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


We have come to the conclusion of the first chapter and we have covered quite a bit of ground. We summarized the core material regarding the data modeling methodology with the Kimball method. We established our definition of business intelligence, which will be applied throughout the book. We also summarized the various tools that we will be using to implement business intelligence with. Our main emphasis will be placed on implementing business intelligence best practices within various tools that will be used based on the data available to us within the AdventureWorks database.

In the next chapter, we will cover extracting additional data from the Web, which will then be added to the AdventureWorks database. This process is known as web scraping and can be performed with great success using tools such as Python and R. In addition to collecting data, we will focus on transforming the collected data for optimal query performance.