Book Image

Introduction to R for Business Intelligence

By : Jay Gendron
Book Image

Introduction to R for Business Intelligence

By: Jay Gendron

Overview of this book

Explore the world of Business Intelligence through the eyes of an analyst working in a successful and growing company. Learn R through use cases supporting different functions within that company. This book provides data-driven and analytically focused approaches to help you answer questions in operations, marketing, and finance. In Part 1, you will learn about extracting data from different sources, cleaning that data, and exploring its structure. In Part 2, you will explore predictive models and cluster analysis for Business Intelligence and analyze financial times series. Finally, in Part 3, you will learn to communicate results with sharp visualizations and interactive, web-based dashboards. After completing the use cases, you will be able to work with business data in the R programming environment and realize how data science helps make informed decisions and develops business strategy. Along the way, you will find helpful tips about R and Business Intelligence.
Table of Contents (19 chapters)
Introduction to R for Business Intelligence
About the Author
About the Reviewers
R Packages Used in the Book
R Code for Supporting Market Segment Business Case Calculations

Chapter 1. Extract, Transform, and Load

Business may focus on profits and sales, but business intelligence (BI) focuses on data. Activities reliant on data require the business analyst to acquire it from diverse sources. The term Extract, Transform, and Load, commonly referred to as ETL, is a deliberate process to get, manipulate, and store data to meet business or analytic needs. ETL is the starting point for many business analytic projects. Poorly executed ETL may affect a business in the form of added cost and lost time to make decisions. This chapter covers the following four key topics:

  • Understanding big data in BI analytics

  • Extracting data from sources

  • Transforming data to fit analytic needs

  • Loading data into business systems for analysis

This chapter presents each ETL step within the context of the R computational environment. Each step is broken down into finer levels of detail and includes a variety of situations that business analysts encounter when executing BI in a big data business world.