Aldo Dagnino - Data Analytics in the Era of the Industrial Internet of Things

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This book has several objectives aimed at increasing the understanding of systems developed to support the Industrial Internet of Things (IIoT). It is important to help the reader understand the IIoT phenomenon as it is an industrial approach that is and will be disruptive in organizations such as manufacturing, service, process industries , and retail, among others. The underlying principles of an IIoT organization are presented and also a discussion on how new technologies such as sensors , wireless , computing, data storage, artificial intelligence and machine learning , high-performance computing, and leading-edge visualization enable the IIoT. The book also provides the reader with a description of a typical IIoT architecture that industries implement to efficiently operate. An important objective is also to show how the IIoT brings about a deep change in business models of providers and consumers of products and services and present examples on the transition between current business models and IIoT business models . One of the important elements of the book is to provide the reader with basic understanding of the power of data analytics, which is the primary engine of IIoT systems. A simple-to-understand discussion on different machine learning models is presented so that business readers, without computer science or engineering background, can follow how these machine learning models operate on data. The readers are also exposed to real-world industrial cases where advanced analytics and machine learning have been used to achieve a business goal, address a business pain point, or crystalize a business opportunity. An important element that is underlined in this book is that technology is not in all cases a vehicle to replace human workers or domain experts . I have been fortunate to work with organizations that are very progressive in terms of technology utilization but also are conscious that these new technologies should be implemented with the idea not only to improve the business but also to empower subject matter experts and workers and make their work more productive, less boring, and more exciting, as well as facilitate and enrich their decision-making capabilities.

The targeted audience of this book includes managers, business professionals, engineers, and students that wish to learn how analytics is used to address business opportunities in the IIoT and bring organizations closer to a new and more productive business model . It is my hope that this book will spark interest in the audience to further understand how data can be used as powerful raw material to extract knowledge jewels within the IIoT framework to improve business outcomes, reduce waste of raw materials and resources, increase level of workers knowledge , increase employee satisfaction, enhance creativity, and help employees to design solutions that are creative and use new technologies.

The Industrial Internet of Things , from my perspective, is a phenomenon based on an interdisciplinary point of view of the modern world. The IIoT is the result of several elements meeting each other in a contemporaneous way. First, recent advances of sensor technologies have been powered by high-speed and low-cost electronic circuits, innovative signal processing methods, and novel advances in manufacturing technologies. Ground-breaking sensor structures have been designed that allow self-monitoring and self-calibration of sensors . The rapid progress of sensor manufacturing technologies has allowed the production of systems and components with a low cost-to-performance ratio. Moreover, miniaturization fosters the deployment of sensors in many areas of industrial systems. Second, the development of high-performance computing allows for deployment of complex machine learning models that can analyze high volumes of sensor data very fast. Third, the phenomenal increase of data storage capabilities allows industries to save an almost infinite amount of structured and unstructured data in data warehouses . Fourth, the advent of powerful visualization tools provides the ability to deploy innovative ways for users to interact with computer systems. Fifth, the advent of cloud computing provides a capability to perform calculations at the cloud but also at the edge or equipment level. Sixth, the networking capabilities available allow high-performance connectivity among IIoT components.

The structure of the book presents a systematic discussion of the topic in its various chapters. After the Introduction chapter, Chapter provides examples of new business models generated by the IIoT, and in my opinion they will thrive in the future.

Chapter provides a description of machine learning and some important modelling specially tailored for managers and non-experts in the field. This chapter offers a classification of machine learning models. A description of each machine learning model used in the examples of the book is presented in a non-expert fashion so businesspeople can understand how the models operate on the data they analyze. This part of the book is a fascinating trip into better understanding the magic that AI/ML can deliver when analyzing data, but it also shows that such magic can be very fragile as the quality of data, the assumptions required, and the interpretation of results dramatically influence the final results and how they are used in the business world.