AI-based data analytics enable business insight

That was the directive SK Sharma was given in 2016, when he was hired as chief analytics and AI officer by Ingrooves Music Group, which provides global music distribution and marketing to indie artists. “The idea is,” he says, “in a very crowded music marketplace, how can we make certain kinds of content, particularly with respect to indie artists, stand out.” Making use of engagement data is “the crux of our business,” says Sharma, “and that’s what necessitates using predictive analytics and really being able to judiciously use the information that we have.”

For Sharma, that meant starting from scratch, assembling a team of data scientists and building an AI pipeline. Sharma and his team created a “smart audiences platform” that places ads promoting an artist’s latest release in front the listeners most likely to engage with it. AI and data analytics are not just applicable to the music industry. AI-based data analytics can make a huge difference in any industry, and in a variety of situations.

Why companies need advanced data analytics

Most organizations today are drowning in data. They keep it for regulatory and compliance purposes, and they also store additional data in the hope that it will be useful one day.

That day has come. Or, as Jason Hardy, global CEO at Hitachi Vantara puts it, companies are experiencing an “aha moment” – realizing that AI-based data analysis can provide real business value and give them a competitive edge. He adds, “Traditionally, companies were saying, ‘Just archive it and we’ll figure out what to do with it later.’ That’s turned into a ‘No, this actually impacts us now; we need to be able to read that data in real time and process and infer against it.'”

This has become true across industries. Better analytics can help improve manufacturing yield, reduce waste, increase efficiency, and lower costs. AI can measure customer satisfaction and detect emotional reactions to product placements in consumer-focused businesses. AI can detect and mitigate supply chain faults before they happen in industries that rely on it.

Hardy says, “We’re hearing customers who say, “I’ve got the AI bandwagon to get on board.” This is what I need to do. I need a platform that can help me do this, whether it’s on-prem or in the cloud.

Unfortunately, most organizations don’t know where to begin. Hardy claims that C-level executives tell Hardy that they want to use AI/machine learning. We want to make use of our data. We want to make it more valuable. We don’t know how to do it. We don’t even know the question we’re trying to answer.”

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This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by the editorial staff of MIT Technology Review.

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