Book Image

The Economics of Data, Analytics, and Digital Transformation

By : Bill Schmarzo
5 (2)
Book Image

The Economics of Data, Analytics, and Digital Transformation

5 (2)
By: Bill Schmarzo

Overview of this book

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."
Table of Contents (14 chapters)
10
Other Books You May Enjoy
11
Index
Appendix A: My Most Popular Economics of Data, Analytics, and Digital Transformation Infographics

Chapter 5: The Economic Value of Data Theorems

  • The Data Economic Multiplier Effect: Data never wears out, never depletes, and can be used across an unlimited number of use cases at near-zero marginal cost.
  • Theorem #1: It isn't the data itself that's valuable; it's the trends, patterns, and relationships gleaned from the data about your customers, products, and operations that are valuable.
  • Theorem #2: It is from the quantification of the trends, patterns, and relationships that drive predictions about what is likely to happen.
  • Theorem #3: Predictions drive monetization opportunities through improved business and operational use cases.
  • Theorem #4: The ability to reuse the same datasets across multiple use cases is the real economic game-changer.
  • Theorem #5: Trying to optimize across a diverse set of objectives can yield more granular, higher fidelity outcomes that enable "doing more with less".