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

Common mistakes when designing visuals


Charts and dashboards tend to be done as an afterthought to analytics. The interesting work (to the analyst) has already been finished at this point. There is a rush to put together some visuals so one can move on to the next challenge.

The rush to throw together a visual is a mistake in itself, as the first impression your audience, the people at the meeting or the users of your dashboard, will make on the quality of your analytics is determined by what they see first - your visuals. In this chapter, we will use the word audience to refer to both the end users of a dashboard and the viewers of an analytics presentation.

This makes it far more important to get it right than you may think. Analytics for the sake of analytics is pointless. Someone needs to actually use it, for it to have value. For someone to be willing to use it, they must understand it and be engaged by it.

It is easy to design bad visuals; we see examples of it all the time - especially...