"Most organizations early on in the data-science learning curve spend most of their time assembling data and not analyzing it. Mature data science organizations realize that in order to be successful they must enable their members to access and use all available data—not some of the data, not a subset, not a sample, but all data. A lawyer wouldn’t go to court with only some of the evidence to support their case—they would go with all appropriate evidence. ...The fundamental building block of a successful and mature data science capability is the ability to ask the right types of questions of the data. This is rooted in the understanding of how the business runs... The mature data science organization has a collaborative culture in which the data science team works side by side with the business to solve critical problems using data. ... [it] includes one or more people with the skills of a data artist and a data storyteller. Stories and visualizations are where we make connections between facts. They enable the listener to understand better the context (What?), the why (So what?), and “what will work” in the future (Now what?)."

--Peter Guerra and Kirk Borne in Ten Signs of Data Science Maturity (2016)

Guerra and Borne (2016) highlight the importance of a diverse and inquisitive team approach to data science. Business intelligence also benefits from this approach. Introduction to R for Business Intelligence gives you a way to explore the world of business intelligence through the eyes of an analyst working in a successful and growing startup company. You will learn R through use cases supporting different business functions.

This book provides data-driven and analytically focused approaches to help you answer business questions in operations, marketing, and finance—a diverse perspective. You will also see how asking the right type of questions and developing the stories and visualizations helps you connect the dots between the data and the business.