Maybe it stems from my childhood fascination with crystal balls and the Magic 8 Ball, but I have always been interested in predictions of the future. Machine learning has done a great job with predictions based on past data about events and behaviors, but it hasn't generally been applied to making sense of the broader world. But that is just what Signal AI is doing with machine learning. They produce "external intelligence" intended as an aid to decision-making. It could also be called "environmental sensing."

Over the past several years, technology has rapidly changed what enterprise analytics can do. Analytical approaches that incorporate predictive models have begun to displace merely descriptive approaches. Descriptive analytics, which continue to be valuable for many users, have evolved as well, making greater use of visual analytics and moving toward a self-service model in which nontechnical users can often develop their own analyses. In general, analytics are quickly becoming both easier to use and more powerful. Despite this progress, it's still difficult to use data and analytics to understand and predict many of the important phenomena in organizations. Predictive models require a substantial amount of past data and a reasonable amount of expertise to create and use, which limits how and when they can be deployed.

Artificial intelligence has huge potential to transform care, but the healthcare system needs to crawl before it can walk, according to a panel of speakers from IBM Watson, the Mayo Clinic, and the American Medical Association at an after hours dinner at HIMSS18 Monday. "I think this AI stuff is absolutely real, but at the same time we haven't finished the first job, which is creating systems that are usable by clinicians," Mayo Clinic Chief Information Officer Cris Ross said. The panel talked a lot about EHRs which are still the number one pain point for physicians, who are spending twice as much time entering data as seeing paper. That documentation, for the benefit of the payer, reflects a set of priorities that need to be changed according to AMA President James Madara, MD. "We have to flip this model, starting at the point where we think the truth of the healthcare system is," he said. "Because what we're doing today would be the equivalent of General Motors making cars and paying attention to the dealers but not caring at all about the drivers or the mechanics. No other field would do that and yet that's where we've ended up."

Many times when I speak with analytics managers or businesspeople interested in analytics, they tell me that performing some analytics on data is not the primary problem they have. "We have to get the analytics integrated with the process and the systems that support it," they say. This issue, sometimes called "operational analytics," is the most important factor in delivering business value from analytics. It's also critical to delivering value from cognitive technologies – which, in my view, are just an extension of analytics anyway. A quick aside: Someone who anticipated this issue early on was Bill Franks, the Chief Analytics Officer at Teradata. He published a book a couple of years ago called The Analytics Revolution, which is really about operational analytics.