One of the most popular tools in data science today is the open-source programming language R. Simply put, R is the language of data. Over the last 20 years, statisticians all over the world have contributed their innovations to open source R. These contributions mean that developers have access to a large library of cutting-edge scientific algorithms that make it possible to rapidly build intelligent analytics applications.

Already we’re seeing the capabilities of R bear fruit across companies both new and traditional: Norway’s eSmart Systems has been deploying R-based forecasting models in the cloud to help optimize the country’s power grid using data from smart meters. American Century Investments is using R as the basis for its quantitative investment platform. The National Weather Service uses R in its River Forecast Centers to help predict flooding. Real-estate analysis company Trulia uses R to help predict home prices. R is part of Twitter’s Data Science Toolbox, used for monitoring the site’s user experience. The list goes on.

But despite this widespread use, we’re really just beginning to understand the power of today’s advanced statistical platforms. Over the next five to 10 years, we’re going to see machine learning and analytics drive intelligence in just about every software application, Internet device, and mobile phone. With so many challenges to solve, the industry must ensure it is putting the right tools into the hands of those looking for answers in these vast treasure troves of data.