Smarter Decisions – The Intersection of Internet of Things and Decision Science E-Book
Jojo Moolayil
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- Herausgeber: Packt Publishing
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
Enter the world of Internet of Things with the power of data science with this highly practical, engaging book
About This Book
- Explore real-world use cases from the Internet of Things (IoT) domain using decision science with this easy-to-follow, practical book
- Learn to make smarter decisions on top of your IoT solutions so that your IoT is smart in a real sense
- This highly practical, example-rich guide fills the gap between your knowledge of data science and IoT
Who This Book Is For
If you have a basic programming experience with R and want to solve business use cases in IoT using decision science then this book is for you. Even if your're a non-technical manager anchoring IoT projects, you can skip the code and still benefit from the book.
What You Will Learn
- Explore decision science with respect to IoT
- Get to know the end to end analytics stack – Descriptive + Inquisitive + Predictive + Prescriptive
- Solve problems in IoT connected assets and connected operations
- Design and solve real-life IoT business use cases using cutting edge machine learning techniques
- Synthesize and assimilate results to form the perfect story for a business
- Master the art of problem solving when IoT meets decision science using a variety of statistical and machine learning techniques along with hands on tasks in R
In Detail
With an increasing number of devices getting connected to the Internet, massive amounts of data are being generated that can be used for analysis. This book helps you to understand Internet of Things in depth and decision science, and solve business use cases. With IoT, the frequency and impact of the problem is huge. Addressing a problem with such a huge impact requires a very structured approach.
The entire journey of addressing the problem by defining it, designing the solution, and executing it using decision science is articulated in this book through engaging and easy-to-understand business use cases. You will get a detailed understanding of IoT, decision science, and the art of solving a business problem in IoT through decision science.
By the end of this book, you'll have an understanding of the complex aspects of decision making in IoT and will be able to take that knowledge with you onto whatever project calls for it
Style and approach
This scenario-based tutorial approaches the topic systematically, allowing you to build upon what you learned in previous chapters.
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Veröffentlichungsjahr: 2016
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Table of Contents
Smarter Decisions – The Intersection of Internet of Things and Decision Science
Smarter Decisions – The Intersection of Internet of Things and Decision Science
Copyright © 2016 Packt Publishing
All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.
Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book.
Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.
First published: July 2016
Production reference: 1220716
Published by Packt Publishing Ltd.
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ISBN 978-1-78588-419-1
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Credits
Author
Jojo Moolayil
Copy Editor
Tasneem Fatehi
Reviewer
Anindita Basak
Project Coordinator
Shweta H Birwatkar
Commissioning Editor
Veena Pagare
Proofreader
Safis Editing
Acquisition Editor
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Indexer
Mariammal Chettiyar
Content Development Editor
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About the Author
Jojo Moolayil is a data scientist, living in Bengaluru—the silicon valley of India. With over 4 years of industrial experience in Decision Science and IoT, he has worked with industry leaders on high impact and critical projects across multiple verticals. He is currently associated with GE, the pioneer and leader in data science for Industrial IoT.
Jojo was born and raised in Pune, India and graduated from University of Pune with a major in information technology engineering. With a vision to solve problems at scale, Jojo found solace in decision science and learnt to solve a variety of problems across multiple industry verticals early in his career. He started his career with Mu Sigma Inc., the world's largest pure play analytics provider where he worked with the leaders of many fortune 50 clients. With the passion to solve increasingly complex problems, Jojo touch based with Internet of Things and found deep interest in the very promising area of consumer and industrial IoT. One of the early enthusiasts to venture into IoT analytics, Jojo converged his learnings from decision science to bring the problem solving frameworks and his learnings from data and decision science to IoT.
To cement his foundations in industrial IoT and scale the impact of the problem solving experiments, he joined a fast growing IoT Analytics startup called Flutura based in Bangalore and headquartered in the valley. Flutura focuses exclusively on Industrial IoT and specializes in analytics for M2M data. It is with Flutura, where Jojo reinforced his problem solving skills for M2M and Industrial IoT while working for the world's leading manufacturing giant and lighting solutions providers. His quest for solving problems at scale brought the 'product' dimension in him naturally and soon he also ventured into developing data science products and platforms.
