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

Composable, Reusable, Continuously Learning Analytic Module Architecture

Figure 6.2 shows a state-of-the-art (as of 2020) analytic module architecture. The architecture is comprised of numerous open source components (MLflow, Seldon Core, Jupyter Notebook, Python, Spark ML, TensorFlow, and so on) built upon a Kubernetes and Docker foundation to facilitate the reuse and portability of the analytic modules across cloud hyperscalers (Amazon Web Services, Google Cloud Platform, Microsoft Azure) as well as on-premises and within embedded product environments.

Figure 6.2: Composable, Reusable Analytic Module Architecture

These composable, reusable, continuously learning analytic modules have the following capabilities:

  • Pre-defined data input definitions and data dictionary (so it knows what type of data it is ingesting, regardless of the origin of the source system)
  • Pre-defined data integration and data transformation algorithms to cleanse, align, and normalize...