Schmarzo The Economics of Data, Analytics, and Digital Transformation

The theorems, laws, and empowerments to guide your organization's digital transformation
1. Auflage 2020
ISBN: 978-1-80056-913-3
Verlag: De Gruyter
Format: EPUB
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 252 Seiten

No detailed description available for "The Economics of Data, Analytics, and Digital Transformation".

Autoren/Hrsg.

Schmarzo, Bill

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Mensch-Maschine-Interaktion Informationsvisualisierung
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Datenbankdesign & Datenbanktheorie
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Automatische Datenerfassung, Datenanalyse
• Mathematik | Informatik EDV | Informatik Informatik Mensch-Maschine-Interaktion Informationsarchitektur

Inhaltsverzeichnis

Table of Contents - The CEO Mandate: Become Value-driven, Not Data-driven
- Value Engineering: The Secret Sauce for Data Science Success
- A Review of Basic Economic Concepts
- University of San Francisco Economic Value of Data Research Paper
- The Economic Value of Data Theorems
- The Economics of Artificial Intelligence
- The Schmarzo Economic Digital Asset Valuation Theorem
- The 8 Laws of Digital Transformation
- Creating a Culture of Innovation Through Empowerment
- Appendix A: My Most Popular Economics of Data, Analytics, and Digital Transformation Infographics
- Appendix B: The Economics of Data, Analytics, and Digital Transformation Cheat Sheet

Über Autor(innen)

Schmarzo Bill:
Bill Schmarzo, The Dean of Big Data is a University of San Francisco School of Management Executive Fellow and an Honorary Professor at the School of Business and Economics at the National University of Ireland-Galway where he teaches and mentors students in his courses “Big Data MBA” and “Thinking Like a Data Scientist". He is the author of Big Data: Understanding How Data Powers Big Business, Big Data MBA: Driving Business Strategies with Data Science, and The Art of Thinking Like a Data Scientist. He has written countless whitepapers, articles and blogs, and given keynote presentations and university lectures on the topics of data science, artificial intelligence/machine learning, data economics, design thinking and team empowerment.