IDG Contributor Network: Big data + AI: Context, trust, and other key secrets to success


When Target deduced that a teenager from Minnesota —and told her father about it before she’d broken the news herself—it was a reminder of just how powerful data analytics can be, and how companies must wield that power carefully.

Several years later, big data and machine learning are being used together more and more in business, providing a powerful engine for personalized marketing, fraud detection, cybersecurity and many other uses. A from Accenture found that 85 percent of executives plan to invest extensively in AI-related technologies over the next three years.

But machine learning doesn’t merely take existing problems and solve them more quickly. It’s an entirely new model that can address new types of problems, spur innovation, and uncover opportunities. To take advantage of it, businesses and users need to rethink some of their approaches to analytics and be aware of AI’s strengths and weaknesses.

Machine learning today, like AI in general, is both incredibly smart and incredibly dumb. It can look through vast amounts of data with great speed and accuracy, identifying patterns and connections that might have gone unnoticed before, but it does so without the broader context and awareness that people take for granted. Thus, it can divine that a girl is pregnant, but has no idea how to act on that information in an appropriate way.

even though it had never been trained to do so. That type of mystery is fine for an experiment like Google’s, but what about in business?

that failed to see how the power of data analytics could backfire. After Facebook failed to predict how its data could be used by a bad actor like Cambridge Analytica, the best excuse it could muster was that it didn’t see it coming. “,” Mark Zuckerberg said. For all the good it brings, machine learning is a powerful capability and companies must be aware of potential consequences of its use. This can include how analytics results are used by employees, as in Target’s case, and also how data might be used by a third party when it’s shared. Naivety is rarely a good look, especially in business.

The use of AI is expanding as companies seek new opportunities for growth and efficiency, but technologies like machine learning need to be used thoughtfully. Sometimes the technology is embedded deep within applications, and not every employee needs to know that AI is at work behind the scenes. But for some uses, results need to be assessed critically to ensure they make good business sense. Despite its intelligence, artificial intelligence is still just that—artificial—and it takes people to get maximize its use. Keeping the above recommendations in mind will help you do just that.

This article is published as part of the IDG Contributor Network.