After a short stint with Flutura, Jojo moved on to work with the leaders of Industrial IoT, that is, G.E. in Bangalore, where he focused on solving decision science problems for Industrial IoT use cases. As a part of his role in GE, Jojo also focuses on developing data science and decision science products and platforms for Industrial IoT.
I would like to sincerely thank my employers Mu Sigma, Flutura and GE for all the opportunities and learnings I got to explore in decision science and IoT. I would also like give deep thanks and gratitude to my mentors Mr. Samir Madhavan and Mr. Derick Jose, without their efforts this book quite possibly would not have happened.
About the Reviewer
Anindita Basak works as Azure and big data consultant for one of the global software giant and helps partners/customers to enablement of Azure SaaS solution architecture development, data platform & analytics guidance implementation. She is an active blogger, Microsoft Azure forum contributor and consultant as well as speaker. In her 8+ years of experience lifecycle majorly worked in Microsoft .Net, Azure, Big Data and Analytics. Earlier in her career, she worked with Microsoft as FTE as well as v-employee for various internal Azure teams. She recently worked as technical reviewer for the following books from Packt Publishing: HDInsight Essentials First Edition, HDInsight Essentials Second Edition, Hadoop Essentials, and Microsoft Tabular Modeling Cookbook.
I would like to thank my mom and dad—Anjana and Ajit Basak—and my loving brother Aditya. Without your help and encouragement I can't reach the goal of my life.
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Preface
The Internet of Things and decision science are among the most trending topics in the industry right now. The problems we solve today have become increasingly ambiguous, uncertain and volatile, and therefore the means to solve them. Moreover, problem solving has evolved from solving one specific problem using data science to the art of problem solving using decision science. The Internet of Things provides a massive opportunity for business to make human life easier which can only be leveraged using decision science. Smarter Decisions – The Intersection of Internet of Things and Decision Science, will help you learn the nuances of IoT and Decision and practically aid you in smarter decision making by solving real-life Industrial & Consumer IoT use cases. The book gives paramount focus on solving a fundamental problem. Therefore, the entire journey of addressing the problem by defining, designing and executing it using industry standard frameworks for decision science is articulated through engaging and easy-to-understand business use cases. While solving the business use cases, we will touch base with the entire data science stack that is descriptive + inquisitive + predictive + prescriptive analytics by leveraging the most popular and open source software 'R'. By the end of this book, you'll have complete understanding of the complex aspects of decision making in IoT and will be able to take that knowledge with you onto whatever project calls for it.
What this book covers
Chapter 1, IoT and Decision Science, briefly introduces the two most important topics for the book in the most lucid way using intuitive real-life examples. The chapter briefs about IoT, its evolution and the key differences between IoT, IIoT, Industrial Internet, Internet of Everything. Decision science is narrated by providing paramount focus on the problem and its evolution in the universe. Finally we explore the problem solving framework to study the decision science approach for problem solving.
Chapter 2, Studying the IoT Problem Universe and Designing a Use Case, introduces a real life IoT business problem and aids the reader to practically design the solution for the problem by using a structured and mature problem solving framework learnt in the preceding chapter. The chapter also introduces the two main domains in IoT that is connected assets and connected operations and various artefacts and thought leadership frameworks that will be leveraged to define and design a solution for the business problem.
Chapter 3, The What and the Why – Using Exploratory Decision Science for IoT, focuses on practically solving the IoT business use case designed in the preceding chapter using the R software for exploratory data analysis. Leveraging an anonymized and masked dataset for the business use case along with the hands on exercises aids the reader to practically traverse through the descriptive and inquisitive phases of decision science. The problem's solution is addressed by answering the two fundamental questions What and Why by performing univariate, bivariate analyses along with various statistical tests to validate the results and thereby render the story.
Chapter 4, Experimenting Predictive Analytics for IoT, enhances the solution of the business use case by leveraging predictive analytics. In this chapter, we answer the question "when" to solve the problem with more clarity. Various statistical models like linear regression, logistic regression and decision trees are explored to solve the different predictive problems that were surfaced during the inquisitive phase of the business use case in the preceding chapter. Intuitive examples to understand the mathematical functioning of the algorithms and easy means to interpret the results are articulated to cement the foundations of predictive analytics for IoT.
