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

The Economic Value of Data Calculation

We will end this chapter with the mathematical formula for the Economic Value of Data calculation. If you can't put the formula into math, well, it'll be hard to lay claim to that Nobel Prize in Economics that I so badly desire!

The Economic Value of a Dataset (EvD) equals the sum of the Attributed Financial Value (FV) of a specific Use Case (Use_case_FV) that each dataset provides to that specific Use Case:

where m is the number of use cases, nj is the number of data sets per use case j, and a0 is a bias.

Using Figure 5.5 (repeated below as Figure 5.8 for simplicity), you apply this formula as such:

  • The first iteration covers the Vendor Quality use case [Use_case_FV1], where [Use_case_FV1] equals $60M. [Count Data_Set] = 3 since there are 3 data sources (data sources A, B, and C) that support the analytics for [Use_case_FV1]. Each of the 3 data sources is attributed (1 ÷ [Count Data_Set]) of the [Use_case_FV...