Five Misconceptions about Data Science - Knowing What You Don't Know

Data science has made its way into practically all facets of society – from retail and marketing, to travel and hospitality, to finance and insurance, to sports and entertainment, to defense, homeland security, cyber, and beyond. It is clear that data science has successfully sold its claim of "actionable insights from data," and truth be told, it often delivers on that claim, adding value that would otherwise go untapped. As a result, data science is often looked to as a panacea, a Swiss army knife, a silver bullet, a must-have, [insert your own cliché here]. This has implications for both data scientists and the organizations they work with. On one hand, data scientists are now beginning to face a new set of challenging problems, problems that even the most advanced machine learning algorithms have yet to solve: managing expectations. And on the other hand, many businesses and organizations are grappling with shifting learning curves, the latest shiny object, and the pressure to keep pace. As the data science bandwagon fills up, there are many individuals that do not fully, or even marginally, understand what data science is, what it can do, and when it is relevant. In what follows, I present what I have encountered to be five of the most common misconceptions about data science – misconceptions that will proliferate and morph as the data science wave rolls on.