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

Digital Economics Effect #1: Marginal Costs Flatten

Since data never depletes, never wears out and can be reused against an unlimited number of use cases at near-zero marginal cost, reusing "curated" data and analytic modules reduce the marginal costs for future use cases (see Figure 7.4).

Figure 7.4: Effect #1: Marginal Costs of Digital Assets Flatten

For organizations to realize the economic benefits of Effect #1, they must actively work to stomp out data silos that inhibit the sharing of organizational data. This is the biggest inhibitor of the economic value of data, as discussed in Chapter 4, University of San Francisco Economic Value of Data Research Paper. Sharing and reusing the "curated" data enables the organization to exploit the data lake to flatten marginal costs. "Curated" data is a dataset in which investments have been made to improve the data's cleanliness, completeness, accuracy, granularity, and latency; in which the...