What this book covers

Chapter 1, The CEO Mandate: Become Value-driven, Not Data-driven, covers the Big Data Business Model Maturity Index and how organizations can become more effective at leveraging data and analytics to power their business models. It discusses the five stages of the Big Data Business Model Maturity Index—Business Monitoring, Business Insights, Business Optimization, Insights Monetization, and Digital Transformation—and provides a best-in-industry benchmark against which organizations can compare themselves (so that they know what "good" looks like), as well as a roadmap for how organizations can become more effective at leveraging data and analytics.

Chapter 2, Value Engineering: The Secret Sauce for Data Science Success, entails my Data Science Value Engineering Framework, a process that starts with a thorough understanding of the organization's key business initiatives, or what the organization is trying to achieve from a business or operational perspective. The Data Science Value Engineering process identifies and interrogates the key stakeholders to identify their top priority use cases (clusters of decisions around a common subject area) that support the business initiative. Once you have identified, validated, valued, and prioritized the use cases, then the supporting data, analytics, architecture, and technology requirements fall out as a consequence of the process.

Chapter 3, A Review of Basic Economic Concepts, is about Economics—the branch of knowledge concerned with the production, consumption, and transfer of wealth or value. Economics provides the framework that we will use to ascertain the value of the organization's data. Also, economics plays a huge role in justifying the game-changing potential of composable, reusable, continuously learning analytic modules. We will review some fundamental economic concepts, such as the Economic Value Curve, the Economic Multiplier Effect, Price Elasticity, the Economic Utility Function, and the Law of Supply and Demand, and discuss the applicability of those economic concepts to the world of data and analytics.

Chapter 4, University of San Francisco Economic Value of Data Research Paper, is the heart of the book and covers the research paper that Professor Mouwafac Sidaoui and I wrote while at the University of San Francisco on determining the value of data. During this research project, my initial frame of thinking was transformed by a simple statement by a research assistant—that data was an unusual asset that never wore out, never depleted, and could be applied against an unlimited number of use cases at a near-zero marginal cost. That's when I realized that determining the value of data wasn't an accounting exercise; it was an economics exercise. Yep, lots of "unlearning" for me!

Chapter 5, The Economic Value of Data Theorems, discusses the Economic Value of Data learning that I have observed since the release of that research paper. I introduce several Economic Value of Data "Theorems" that organizations can use to guide their data, analytic, and human investments to derive and drive new sources of customer, product, and operational value.

Chapter 6, The Economics of Artificial Intelligence, builds on one of the key inhibitors to the Economic Value of Data that we uncovered in the research paper—orphaned analytics. Since the completion of the USF research project, two companies have totally transformed my thinking about the game-changing potential of leveraging Artificial Intelligence (AI) to create analytic assets that appreciate, not depreciate, in value the more that they are used. This is truly an eye-opening chapter!

Chapter 7, The Schmarzo Economic Digital Asset Valuation Theorem, builds upon the economic aspects of data and analytics covered in the previous chapters to create the "Schmarzo Economic Digital Assets Valuation Theorem." I drill into the concepts that support the theorem and provide detailed examples as to how it works. Hopefully, this work will be sufficient to convince the Royal Swedish Academy of Sciences that I am worthy of a Nobel Prize in Economics (otherwise, I'll just have to settle with having written this book instead).

Chapter 8, The 8 Laws of Digital Transformation, brings together the data and analytic concepts from the other chapters to create the Digital Transformation roadmap, including the "laws" that guide an organization's digital transformation. And while this chapter may be a wee bit presumptive (since organizations actually will never complete their digital transformations), it will provide guidance as to what some organizations can do today to further their digital transformation.

Chapter 9, Creating a Culture of Innovation Through Empowerment, concludes the book with a focus on the role of empowering teams to drive sustainable and continuous digital transformation. This may be the most important chapter in the book because if you haven't empowered your teams, then no amount of data and analytics will make a difference in your digital transformation. I'll give examples about how organizations can empower teams that strive toward the Best "Best Options" (instead of settling for the Least "Worst Options") on the path to scaling innovation.

Appendix A, My Most Popular Economics of Data, Analytics, and Digital Transformation Infographics, includes my most popular infographics. Infographics are a great communication tool; the modern storyteller's weapon to give difficult and provocative ideas voice and make them come to life. I hope that you enjoy these infographics as much as I do.

Appendix B, The Economics of Data, Analytics, and Digital Transformation Cheat Sheet, provides a cheat sheet summary of all the chapters.

Throughout the book, several fundamental enablers repeat—Analytic Profiles and Digital Twins as critical analytic assets; composable, reusable, continuously learning analytic modules; a use case-by-use case approach for valuing and building one's data and analytic assets; and the data lake as a collaborative value creation platform. These are the connective tissue that ties everything together and forms the basis for understanding and mastering the economics of data, analytics, and digital transformation.

I hope you enjoy the book and remember if you want to change the game, change the frame.

Download the color images

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. You can download it here: https://static.packt-cdn.com/downloads/9781800561410_ColorImages.pdf

Conventions used

There are a number of text conventions used throughout this book.

Bold: Indicates a new term, an important word, or words that you see on the screen, for example, in menus or dialog boxes, also appear in the text like this. For example: "Develop a Business Case with financial and business justification and supporting Return on Investment (ROI) analysis."