Chapter 5, Enhancing Predictive Analytics with Machine Learning for IoT, takes an attempt to improve the results of predictive modelling exercises in the preceding chapter by leveraging cutting edge machine learning algorithms like Random Forest, XgBoost and deep learning algorithms like multilayer perceptrons. With improved results from improved algorithms, the solution for the use case is finally completed by leveraging the 3 different layers of decision science: descriptive + inquisitive + predictive analytics.
Chapter 6, Fast track Decision Science with IoT, reinforces the problem solving skills learnt so far by attempting to solve another fresh IoT use case from start to end within the same chapter. The entire journey of defining, designing and solving the IoT problem is articulated in a fast track mode.
Chapter 7, Prescriptive Science and Decision Making, introduces the last layer of the decision science stack i.e. prescriptive analytics by leveraging a hypothetical use case. The entire journey of evolution of a problem from descriptive to inquisitive to predictive and finally to prescriptive and back is illustrated with simple and easy to learn examples. After traversing the problem through prescriptive analytics, the art of decision making and storyboarding to convey the results in the most lucid format is explored in detail.
Chapter 8, Disruptions in IoT, explores the current disruptions in IoT by studying a few like fog computing, cognitive computing, Next generation robotics and genomics and autonomous cars. Finally the privacy and security aspects in IoT is also explored in brief.
Chapter 9, A Promising Future with IoT, discusses about how the near future will radically change human life with the unprecedented growth of IoT. The chapter explores the visionary topics of the new IoT business models such as, AssetDevice as a service and the evolution of connected cars to smart cars & connected humans to smart humans.
What you need for this book
In order to make your learning efficient, you need to have a computer with either Windows, Mac, or Ubuntu.
You need to download and install R to execute the codes mentioned in this book. You can download and install R using the CRAN website available at http://cran.r-project.org/. All the codes are written using RStudio. RStudio is an integrated development environment for R and can be downloaded from http://www.rstudio.com/products/rstudio/.
The different R packages used in the book are freely available to download and install for all operating systems mentioned above.
Who this book is for
Smarter Decisions – The intersection of Internet of Things and Decision Science is intended for data science and IoT enthusiasts or project managers anchoring IoT Analytics projects. Basic knowledge of R in terms of its libraries is an added advantage, however the verbiage for interpretation of the results will be independent of the codes. Any non-technical data science and IoT enthusiast can skip the codes and read through the output and still be able to consume the results.
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Chapter 1. IoT and Decision Science
The Internet of Things (IoT) and Decision Science have been among the hottest topics in the industry for a while now. You would have heard about IoT and wanted to learn more about it, but unfortunately you would have come across multiple names and definitions over the Internet with hazy differences between them. Also, Decision Science has grown from a nascent domain to become one of the fastest and most widespread horizontal in the industry in the recent years. With the ever-increasing volume, variety, and veracity of data, decision science has become more and more valuable for the industry. Using data to uncover latent patterns and insights to solve business problems has made it easier for businesses to take actions with better impact and accuracy.
Data is the new oil for the industry, and with the boom of IoT, we are in a world where more and more devices are getting connected to the Internet with sensors capturing more and more vital granular dimensions that had never been touched earlier. The IoT is a game changer with a plethora of devices connected to each other; the industry is eagerly attempting to untap the huge potential that it can deliver. The true value and impact of IoT is delivered with the help of Decision Science. IoT has inherently generated an ocean of data where you can swim to gather insights and take smarter decisions with the intersection of Decision Science and IoT. In this book, you will learn about IoT and Decision Science in detail by solving real-life IoT business problems using a structured approach.
In this chapter, we will begin by understanding the fundamental basics of IoT and Decision Science problem solving. You will learn the following concepts:
It is highly recommended that you explore this chapter in depth. It focuses on the basics and concepts required to build problems and use cases. As hands-on exercises are not added, I am sure most software engineers would be tempted to skip this and move to the later chapters. The later chapters will frequently refer to concepts and points elucidated here for more realistic context. Hence, it's very important to go through this chapter in detail before moving on.
Understanding the IoT
To get started with the IoT, lets first try to understand it using the easiest constructs. Internet and Things; we have two simple words here that help us understand the entire concept. So what is the Internet? It is basically a network of computing devices. Similarly, what is a Thing? It could be any real-life entity featuring Internet connectivity. So now, what do we decipher from IoT? It is a network of connected Things that can transmit and receive data from other things once connected to the network. This is how we describe the Internet of Things in a nutshell.
