stay and will continue adding value, and even disrupting industries.
It’s worth mentioning Google CFO Ruth Porat’s recent description of data, which encapsulates where machine learning and data are heading. Just two weeks ago, she coined the phrase “data is more like sunlight than oil”. The twist over “data is the new oil” hits the nail on how data is not finite, growing at staggering rates, and – most importantly – represents the future.