Is Machine Learning Inevitable for Data Analytics?

One of the most watched developments in data analytics and business technology these days is that of machine learning. Becoming something of a business-critical technology, machine learning makes computing processes more efficient, cost-effective, and reliable and may ultimately accelerate every aspect of business decision-making.

Machine learning has applications in most industries, where it presents a great opportunity to improve upon existing processes. Yet many organizations are slow on the uptake. Recent surveys report that fewer than 25% of businesses have adopted any significant level of machine learning automation; yet it is currently behind some of the most game-changing advancements at Google, PayPal, Netflix, and other industry giants.

And for good reason. Where data analytics is easily the jet plane of data processing, machine learning could be considered a rocket ship, providing a means to quickly and automatically produce models that analyze larger volumes of highly complex data and deliver results faster and with more accuracy.

Traditional data analysis has become invaluable to enterprises for its ability to mine their ever-growing data stores, produce reports and models of historical developments, trends, and deliver predictive tools. It helps at every level of the business to help quantify and track goals, cut costs, boost productivity, and improve customer experience. Doing so, it delivers more attuned decision-making that makes a business more profitable and more competitive.

But there will come a point when every department in a large organization—finance, marketing, IT, operations, development, or anything else—will be able to reap benefits from the accelerated processing that machine learning has to offer.

With traditional data analytics, data models are typically static and can be of limited value when it comes to working with fast-changing and unstructured data. But as more fluid and sophisticated applications become desirable, it becomes necessary to be able to identify relationships between larger numbers of inputs and external factors, all of which produce millions of data points. The exponential growth of relevant data at some point requires heftier measures to mine the treasure of insights that lie within it.

That’s where traditional data analytics leaves off and ML can begin. Whereas traditional data analysis requires models built on historical data and the inclusion of industry-expert judgment to define the relationships between the variables, machine learning takes a different approach. It “only” requires the goals and objectives as inputs along with the relevant data, and then automatically and autonomously looks for predictor variables and their interactions in order to produce the desired outcomes. By doing so, machine learning accelerates a business’s ability to predict future activity— including trends, behaviors, patterns—based on past behavior and activity. (Sound familiar?) By programming in the outcome you want, it will find out how to get you there.

This predictive capability can be tremendously valuable to any organization. For example, where markets are concerned, what you can predict, you can respond to. Where behavior is concerned, you can provide more convenience in anticipation of your customer’s desires. And where sales are concerned, you can plan to produce and sell now what you expect your market to value in the future.

Think of some of the most advanced computer feats in recent times and you’ve probably identified areas using ML. Facebook would not be Facebook without its learning algorithms that gather behavioral information and predict interests and sell ads on its news feed. From Siri and Alexa’s ability to parse human language and respond to it, to Amazon, Netflix and Spotify’s ability to suggest similar items you might like based on your purchase history, the world is experiencing more of the features of machine learning.