Book Image

Analytics for the Internet of Things (IoT)

By : Andrew Minteer
5 (1)
Book Image

Analytics for the Internet of Things (IoT)

5 (1)
By: Andrew Minteer

Overview of this book

We start with the perplexing task of extracting value from huge amounts of barely intelligible data. The data takes a convoluted route just to be on the servers for analysis, but insights can emerge through visualization and statistical modeling techniques. You will learn to extract value from IoT big data using multiple analytic techniques. Next we review how IoT devices generate data and how the information travels over networks. You’ll get to know strategies to collect and store the data to optimize the potential for analytics, and strategies to handle data quality concerns. Cloud resources are a great match for IoT analytics, so Amazon Web Services, Microsoft Azure, and PTC ThingWorx are reviewed in detail next. Geospatial analytics is then introduced as a way to leverage location information. Combining IoT data with environmental data is also discussed as a way to enhance predictive capability. We’ll also review the economics of IoT analytics and you’ll discover ways to optimize business value. By the end of the book, you’ll know how to handle scale for both data storage and analytics, how Apache Spark can be leveraged to handle scalability, and how R and Python can be used for analytic modeling.
Table of Contents (20 chapters)
Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

Business value concerns


Many companies are struggling to find value with IoT data. The costs to store, process, and analyze IoT data can grow quickly. With future financial returns uncertain, some companies are questioning if it is worth the investment.

According to McKinsey & Company, a consulting agency, most IoT data is not used. From their research, less than 1% of data generated by an oil platform was used for decision-making purposes.

Finding value with IoT analytics is often like finding a diamond in a mountain of rubble. We can accept that 1% of the data has value, but which 1% is it? This can vary depending on the question. One man's worthless granite is another man's priceless diamond.

The business value challenge is how to keep costs low while increasing the ability to create superior financial returns. Analytics is a great way to get there.