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The Economics of Data, Analytics, and Digital Transformation

You're reading from   The Economics of Data, Analytics, and Digital Transformation The theorems, laws, and empowerments to guide your organization's digital transformation

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Product type Paperback
Published in Nov 2020
Publisher Packt
ISBN-13 9781800561410
Length 260 pages
Edition 1st Edition
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Author (1):
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Bill Schmarzo Bill Schmarzo
Author Profile Icon Bill Schmarzo
Bill Schmarzo
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Table of Contents (14) Chapters Close

Preface 1. The CEO Mandate: Become Value‑driven, Not Data-driven 2. Value Engineering: The Secret Sauce for Data Science Success FREE CHAPTER 3. A Review of Basic Economic Concepts 4. University of San Francisco Economic Value of Data Research Paper 5. The Economic Value of Data Theorems 6. The Economics of Artificial Intelligence 7. The Schmarzo Economic Digital Asset Valuation Theorem 8. The 8 Laws of Digital Transformation 9. Creating a Culture of Innovation Through Empowerment 10. Other Books You May Enjoy
11. Index
Appendix A: My Most Popular Economics of Data, Analytics, and Digital Transformation Infographics
1. Appendix B: The Economics of Data, Analytics, and Digital Transformation Cheat Sheet

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...

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