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The Economics of Data, Analytics, and Digital Transformation

The Economics of Data, Analytics, and Digital Transformation

By : Bill Schmarzo
4.4 (20)
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The Economics of Data, Analytics, and Digital Transformation

The Economics of Data, Analytics, and Digital Transformation

4.4 (20)
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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10
Other Books You May Enjoy
11
Index
1
Appendix A: My Most Popular Economics of Data, Analytics, and Digital Transformation Infographics

Summary

If "what" your organization seeks is to exploit the potential of data science to power your business models, then the Data Science Value Engineering Framework provides the "how" your organization can do it.

The Value Engineering Framework starts with the identification of a strategic business initiative that not only determines the sources of value but provides the framework for a laser-focus on delivering business value.

A diverse set of stakeholders is beneficial because they provide different perspectives on the key decisions upon which the data science effort seeks to optimize in support of the targeted business initiative.

The heart of the Data Science Value Engineering Framework is the collaboration with the different stakeholders to identify, validate, value, and prioritize the key decisions (use cases) that they need to make in support of the targeted business initiative.

After gaining a thorough understanding of the top priority use cases, the analytics, data, architecture, and technology decisions now have a value-centric framework within which to make those decisions (by understanding what's important AND what's not important).

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