Keywords: data science, business, industry, best practice, critical enterprise environments, R,
Over the last couple of years, R has become increasingly popular among business users. Today, it is the first choice of many data science departments when it comes to ad-hoc analysis and data visualization, research and prototyping.
But when it comes to critical production environments, IT departments are still reluctant to consider R as part of their software stack. And there are reasons for that: Dynamic typing, the reputation for being slow (still around!), the lack of experience regarding management and administration of R (and its 10,000 packages), to name some of them.
Nevertheless, with the help of some friends, it is feasible and reasonable to use R as a core application even in critical production environments. This talk will share lessons learned from practical experience and point out a best practice landscape of tools, approaches and methods to make that happen.