Four Ways Machine Learning Will Improve Big Data

By Dan Newman, Futurum We already know that 90 percent of the world’s data has been created in the last few years alone. To deal with the massive quantities of IoT data, companies will need to turn to machine learning. It will simply be too much for humans to handle alone.

By Dan Newman, Futurum

Previously, I reported on Dell Technologies’ IoT strategy announcement,” which focused a lot on the “Deluge of Big Data” created by the Internet of Things (IoT) and how to manage it intelligently. Judging by their insights, I’d say we’re about to see a huge rise in the use of machine learning to manage the mass amounts of data we’re creating—and will continue to create—in coming years.

We already know that 90 percent of the world’s data has been created in the last few years alone. But the IoT is going to exponentially increase that data creation as tens of billions of devices activate and connect online. To deal with the massive quantities of IoT data, companies will need to turn to machine learning. It will simply be too much for humans to handle alone.

Several big companies already use machine learning and data to help make their customers lives better. Netflix, for example, uses it to recommend shows viewers might like based on other shows they’ve viewed online. Amazon does the same thing by recommending products shoppers may find valuable. In such a way, machine learning turns the piles of available data into useful information that can improve customer experience (CX), business efficiencies, and customer insights. But there is even more machine learning can do. The following are just a few ways today’s companies are already making the most of their data via machine learning.

Eliminating Data Junk

With so much information available—and much more to come—one of the most important jobs machine learning can do is figure out which data is useful, and which is not. As humans, it would be nearly impossible for us to do this quickly and accurately. Machine learning can keep data lakes clean and consistent—even when it comes to unstructured data that may have been difficult to sift and sort in the past.

Recognizing Patterns

Imagine if you were given 1,000 customer profiles, which included each customer’s buying history for the past five years. Your boss asks you to find the patterns in their purchases—not just by customer, but throughout the entire batch of customers. It sounds almost impossible, right? But machine learning can do it quickly and automatically, helping you better understand your customers and their decision-making. Why is that important? That kind of information can help you determine the ad, product, or incentive your customer is most likely to respond to, which in turn increases the chance of a sale overall.

Eliminating (Most) Bias

Think about it: every decision you make on a given day holds some type of bias. You choose what to eat, where to sit, what to watch, and who to speak to, all based on your personal preferences. Those biases don’t disappear in the business setting. In fact, they often cloud one’s ability to make sound business choices. We’ve all been in business meetings when someone made an emotional appeal about a certain project, or made a decision that simply made no logical sense. Machine learning helps take most of the bias out of decision-making by presenting information based on factual data trends.

This comes with a caveat: as I recently shared in Forbes, artificial intelligence is not itself bias free. It is created by humans, and thus carries the biases of those humans who created it. We’ve seen this with Facebook and Google actually creating bias by presenting users information they are most likely to agree with. Human judgment and logic are still important components of data processing.

Improving Decision-making

While it would take humans weeks—or years—to process all the data being gathered by their companies, machines can do it in real-time, while the data is still relevant and meaningful. In this way, companies will know almost instantly when an ad isn’t working and can make campaign changes in real-time.

Still, another caveat worth noting: the willingness to make data-backed decisions is a cultural issue. For data to be useful, you will need to have people within your business that are willing to use it. I’ve seen many companies—especially legacy companies—where this simply isn’t the case. Culture is huge in digital transformation. You may need to work to create a data-driven culture before implementing machine learning into your tech strategy.

Wherever you stand in digital transformation, I can almost guarantee machine learning is a necessary next-step in your journey. Companies that fail to adopt a smarter IoT by processing data automatically are likely to get swallowed up by the data monster that is surely to come. Data is not going anywhere. In fact, it’s growing. But one thing is for sure: it’s only as useful as you and your AI are able to make it.