Book Image

Practical Business Intelligence

By : Ahmed Sherif
Book Image

Practical Business Intelligence

By: Ahmed Sherif

Overview of this book

Business Intelligence (BI) is at the crux of revolutionizing enterprise. Everyone wants to minimize losses and maximize profits. Thanks to Big Data and improved methodologies to analyze data, Data Analysts and Data Scientists are increasingly using data to make informed decisions. Just knowing how to analyze data is not enough, you need to start thinking how to use data as a business asset and then perform the right analysis to build an insightful BI solution. Efficient BI strives to achieve the automation of data for ease of reporting and analysis. Through this book, you will develop the ability to think along the right lines and use more than one tool to perform analysis depending on the needs of your business. We start off by preparing you for data analytics. We then move on to teach you a range of techniques to fetch important information from various databases, which can be used to optimize your business. The book aims to provide a full end-to-end solution for an environment setup that can help you make informed business decisions and deliver efficient and automated BI solutions to any company. It is a complete guide for implementing Business intelligence with the help of the most powerful tools like D3.js, R, Tableau, Qlikview and Python that are available on the market.
Table of Contents (16 chapters)
Practical Business Intelligence
About the Author
About the Reviewer
Customer Feedback

Exporting R to Microsoft Power BI

R is a great standalone tool used to deliver reports to users, but it is also one of the few languages that are incorporated into several other BI tools, such as Microsoft Power BI. When we last explored Power BI in Chapter 3, Analysis with Excel and Creating Interactive Maps and Charts with Power BI, we focused on visualizing data pulled in from a Microsoft SQL Server query. You may then ask why it would be necessary to pull in data through R when it can be done directly through Power BI. As we saw earlier when we were forecasting data, R has the ability to generate data points based on different libraries applied to the original dataset. This generated data can then be easily merged back into the original dataset. It is this merged dataset that brings added value to a visualization inside of Power BI.

Merging new columns to dataframes in R

The original dataframe used in this chapter was called SQL_Query_1 and contained Discount Codes by Week. We could choose...