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
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11
Index
Appendix A: My Most Popular Economics of Data, Analytics, and Digital Transformation Infographics

Summary

The Schmarzo Economic Digital Asset Valuation Theorem provides a compelling economic reason for organizations to make the necessary investments in the creation, sharing, reuse, and continuous refinement of their data and analytic assets—assets that not only never deplete, never wear out and can be used across an unlimited number of use cases at near-zero marginal cost, but assets that appreciate in value the more that they are used.

In summary, by reusing the datasets and analytics modules, organizations thereby not only maintain but increase the economic value of their data and analytic modules. This affords organizations a once-in-a-generation opportunity to exploit the unique economic characteristics of their data, and analytic modules to derive and drive new sources of customer, product, and operational value buried in their data.

Following the guidelines outlined in this book and highlighted in this chapter, organizations can reap the game-changing effects...