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

Digital Economics Effect #2: Economic Value of Digital Assets Grows

Sharing and reusing data and analytic modules accelerate use case time-to-value and de-risks use case implementation (see Figure 7.5).

Figure 7.5: Effect #2: Economic Value of Digital Assets Grows

Reuse of the data courtesy of the organization's data lake is one factor that can accelerate use case time-to-value and de-risk use case implementations. For example, one might find that it takes 9 months to complete the first use case because the organization needs to pay the price of creating curated data (including associated data preparation, data management, data transformation, data enrichment, and data governance) that is housed in the organization's data lake. And then maybe the second use case takes 6 months because while the organization can reuse the data in the data lake from Use Case #1, it might need to add another curated data source to support Use Case #2 (and so forth as the organization...