Why analytical skills for AI

Judging from the headlines and commentary in social media during the second half of the 2010s, the age of artificial intelligence has finally arrived with its promises of automation and value creation. Not too long ago, a similar promise came with the big data revolution that started around 2005. And while it is true that some selected companies have been able to disrupt industries through AI- and data-driven business models, many have yet to realize the promises.

There are several explanations for this lack of measurable results — all with some validity, surely — , but the one put forward in this book is the general lack of analytical skills that are complementary to these new technologies.

The central premise of the book is that value at the enterprise is created by making decisions, not with data or predictive technologies alone. Nonetheless, we can piggyback on the big data and AI revolutions and start making better choices in a systematic and scalable way, by transforming our companies into modern AI- and data-driven decision-making enterprises.

To make better decisions we need first to ask the right questions, forcing us to move from descriptive and predictive analyses to prescriptive courses of action. I will devote the first few chapters on clarifying these concepts and learning how to ask better business questions suitable for this type of analysis. I will then delve into the anatomy of decision-making, starting with the consequences or outcomes we want to achieve, moving backwards to the actions we can make, and discussing the problems and opportunities created by intervening uncertainty and causality. Finally we will learn how to pose and solve prescriptive problems.

Use-case-driven approach

Since my aim is to help practitioners to create value from AI and data science using this analytical skillset, in each chapter I will show how each skill works with the help of a collection of use cases. I selected them from my own experience, because many companies face them and are thus advertised by consulting companies without providing alternative solutions, because students found them interesting or because they are building blocks for more complex problems that are found in the industry. But in the end this choice was subjective and depending on your industry they may be more or less relevant.

What this book isn’t

This book isn’t about artificial intelligence or machine learning. This book is about the extra skills needed to be successful at creating value from these predictive technologies.

I have provided an introduction to machine learning in the Appendix for the purpose of being self-contained, but it isn’t a detailed presentation of machine learning related material nor was it planned as one. For that you can check many of the great books out there (some mentioned in the Suggested Readings section of the Appendix).

Who is this book for

This book is for anyone wanting to create value from machine learning. I’ve used parts of the material with business students, data scientists and business people alike.

The most advanced material deals with decision-making under uncertainty and optimization, so having a background on probability, statistics or calculus should help. For readers without this background I’ve tried to make the presentation self-contained. On a first pass, you may just skip the technical details and focus on developing an intuition and an understanding of the main messages for each chapter.

• If you’re a business person with no interest whatsoever in doing machine learning yourself, this book should at least help redirect the questions you want your data scientists to answer. Business people have great ideas but have difficulties expressing what they want to more technical types. If you want to start using AI in your own line of work, this book will help you formulate and translate the questions so that others can work on the solution. My hope is that it will also serve as inspiration to solve new problems you didn’t think were attainable.

• If you’re a data scientist, this book will provide a holistic view on how you can approach your stakeholders and generate ideas to apply your technical knowledge. In my experience, data scientists become really good at solving predictive problems, but many times have difficulties delivering prescriptive courses of action. The result is that your work doesn’t create as much value as you want and expect. If you’ve felt frustrated because your stakeholders don’t understand the relevance of machine learning, this book could help you transform the question you’re solving to take it “closer to the business”.

• If you’re a developer interested in data science this book will take you closer to the business and provide an understanding of how data science creates value. You may already have other more technical readings on your path to deep learning and the like, so this may feel just right when you want to read something more “businessy” without completely losing the more formal and technical foundations. It should also serve as a North Star to remind you that it’s not about technical knowledge but about value creation.

What’s needed

I wrote this book in a style that is supposed to be readable for very different audiences. I do not expect the reader to have any prior knowledge of probability or statistics, machine learning, economics or the theory of decision making.

Readers with such backgrounds will find the more technical material introductory, and that’s actually great. In my opinion, the key to creating value through these techniques is to not focus on the technical side but on the business. I hope that by focusing on the use cases they can find many new ways to solve the problems they’re facing.

