Where The Data Beasts Lie

Since the mid-nineties, the story about IT has been that the “New Information Economy” would give way to vast gains in productivity and creativity. We’ve been told that if we simply implement ERP, CRM, etc., our marketing efforts would be smarter and more efficient. But after 20+ years that is not exactly the case.

The benefits of massive IT implementation and big data are elusive. We have experienced increased efficiency, but far less than was promised us. We are certainly nowhere near the hyper-targeted, data-fueled industry we were told we would become. Take Amazon, for example. Amazon has had more than thirty years to learn our preferences and fine-tune its recommendation engine. Yet it still recommends products that we have just bought and, hence, no longer need and even suggests items to buy that are already in our shopping carts. We’ve been told that the more data a company amasses, the better it will be at targeting consumers. But is this really the case? Think about the old story of the disruptor, Netflix, and the fallen giant, Blockbuster – wasn’t that really one of supply and demand and a better distribution model? Or did we all really watch Netflix because of suggestions generated by its AI, big data, and machine learning? I’m guessing not. Amazon may be an excellent distribution company, but from any human consumer perspective, its recommendation algorithms are deeply flawed.

I can’t help but wonder if this is because big data analytics aren’t generating the insights needed to target consumers any better than what TV advertising and newspapers have always done. Billions of dollars have moved from offline to online advertising, but, in the offices of big advertisers, there is a sinking feeling that the data-fueled Facebook or Google advertising model isn’t going to help them as much as they had hoped.

Part of the marketing model of information technology is that the next big thing is always around the corner. These days, that means deep learning and AI. We’re being told that machine learning is going to revolutionize and disrupt everything as we know it and spur amazing insights that lead to brilliant creative work. You know, just the way big data was meant to just a few years ago. But before this hype-cycle takes off again, maybe it’s time to take a step back and ask ourselves if there’s a better way. What makes these technologies that much different from those of the past? Will they truly stimulate unprecedented insight into what motivates people? Or is this just another round of hype that will distract us from real matters of human importance?

Published by gavinjohnston67

Take an ex-chef who’s now a full-fledge anthropologist and set him free to conduct qualitative research, ethnography, brand positioning, strategy and sociolinguistics studies and you have Gavin. He is committed to understand design and business problems by looking at them through an anthropological lens. He believes deeply in turning research findings into actionable results that provide solid business strategies and design ideas. It's not an insight until you do something with it. With over 18 years of experience in strategy, research, and communications, he has done research worldwide for a diverse set of clients within retail, legal, banking, automotive, telecommunications, health care and consumer products industries.

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