The failure of "a company not known for epidemiological research" actually says a good deal about Big Data as it's been presented to many businesses. The "magic Christmas-land" view of Big Data boils down to nothing less than instant expertise as a service. You don't have to know what you're doing. You don't have to understand the field. You don't have to invest money in people who know what's going on, or time in developing expertise. Instead, you just throw enough data at the problem to tease out a correlation. You don't need to understand a model. When trends change, there's no model to break. The body of data just changes underneath you and carries your correlations along with it.
This wasn't an experiment in epidemiology. It was an experiment in machine prediction, using something that people care enough about to give them a lot of data, and using something that is fairly easy to track after-the-fact to tease out what worked and what didn't. Bringing in people who understood epidemiology would just undermine the entire point.
Obviously, the real world is more complicated than that. All technology is more complicated than the idea men will tell you. But how far you get before you start to really hit those complications gives you an idea of the potential. Google failure is notable because it's Google. They are the biggest of the Big Data companies. They have access to more data than anyone else through their services. They use algorithms to make pretty much every decision (including who to hire). They know what they're doing. And it still didn't help that much. You can't blame the failure on lack of technical execution. You can only blame it on the Big Data concept itself.