For readers with no background in these topics I’ve tried to provide a very minimal introduction to the key themes that I need to develop each of the use cases. If you’re interested in going deeper I’ve also provided a list of references that I’ve found useful, but I’m sure you can find many more on the internet. If you’re not interested in going deeper, that’s fine too. My advice is to focus on the broader picture and intuition. That way you’ll be able to ask the right questions to the right people at your companies.

What’s really needed to get the most value from this book is curiosity. And if you’ve reached this paragraph most likely you’re good on this.

Conventions Used in This Book

The following typographical conventions are used in this book:

Italic

Indicates new terms, URLs, email addresses, filenames, and file extensions.

Constant width

Used for program listings, as well as within paragraphs to refer to program elements such as variable or function names, databases, data types, environment variables, statements, and keywords.

Constant width bold

Shows commands or other text that should be typed literally by the user.

Constant width italic

Shows text that should be replaced with user-supplied values or by values determined by context.

Using Code Examples

Supplemental material (code examples, exercises, etc.) is available for download at https://github.com/oreillymedia/title_title.

This book is here to help you get your job done. In general, if example code is offered with this book, you may use it in your programs and documentation. You do not need to contact us for permission unless you’re reproducing a significant portion of the code. For example, writing a program that uses several chunks of code from this book does not require permission. Selling or distributing a CD-ROM of examples from O’Reilly books does require permission. Answering a question by citing this book and quoting example code does not require permission. Incorporating a significant amount of example code from this book into your product’s documentation does require permission.

We appreciate, but do not require, attribution. An attribution usually includes the title, author, publisher, and ISBN. For example: “Analytical Thinking for AI and Data Science by Daniel Vaughan (O’Reilly). Copyright 2020 Daniel Vaughan, 978-1-492-06094-9.”

If you feel your use of code examples falls outside fair use or the permission given above, feel free to contact us at [email protected].

O’Reilly Online Learning

Our unique network of experts and innovators share their knowledge and expertise through books, articles, conferences, and our online learning platform. O’Reilly’s online learning platform gives you on-demand access to live training courses, in-depth learning paths, interactive coding environments, and a vast collection of text and video from O’Reilly and 200+ other publishers. For more information, please visit http://oreilly.com.

How to Contact Us

Please address comments and questions concerning this book to the publisher:

• O’Reilly Media, Inc.
• 1005 Gravenstein Highway North
• Sebastopol, CA 95472
• 800-998-9938 (in the United States or Canada)
• 707-829-0515 (international or local)
• 707-829-0104 (fax)

We have a web page for this book, where we list errata, examples, and any additional information. You can access this page at http://www.oreilly.com/catalog/9781492060949.

To comment or ask technical questions about this book, send email to [email protected].

For more information about our books, courses, conferences, and news, see our website at http://www.oreilly.com.

Find us on Facebook: http://facebook.com/oreilly

Follow us on Twitter: http://twitter.com/oreillymedia

Watch us on YouTube: http://www.youtube.com/oreillymedia

Acknowledgments

This book had three sources of inspiration. First, it has been the backbone in a Big Data for Managers course at the Tecnologico de Monterrey, in Mexico City. As such I’m grateful to the university and the EGADE Business School specifically; they have provided a great place to think, discuss and lecture on these ideas. Each cohort of students helped improve on the material, presentation and use cases. To them I’m infinitely grateful.

My second source of inspiration came from my work as Head of Data Science at Telefonica Movistar Mexico and the wonderful team of data scientists that were there during my tenure. They helped create a highly energetic atmosphere where we could think out of the box and propose new projects to our business stakeholders.

I’m finally indebted to the different business people that I’ve encountered during my career, and especially during my tenure at Telefonica Movistar Mexico. It was never easy to sell these ideas, and the constant challenge helped improve my understanding of how they view the business, forcing me to build bridges between these two seemingly unrelated worlds.

I’m grateful to my family and friends for their support from the beginning. Finally, I’m infinitely grateful to my dogs Matilda and Domingo. They were the perfect companions to the many long hours working on the book, always willing to cheer me up. We’ll finally have more time to go to the park now.

Last but not least, I’m deeply grateful to my editor, Michele Cronin. Her suggestions dramatically helped improve the presentation of the book. Any mistakes that remain are my own, of course.