Book Image

Smarter Decisions - The Intersection of Internet of Things and Decision Science

By : Jojo Moolayil
Book Image

Smarter Decisions - The Intersection of Internet of Things and Decision Science

By: Jojo Moolayil

Overview of this book

With an increasing number of devices getting connected to the Internet, massive amounts of data are being generated that can be used for analysis. This book helps you to understand Internet of Things in depth and decision science, and solve business use cases. With IoT, the frequency and impact of the problem is huge. Addressing a problem with such a huge impact requires a very structured approach. The entire journey of addressing the problem by defining it, designing the solution, and executing it using decision science is articulated in this book through engaging and easy-to-understand business use cases. You will get a detailed understanding of IoT, decision science, and the art of solving a business problem in IoT through decision science. By the end of this book, you’ll have an understanding of the complex aspects of decision making in IoT and will be able to take that knowledge with you onto whatever project calls for it
Table of Contents (15 chapters)
Smarter Decisions – The Intersection of Internet of Things and Decision Science
Credits
About the Author
About the Reviewer
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Preface

Exploratory data analysis


This part of the problem solving stack is also called "Confirmatory data analysis". Generally, the problems that we touch base over the Internet and other learning resources explain a stack called "ECR" that can be extended as Exploratory Data Analysis + Confirmatory Data Analysis + Root Cause Analysis. This is the same approach that we have considered-Exploratory Data Analysis (EDA)-where we understand "What" happened, then CDA, that is, Confirmatory Data Analysis, where we cement the results from our exercises using statistical tests. Finally, we will answer the "Why" question using Root Cause Analysis. In our current approach, we have the same approach but a slightly different naming convention. We have broken down the steps into more granular ones:

We have now reached the EDA phase, that is, we will now validate the insights and patterns that we observed in the data. Let's start with understanding how we are going to approach this. If we look back at the journey...