What are the Big Guys Using?
Summary: The largest companies utilizing the most data science resources are moving rapidly toward more integrated advanced analytic platforms. The features they are demanding are evolving to promote speed, simplicity, quality, and manageability. This has some interesting implications for open source R and Python widely taught in schools but significantly less necessary with these more sophisticated platforms.
We continue to be dazzled, and perhaps rightly so, by the advances in deep learning and question answering machines like Watson. And while these are fun to read about and some of the apps that incorporate them can be both handy and addictive, they cause us to take our eye off the bigger ball. The bigger ball in this case is what are the largest non-tech companies using for data science? What are their processes? What are their best practices?
This foundational practice of data science may seem pretty common place, filled with scoring models, numerical forecasts, geospatial analytics, and clustering and association analysis of products, customers and click streams. The reality is though that these largest data-science-using companies (Gartner says you need to have 10 to 50 or more data scientists of varying skills