Now, let's take a glance at the definition. IoT can be defined as the ever-growing network of Things (entities) that feature Internet connectivity and the communication that occurs between them and other Internet-enabled devices and systems. The Things in IoT are enabled with sensors that capture vital information from the device during its operations, and the device features Internet connectivity that helps it transfer and communicate to other devices and the network. Today, when we discuss about IoT, there are so many other similar terms that come into the picture, such as Industrial Internet, M2M, IoE, and a few more, and we find it difficult to understand the differences between them. Before we begin delineating the differences between these hazy terms and understand how IoT evolved in the industry, lets first take a simple real-life scenario to understand how exactly IoT looks like.
IoT in a real-life scenario
Let's take a simple example to understand how IoT works. Consider a scenario where you are a father in a family with a working mother and 10-year old son studying in school. You and your wife work in different offices. Your house is equipped with quite a few smart devices, say, a smart microwave, smart refrigerator, and smart TV. You are currently in office and you get notified on your smartphone that your son, Josh, has reached home from school. (He used his personal smart key to open the door.) You then use your smartphone to turn on the microwave at home to heat the sandwiches kept in it. Your son gets notified on the smart home controller that you have hot sandwiches ready for him. He quickly finishes them and starts preparing for a math test at school and you resume your work. After a while, you get notified again that your wife has also reached home (She also uses a similar smart key.) and you suddenly realize that you need to reach home to help your son with his math test. You again use your smartphone and change the air conditioner settings for three people and set the refrigerator to defrost using the app. In another 15 minutes, you are home and the air conditioning temperature is well set for three people. You then grab a can of juice from the refrigerator and discuss some math problems with your son on the couch. Intuitive, isn't it?
How did it his happen and how did you access and control everything right from your phone? Well, this is how IoT works! Devices can talk to each other and also take actions based on the signals received:
The IoT scenario
Lets take a closer look at the same scenario. You are sitting in office and you could access the air conditioner, microwave, refrigerator, and home controller through your smartphone. Yes, the devices feature Internet connectivity and once connected to the network, they can send and receive data from other devices and take actions based on signals. A simple protocol helps these devices understand and send data and signals to a plethora of heterogeneous devices connected to the network. We will get into the details of the protocol and how these devices talk to each other soon. However, before that, we will get into some details of how this technology started and why we have so many different names today for IoT.
Demystifying M2M, IoT, IIoT, and IoE
So now that we have a general understanding about what is IoT, lets try to understand how it all started. A few questions that we will try to understand are: Is IoT very new in the market?, When did this start?, How did this start?, Whats the difference between M2M, IoT, IoE, and all those different names?, and so on. If we try to understand the fundamentals of IoT, that is, machines or devices connected to each other in a network, which isn't something really new and radically challenging, then what is this buzz all about?
The buzz about machines talking to each other started long before most of us thought of it, and back then it was called Machine to Machine Data. In early 1950, a lot of machinery deployed for aerospace and military operations required automated communication and remote access for service and maintenance. Telemetry was where it all started. It is a process in which a highly automated communication was established from which data is collected by making measurements at remote or inaccessible geographical areas and then sent to a receiver through a cellular or wired network where it was monitored for further actions. To understand this better, lets take an example of a manned space shuttle sent for space exploration. A huge number of sensors are installed in such a space shuttle to monitor the physical condition of astronauts, the environment, and also the condition of the space shuttle. The data collected through these sensors is then sent back to the substation located on Earth, where a team would use this data to analyze and take further actions. During the same time, industrial revolution peaked and a huge number of machines were deployed in various industries. Some of these industries where failures could be catastrophic also saw the rise in machine-to-machine communication and remote monitoring:
Telemetry
Thus, machine-to-machine data a.k.a. M2M was born and mainly through telemetry. Unfortunately, it didn't scale to the extent that it was supposed to and this was largely because of the time it was developed in. Back then, cellular connectivity was not widespread and affordable, and installing sensors and developing the infrastructure to gather data from them was a very expensive deal. Therefore, only a small chunk of business and military use cases leveraged this.
