Build a continuously learning and adapting organization that can extract increasing levels of business, customer and operational value from the amalgamation of data and advanced analytics such as AI and Machine Learning
▶Book Description
In today's digital era, every organization has data, but just possessing enormous amounts of data is not a sufficient market discriminator.
The Economics of Data, Analytics, and Digital Transformation aims to provide actionable insights into the real market discriminators, including an organization's data-fueled analytics products that inspire innovation, deliver insights, help make practical decisions, generate value, and produce mission success for the enterprise.
The book begins by first building your mindset to be value-driven and introducing the Big Data Business Model Maturity Index, its maturity index phases, and how to navigate the index. You will explore value engineering, where you will learn how to identify key business initiatives, stakeholders, advanced analytics, data sources, and instrumentation strategies that are essential to data science success. The book will help you accelerate and optimize your company's operations through AI and machine learning.
By the end of the book, you will have the tools and techniques to drive your organization's digital transformation.
Here are a few words from Dr. Kirk Borne, Data Scientist and Executive Advisor at Booz Allen Hamilton, about the book:
Data analytics should first and foremost be about action and value. Consequently, the great value of this book is that it seeks to be actionable. It offers a dynamic progression of purpose-driven ignition points that you can act upon.
▶What You Will Learn
⦁Train your organization to transition from being data-driven to being value-driven
⦁Navigate and master the big data business model maturity index
⦁Learn a methodology for determining the economic value of your data and analytics
⦁Understand how AI and machine learning can create analytics assets that appreciate in value the more that they are used
⦁Become aware of digital transformation misconceptions and pitfalls
⦁Create empowered and dynamic teams that fuel your organization's digital transformation
▶Key Features
⦁Master the Big Data Business Model Maturity Index methodology to transition to a value-driven organizational mindset
⦁Acquire implementable knowledge on digital transformation through 8 practical laws
⦁Explore the economics behind digital assets (data and analytics) that appreciate in value when constructed and deployed correctly
▶Who This Book Is For
This book is designed to benefit everyone from students who aspire to study the economic fundamentals behind data and digital transformation to established business leaders and professionals who want to learn how to leverage data and analytics to accelerate their business careers.
▶What this book covers
⦁ Chapter 1, The CEO Mandate: Become Value-driven, Not Datadriven, 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.
