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

Value Engineering: The Secret Sauce for Data Science Success

If we believe that the Big Data Business Model Maturity Index described in Chapter 1, The CEO Mandate: Become Value-driven, Not Data-driven, is what organizations could do to become more effective at leveraging data and analytics to power their business models, then your next question is "How can I achieve that?"

Let me introduce you to the Data Science Value Engineering Framework (see Figure 2.1).

Figure 2.1: Data Science Value Engineering Framework

The Data Science Value Engineering Framework (process) provides a simple yet effective methodology for exploiting the economic value of your data and analytic assets; a methodology to drive the collaboration between the business subject matter experts (stakeholders) and your data science team to apply data and analytics to improve the operational and business effectiveness of all industries including healthcare, public safety, manufacturing, transportation, energy, education, the environment, sports, entertainment, financial services, retail, and more.

Let's drill into each of the steps of the Data Science Value Engineering Framework—the "How to do it" framework.