As time passed, a lot of changes happened. The Internet was born and flourished exponentially. The number of devices that got connected to the Internet was colossal. Computing power, storage capacities, and communication and technology infrastructure scaled massively. Additionally, the need to connect devices to other devices evolved, and the cost of setting up infrastructure for this became very affordable and agile. Thus came the IoT. The major difference between M2M and IoT initially was that the latter used the Internet (IPV4/6) as the medium whereas the former used cellular or wired connection for communication. However, this was mainly because of the time they evolved in. Today, heavy engineering industries have machinery deployed that communicate over the IPV4/6 network and is called Industrial IoT or sometimes M2M. The difference between the two is bare minimum and there are enough cases where both are used interchangeably. Therefore, even though M2M was actually the ancestor of IoT, today both are pretty much the same. M2M or IIoT are nowadays aggressively used to market IoT disruptions in the industrial sector.
IoE or Internet of Everything was a term that surfaced on the media and Internet very recently. The term was coined by Cisco with a very intuitive definition. It emphasizes Humans as one dimension in the ecosystem. It is a more organized way of defining IoT. The IoE has logically broken down the IoT ecosystem into smaller components and simplified the ecosystem in an innovative way that was very much essential. IoE divides its ecosystem into four logical units as follows:
Built on the foundation of IoT, IoE is defined as The networked connection of People, Data, Processes, and Things. Overall, all these different terms in the IoT fraternity have more similarities than differences and, at the core, they are the same, that is, devices connecting to each other over a network. The names are then stylized to give a more intrinsic connotation of the business they refer to, such as Industrial IoT and Machine to Machine for (B2B) heavy engineering, manufacturing and energy verticals, Consumer IoT for the B2C industries, and so on.
Digging deeper into the logical stack of IoT
Now that we have a clear understanding of what is IoT and the similar terms around it, lets understand the ecosystem better. For convenience, IoE will be referred as IoT while exploring the four logical components of the stack in brief.
IoTs logical stack
When we deconstruct the IoT ecosystem into logical units, we have People, Processes, Data, and Things. Lets explore each of these components in brief.
People
People or we interact with devices and other people on a daily basis. The communication could be either People to People, People to Device, or Device to People. Considering People as a separate dimension in the IoT ecosystem is an essential move as the complexity in understanding this is really challenging. When any form of communication occurs where People play a role on either end of the interaction, it embeds a unique pattern that is intrinsic to the People dimension. Lets understand this better with an example. Most of us use social networking sites such as Facebook, Twitter, LinkedIn, and so on, where we are connected to multiple people/friends. Here, the communication paths are mainly People to People. Considering the previous example, we had people to device and device to people communication paths (communication between the smartphone and microwave). Considering People as a dimension, everyone would differ in the way they interact with the system. I might find the new interface of Facebook very difficult to use but a friend may find it extremely easy. The real problem here is everyone is skilled, but the skillsets differ from person to person. The characteristics of the interaction identified by a person may be a representative for a very small community.
We have a population of six billion plus, and over 1/6th of them have already been connected. With such a huge population consisting of a plethora of communities representing people of different geographical areas, culture, thinking, and behavior, defining one generic set of rules or characteristics to define people interaction is very challenging. Instead, if we understand the People dimension in a more constructive way, we can tap the opportunity to capture the behavior more accurately and help them benefit from the ecosystem in the best way.
With the advent of IoT, we have sensors capturing information and characteristics at more granular levels than ever before. Here, if we can accurately define People as a complete dimension, personalized experience will be a complete game changer. The smart watch industry is investing humongous efforts to get its offering more personalized; if it succeeds, it will be a pivotal player in the coming revolution.
Processes
The most lucid definition for Processes would be everything required to deliver the right information to the right person/system at the right time. A wide variety of things fall in the Processes dimension that includes technology, protocols, business logic, communication infrastructure, and so on. Broadly, they can be classified into two components-Technology and Business Processes. Lets explore these two components in brief in order to understand the Processes dimension in more detail.
Technology
The technology required in the Processes dimension of IoT comprises of the software, protocol, and infrastructure. We will explore Technology by understanding its three broad divisions for Processes.
