Clinton’s Big Data Analytics Didn’t Work


It is fairly widely known that Hillary Clinton employed a team of around 60 data scientists to guide her campaign to be President. Clearly it wasn’t a game winner, since life is about more than numbers and clicks. Donald Trump on the other hand didn’t bother with data science until the end game, but will step into the White House next year. He played up to the discontent within the USA in a masterly manner, and no amount of data science can replace that level of insight and manipulation.

It would be unfair to say the data science didn’t work. Within the context under which it was operating, it may well have improved things, but there is a lesson to be learned here. Turning our focus to business, it is well known that the two most important factors affecting business success are customer preference for your products and services, and participation in growing markets. If you have a good product, and people know it is a good product, it will sell. Steve Jobs was the embodiment of the superior product mindset. As for growing markets, well this is where insight and instinct come into their own. Some people see the big picture, and others get lost in details. Someone who sees where the economy, fashions, trends, preferences and so on are headed, will probably have the sense to know which markets are best targeted. No amount of big data analytics will make everything better if the product/services are inferior and your business is participating in waning markets.

Various researchers are finding that human gut instinct can be a formidable weapon. Gerd Gigerenzer, the author of Risk Savvy, and Director at the Max Planck Institute provides plenty of evidence that our gut instincts often outperform rigorous analysis – not least because the rigorous analysis is sometimes just plain wrong. So don’t be too keen to throw the baby out with the bathwater, and at least consider your gut instincts, giving evidence based decisioning a little less credibility than the pompous name might suggest.