Machine Learning: The Future is Now for Analysts
Seven steps to launching your predictive analytics rocketship.
We’re all searching for those “aha!” moments. Maybe it comes when you’re trying on that perfect outfit from an online personal shopping service, or driving this morning’s GPS-optimized route through rush-hour traffic. Something comes together, a bunch of dots get connected, and, like magic, it all works out! You find the perfect match or, as if by instinct, navigate around traffic on a route you’ve never driven before.
The common thread between those two different moments? You’re using machine learning. Like an invisible web, these human-written algorithms impact the world around you in both obvious and obscure ways. As a data analyst, you might want to add the magic of machine learning to your analytic arsenal. You, too, could be answering new, interesting, and future-facing questions.
Machine learning is the iterative process a computer follows when it is asked by a human to identify patterns in a dataset given specific constraints.
In fact, machine learning can deliver benefits to every department in your company since it allows you to recognize patterns in vast assortments of data to predict possible outcomes, allowing leaders to plan and take action. From pattern recognition comes predictive use cases, like customer response modeling, demand and inventory forecasting, and many more applications that drive business performance.
Machine learning is part of a new employment dynamic, creating jobs that center around analytical work augmented by artificial intelligence (AI). In the process, 2.3 million new jobs will be created by 2020, according to Gartner — a net gain, even as 1.8 million “old tech” jobs have been displaced.
A study released by the World Economic Forum shows that data-related jobs will be the most in demand within the next four to five years, along with AI and machine learning specialists. The job categories that will be the most in demand include data analysts and data scientists; AI and machine learning specialists; software and applications developers and analysts; and big data specialists, although it's likely these people will have other titles in the coming years.
Your company has a wealth of data that could be used for machine learning initiatives. But how do you get started? Let’s break it down.
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