Software
Software consists mainly of the operating system. Devices in IoT require a special kind of an operating device. Smart devices such as the smart refrigerator, smart microwave, and many others require an operating system running on them that can then enable it to be an active component in the network. Tasks executed can vary from sending, processing, and receiving data or executing instructions and sending signals to respective controllers within the device for action. Now, the question is, why do these devices require a special operating system? Why cant the existing rich flavors of Unix/Linux, Windows, Mac, or even Android be used? The answer is the same as the reason that we used Android for smartphones and not the existing OS back then. The devices that connect to the network in IoT are small or sometimes tiny. Ideally, these devices would be equipped with less powerful computing capabilities, lower memory, and lower battery life. It is almost impossible to run a fully-fledged operating system on them. We need a specially designed OS that can take care of the limited memory, processing power and battery life of the device and yet provide maximum functionality to tag the device as a smart device. Google recently launched an operating system for IoT devices called Brillo. Brillo is an Android-based embedded operating system specifically designed for low power and memory-constrained IoT devices. It provides the core platform services required for IoT devices along with a developer kit freely available for developers/hardware vendors to get the OS running and build additional services on their devices. Some similar examples would be Apple's Watch OS for Apple Watch, Android Wear from Google for smartwatches, and others. Soon, we can expect a vast community of devices running Brillo and a plethora of apps that can be installed additionally for even better functionality (something very similar to the Google Play store).
Protocol
Once the devices are software-enabled, we need to get a protocol in place that can help them communicate with other heterogeneous devices in the network. To understand this better, recollect the first example where we could defrost the refrigerator using our smartphone. The smartphone needs to talk to the refrigerator that also needs to understand what exactly is being communicated. With a huge variety of heterogonous devices, this communication path just gets more and more complicated. Hence, we need to have a simplified protocol in place where complicated process can be abstracted and the devices can communicate with each other effectively. Google recently launched an open source protocol called Weave. Weave is basically an IoT protocol that is a communications platform for IoT devices that enables device setup, phone-to-device-to-cloud communication, and user interaction from mobile devices and the web. It has ushered productivity in the developers efforts by easing up device interoperability regardless of the brand or manufacturer.
Infrastructure
Infrastructure can simply be defined as the integration of the operating system, communication protocol, and all other necessary components to harmonize the environment for an IoT use case. All major cloud infrastructure providers are now focusing on providing an IoT-specialized environment. Google launched IoT Cloud Solutions, Amazon launched AWS IoT, Microsoft launched Azure IoT Suite, and so on. All of these solutions integrate the disparate systems together to make the ecosystem scalable and agile. Digging deeper into these suites will be beyond the scope of this book.
Business processes
The second part of the Processes dimension is Business Processes. It basically covers the set of rules and processes to govern the communication and operation of the devices connected in the IoT ecosystem. There isn't a concrete definition till now that can be used here and the discussion of the topic will be beyond the scope of this book. However, we will take a look at this closely while solving an IoT use case in Chapter 3, The What And Why - Using Exploratory Decision Science for IoT and Chapter 4, Experimenting Predictive Analytics for IoT.
Things
Things form the crux of the IoT ecosystem. They include any form of sensors, actuators, or other type of devices that can be integrated into machines and devices to help them connect to the Internet and communicate with other devices and machines. These things will be always active during their lifetime and will sense events, capture important information, and communicate them with other devices.
A typical example would be the refrigerator, TV, or microwave that we considered in the previous use case. The sensors installed in these devices capture data and send information/signals to other devices that can then be used to take action.
Data
Data is by all means the most value-adding dimension in the IoT ecosystem. Today, the devices that are connected to the Internet are capturing tons and tons of data that can represent the most granular-level details for the devices they are connected to. The magnitude of this data is colossal. Storing and processing such vast and varied amounts of data questions the fact whether the data is really valuable. In a true sense, most of the data is transient in nature and loses its value within minutes of generation. With ever-improving technology and computing capabilities, the amount of data processing and storage that the devices are capable of today is great, but we can leverage this power to deliver better value than just delivering raw data. Tons of algorithms can be executed and business rules can be applied where a lot of value can be extracted from the data before sending it over to the server. This requires the combination of multiple disciplines together to solve the problem and deliver value.
