The Data Science Puzzle, Revisited

Last year I wrote an overview post which defines a number of key concepts related to data science -- including data science itself -- and attempts to explain how these pieces fit together into a so-called "data science puzzle." As a new year begins, and a previous year worth of advances, insights, and accomplishments get rolled into our collective professional outlook, I thought it would be prudent to revisit this puzzle, noting and incorporating any changes and updates which may contribute to rearranging the puzzle for the foreseeable future, and to provide some addition commentary where warranted. Big Data is still important to data science. Take your pick of metaphors, but any way you look at it, Big Data is the raw material that has continues to fuel the data science revolution. As relates to Big Data, I believe that justification of data-acquisition and -retention from a business point of view, expectations that Big Data projects start providing actual financial returns, and the challenges related to data privacy and security will become the big Big Data stories not only of 2017 but moving forward in general.