Big Data, Small Target: The Smart Approach To Artificial Intelligence

Companies that have invested heavily in big data solutions want to know how to make smart, strategic investments that will distinguish them from the competition and enable the best possible return before making the decision to go all in. In the past, not all enterprise big data initiatives went as planned. These failures are not usually published, but the big data failure rate is unusually high. According to Gartner, only 15% of businesses make it past the pilot stage of these projects. Our fear, as leaders of technology companies, is that with so much attention surrounding AI, the pressure is on to apply the technology or risk falling behind the many decision makers who are adopting technologies without first establishing clear business goals and understanding the differences between AI and ML and how they should be applied. It's easy to get caught up in the allure of artificial intelligence as well as its hype, including breakthroughs like deep learning, but those looking to make an outsized impact should instead focus on its more practical counterpart: good old-fashioned machine learning -- or "cheap learning," as my colleague Ted Dunning and Ellen Friedman explain in their guide Practical Machine Learning: Innovations in Recommendation.