To understand this better, consider the example of a pedometer installed in our smart watch. Rather than just reporting the number of steps that we have walked, it can calculate the amount of calories we have burned, average time taken for the activity, metrics like deviation from the previous days activity, deviation from milestones, and other social information such as how do we compare with our friends, and so on. To capture and process all of this information locally and send the final results to the server that can be directly stored for future actions requires the combination of multiple disciplines to make the task efficient. Math, business, technology, design thinking, behavioral science, and a few others would need to be used together to solve the problem. In reality, it would be futile to send across all the raw data captured from devices to the servers assuming that it can be leveraged for future use. A variety of new algorithms have been designed to ingest this data locally and deliver only rich, condensed, and actionable insights in real time. We will explore this in more detail with fog computing in Chapter 8, Disruptions in IoT. Smart watches such as the Microsoft Band and self-driving cars such as Tesla Model S are the best examples to understand the true scenarios where we can study the challenges of processing data in real time for insights and actions. In all true sense, data is what essentially helps in delivering the last mile value in the IoT fraternity. Hence, we need talent to deal with the data as a separate dimension in the IoT stack.
The problem life cycle
You learned about IoT and explored its logical stack to understand more about People, Processes, Data, and Things. The core agenda of this book is to solve IoT business problems using Decision Science. Problem solving has been an art and has its origin ever since mankind evolved. I would like to introduce The Problem Life Cycle to learn how the problem keeps evolving. Understanding this topic is very essential to solve better problems in IoT.
Every industry has been trying to solve a problem. E-retail solved the problem of inconvenience in physical shopping for busy and working consumers, the printing press solved the problem of mass producing documents for the consumers, and so on. A few visionaries such as Apple Inc. have tried to solve a problem by first creating it. The iPod and iPad were devices that were a part of this revolution. The biggest challenge in solving a problem is that the problem evolves. If we take a deeper look at the problem life cycle, we can understand that the problem evolves from a Muddy to Fuzzy and finally to a Clear state and keeps repeating the cycle:
The problem life cycle
Lets take a simple example to understand this better. Consider the Marketing problem. Every organization wants to promote their products and services better by marketing them. Marketing has been a problem since ages. Lets assume that the inception of marketing happened with the invention of the printing press. Initially, the problem for marketing would be in the muddy phase, where a team of analysts would try to get the best strategy to market a product or service in place. Back then, newspapers and print media were the only medium, and the strategies and nature of the problem was very much limited to them. When the problem is new, it is in the muddy stage; we have no clear idea about how to solve it. We would try to understand the problem by experimenting and researching. Gradually, we gain some knowledge about the system and problem and then define a couple of best strategies and guidelines to solve the problem. This is when the problem evolves to the fuzzy stage. Here, the solution for the problem is still not clear, but we have a fair understanding of how to go about it. Finally, after a lot of research and experiments from a large pool of people sharing their results and understandings, we might finally have a concrete methodology in place that can be used as a complete guide to solve the problem. This is when the problem reaches the clear stage. It is the pinnacle of the problem solving methodology where we have a clear understanding about how to tackle the problem and solve it. However, one fine day, a big disruption happens and the problem that was finally in the clear state collapses and returns to the muddy stage. In the case of marketing, when people aced the best strategies to advertise using print media and newspapers, it collapsed with the invention of the radio. All of a sudden, the nature of the problem changed and it required a radically different approach to solve it. The experts, who had concrete approaches and strategies for the problem solving back then, had to revisit and start from the beginning as the problem went back to the muddy stage. The problem life cycle kept evolving, and this was repeated when television was introduced and again when social media was introduced. Today, with the social media booming and expanding to newer areas, we have the marketing problem currently stable at the fuzzy state. Soon, with the advent of virtual reality and augmented reality, it is expected to roll back to the muddy phase.
To get more real, lets relate the scenario with a more recent version of the problem. Consider a social media analyst trying to solve a problem: optimizing targets for sponsored ads that need to be placed in the Facebook newsfeed for a user based on his behavior. If we find the user to be a football enthusiast, we would insert an ad into his newsfeed for a sportswear brand. To keep things simple, assume that we are the first ones to do this and no one has ever attempted this in history. The problem will currently be in the muddy state. So logically, there would be no references or material available over the Internet for our help and research. Our problem solving task begins by identifying the users interest. Once he has been identified as a potential user with an interest in football, we need to place a sponsored ad in his newsfeed. How do we discover the users interest? There are a variety of metrics that can help us discover his interests, but for simplicity, lets assume that the users interests will be identified purely by the Status Updates he posts on his wall.
