Preface

How do you make sense of the huge amount of data generated by IoT devices? And after that, how do you find ways to make money from it? None of this will happen on its own, but it is absolutely possible to do it. This book shows how to start with a pool of messy, hard-to-understand data and turn it into a fertile analytics powerhouse.

We start with the perplexing undertaking of what to do with the data. IoT data flows through a convoluted route before it even becomes available for analysis. The resulting data is often messy, missing, and mysterious. However, insights can and do emerge through visualization and statistical modeling techniques. Throughout the book, 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. We cover the major IoT communication protocols. Cloud resources are a great match for IoT analytics due to the ease of changing capacity and the availability of dozens of cloud services that you can pull into your analytics processing. Amazon Web Services, Microsoft Azure, and PTC ThingWorx are reviewed in detail. You will learn how to create a secure cloud environment where you can store data, leverage big data tools, and apply data science techniques.

You will also get to know strategies to collect and store data in a way that optimizes its potential. The book also covers strategies to handle data quality concerns. The book shows how to use Tableau to quickly visualize and learn about IoT data.

Combining IoT data with external datasets such as demographic, economic, and locational sources rockets your ability to find value in the data. We cover several useful sources for this data and how each can be used to enhance your IoT analytics capability.

Just as important as finding value in the data is communicating the analytics effectively to others. You will learn how to create effective dashboards and visuals using Tableau. This book also covers ways to quickly implement alerts in order to get day-to-day operational value.

Geospatial analytics is introduced as a way to leverage location information. Examples of geospatial processing using Python code are covered. Combining IoT data with environmental data enhances predictive capability.

We cover key concepts in data science and how they apply to IoT analytics. You will learn how to implement some examples using the R statistical programming language. We will also review the economics of IoT analytics and discover ways to optimize business value.

By the end of the book, you will 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.