‘Working’ Analytics: a useful term that distinguishes building deployable models that solve problems with a minimal amount of cost and complexity. Almost by definition, ‘Big’ is not ‘working’ analytics; it’s something else. When things get big, they get costly and complex. They get impractical to operationalize much less gain useage in day-to-day operations. A foundation principle for data-science that pre-dates ‘BIG’ is parsimony, also known as Occam’s razor.
For data scientists, ask yourself whether you want to be a ‘working’ practitioner or a developer of complex, inexplicable and mostly unused solutions. You can certainly make complex solutions but your job is to make them simple.
For employers, it is temping to believe in ‘unicorns’….a wickedly complex algorithm that creates a discontinuous shift in your industry and crushes the competition for years to come. But think about hiring people with the attitude and habit of contrarian thinking (e.g. putting a camera on a phone). Hire a blend of ‘working’ practitioners with a philosophy of parsimony, and ‘explorers’ who will thrash data and models regardless of where it takes them.
There are many, many working problems to solve while you are looking for your unicorn.
On this subject, a useful (and challenging) concept from Oliver Wendell Holmes:
“I would not give a fig for the simplicity this side of complexity, but I would give my life for the simplicity on the other side of complexity.
Edward H. Vandenberg