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Analytics for the Internet of Things (IoT)

You're reading from   Analytics for the Internet of Things (IoT) Intelligent analytics for your intelligent devices

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Product type Paperback
Published in Jul 2017
Publisher Packt
ISBN-13 9781787120730
Length 378 pages
Edition 1st Edition
Languages
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Author (1):
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Andrew Minteer Andrew Minteer
Author Profile Icon Andrew Minteer
Andrew Minteer
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Table of Contents (14) Chapters Close

Preface 1. Defining IoT Analytics and Challenges 2. IoT Devices and Networking Protocols FREE CHAPTER 3. IoT Analytics for the Cloud 4. Creating an AWS Cloud Analytics Environment 5. Collecting All That Data - Strategies and Techniques 6. Getting to Know Your Data - Exploring IoT Data 7. Decorating Your Data - Adding External Datasets to Innovate 8. Communicating with Others - Visualization and Dashboarding 9. Applying Geospatial Analytics to IoT Data 10. Data Science for IoT Analytics 11. Strategies to Organize Data for Analytics 12. The Economics of IoT Analytics 13. Bringing It All Together

Adding internal datasets


IoT data by itself is only part of the story. There is a multitude of useful data already available to you that could be a store of hidden value. Internal datasets can be overlooked as a way to quickly enhance IoT data. They are an excellent place to start. IoT data should not be viewed in isolation; think of it as a continuation of data already stored about your company's products, customers, and processes.

Data should be combined into a 360-degree view of your business to maximize the opportunity of finding new value in it. The fastest and easiest place to start is usually (but not always) the internal datasets that you already have available.

The reason it may not always be the fastest and easiest comes from internal data security and legacy system hurdles; which can sometimes make it difficult to extract internal data to combine with IoT data. This may be a stumbling block for internal datasets, which, perhaps counter-intuitively, is not the case with external...

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