We can then simply try to analyze the statuses updated by the person and define his interests. If the word Football or names of any popular football players or football teams appear more than a desired threshold, we can possibly say that he would be following football and hence would be a potential target. Based on this simple rule, we create better strategies and algorithms where our accuracy of finding the potential users can be reached with the minimum amount of time and effort. Gradually, the problem moves from the muddy stage to the fuzzy stage. We now have a good amount of understanding regarding the problem. We may not have the best and most effective solution for the problem, but we definitely have a fair idea to get started and find a solution without too much research. Over a period of time, we, and many other similar folks, conduct various experiments, publish various blogs and research papers of the results, and help others learn from our methods and experiment more. Eventually, there would be a time when we will have attempted the exhaustive solution paradigms and have the knowledge for the best and most effective solution for any sort of analysis in that domain. Finally, it reaches its pinnacle point-the clear stage.
One day, all of a sudden, Facebook and other social media giants launch a new feature. Users can now share photos along with their status updates. A radical change will be seen in the way the user will now use the social network. People tend to post more photos than text updates. All the thought-leadership frameworks and research papers and blogs that proved to be highly successful earlier now seem to be ineffective. We are not sure how to analyze photos updated by the user in order to understand his interests. Unfortunately, the problem goes back to the muddy stage. These big changes keep happening again and again. After photos, it will be videos, then audios, and so on, and the cycle keeps repeating as usual. Recently, the user behavior on social networks has dramatically changed. People post more pictures than type any comment or status updates. These photos may or may not be symbolic of the message that the user wants to convey. Sarcasm or satire may be the objective. The memes that get viral over the Internet have no clear message embedded in them. It may be sarcasm or simple smileys that the user wants to comment on. Analyzing the meaning of these images (memes) to understand the actual message that the user wants to convey with algorithms and computers to find out his interests is a challenging deal.
Hence, understanding the problem life cycle helps prepare us better for the evolution of the problem and adapt the problem solving strategies better and faster.
The problem landscape
Two questions that will have definitely surfaced in our thoughts are as follows:
Lets see how this will be helpful. While solving a problem, understanding the current state of the problem is essential for the analyst. Whenever we solve a problem, we would always prepare for the next state of the problem life cycle knowing that change in the problems current state is inevitable. If the problem is currently in the clear state, then the amount of time and effort we would invest as a data scientist would be considerably less than if the problem would have been in the muddy or fuzzy stage. However, the problem remains for the least amount of time in the clear stage. The jump from clear to muddy is shorter compared to any other transition in the problem life cycle. Being aware about the problem life cycle, an organization/data scientist would then prepare better for radical changes that are bound to happen in a short while. We would need to design our solution to be agile and prepare for the next change. Similarly, if the problem is in the fuzzy stage, a lot of our solutions will be designed in such a way that they can be productized for a particular use case or industry. Finally, when the solution is in the muddy state, our solutions in problem solving will be more of a service-based offering than a product. The amount of experiments and research that we would need for the problem to be solved is highest in the muddy state and least in the clear state:
The problem life cycle in brief
So how does this relate to IoT and Decision Science and the intersection of the two? Decision Science has been a bit more widespread and prevalent in the industry than IoT. There have been tons of experiments and research conducted on data to find insights and add value that make Decision Science currently in the fuzzy stage. IoT, on the other hand, is fairly new and requires loads of research and experiments to get tangible results, which makes it in the muddy stage. However, when we talk about the intersection of the two, we are dealing with a set of interesting problems. On one side, we have a fairly mature ecosystem of Decision Science that has given tangible value to the industry through its experiments whereas IoT is still nascent. The intersection of the two is a very promising and lucrative area for business. It is in a position where it is steadily moving from the muddy to fuzzy stage. Very soon, we will see tangible results from large-scale IoT use cases in the industry that will immediately trigger the revolution for productization on Decision Science for IoT. Decision Science for IoT is rapidly being experimented and the initial results seem to be very promising. The era, where Decision Science for IoT will be in the fuzzy state, is very near.
With this in mind, we can now get to the basics of problem solving while being prepared for the use case to evolve into a fuzzy state. With the understanding of the problem life cycle concrete, lets now explore the problem landscape in detail.
What is the problem landscape? Why do we need to bother about it?
A simple answer would be, understanding the current state of the problem is just one dimension, but understanding the type of problem is a more essential part of problem solving. Lets make this simple. To understand the problem landscape, refer to the following image and try to visualize the problems on two dimensions-frequency and impact. Just like any other scatterplot, this one can also be divided into four major areas:
