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

A Quick Primer on Deep Learning, Reinforcement Learning, and Artificial Intelligence


While it is unlikely that you will ever be asked to build your own neural network or RL algorithm, it is important to understand how these advanced analytics work (at a high level) and what can be done with them from a value creation perspective. These are the tools of a modern-day value creation alchemist.

DL is a set of algorithms that analyze massive datasets using a multi-layered neural network structure, where each layer is comprised of numerous nodes, to train and learn to recognize and codify patterns, trends, and relationships buried in the data… without human intervention. Common applications of DL include image recognition, natural language processing, disease detection, and facial recognition (see Figure 6.4).

Figure 6.4: How Deep Learning Works

There are two key capabilities that underpin the continuous learning nature of...