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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

Transitioning from Business Monitoring to Business Insights

Here are the actions to transition from Phase 1: Business Monitoring to Phase 2: Business Insights:

  • Identify an organizational Strategic Business Initiative; that is, what is the organization trying to accomplish over the next 12 to 18 months from a business perspective, and what are the financial, customer, and operational impacts of that initiative.
  • Identify, validate, value, and prioritize the organization's key business and operational Decisions that the key stakeholders need to make in support of the targeted strategic business initiative. Cluster the decisions into common subject areas or Use Cases.
  • Capture, cleanse, normalize, transform, enrich, and make available the relevant data sources in a Data Lake (a data lake is a centralized data repository that allows organizations to store both structured and unstructured data at the lowest level of granularity)—at the lowest or most detailed...
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