Data Alchemy: The Genesis of Business Value

by Tirath Virdee, Doug Brown

Overview

Beginning with the key challenges that enterprises face in generating value from data, this practical and provocative book systematically outlines practical processes, frameworks and data science and artificial intelligence toolkits to enable businesses to achieve better business outcomes. Written by two leading practitioners, this playbook explores the relationship between data, customer experience and business value. The book features illustrative examples and open source codes to enhance your business knowledge and provide the necessary actions relevant to any industry, and can be successfully deployed by business executives, data science innovators and practitioners. Referencing multi-cloud Agile DevOPs, Data Science and AI, the book addresses issues from defining and sizing projects to continuous development, continuous integration and continuous deployment. It breaks down the value journey into easy to understand steps that all businesses will find engaging and invaluable in their data-driven transformation.

Product Details

ISBN-13:	9781911671213
Publisher:	LID Publishing
Publication date:	12/16/2021
Sold by:	Barnes & Noble
Format:	eBook
File size:	8 MB

About the Author

Tirath Virdee is Director of AI at Capita and a member of the Parliamentary Groups on AI and Blockchain.

Table of Contents

Acknowledgements 1

Chapter 1 Alchemy and Data Intelligence 2

What is alchemy?

Unveiling the secrets: the process of alchemy and value creation

Chapter 2 Data Intelligence and Value Exchange 10

What is data intelligence value exchange?

A brief history of value exchange

Data value exchange

Shared trust and social currency

Data democratization: data-exchange marketplaces

The emergence of shadow intelligence and the phoenix doctrine

Chapter 3 Understanding Intelligence and Intelligent Systems 26

Inspiration from nature

The emergence of intelligence

Neurons: the building blocks of natural intelligence

The neurophysiology of cognition

Manifestations of intelligence

Machine intelligence

Types of machine intelligence

Measuring intelligence

Explaining intelligence

What is consciousness?

Abstracting intelligence into a periodic table

Chapter 4 Data: Sources, Collection and Engineering 70

Data

Types of data

Data governance

Data ethics

Data sources

Data engineering

Data democratization and data exchange

The future of data

Chapter 5 Artificial Learning Systems 110

Overview of machine learning

Learning from connected neurons

Goodness of learning

Artificial neural networks

Deep learning frameworks

Spiking neural networks

AutoML and the democratization of AI

Automation and robotic process automation

Current limitations of learning

Multisensory algorithms

Learning self-improvement and evolution

Chapter 6 The AI Landscape: Present and Future 154

Explainable AI (XAI) and trust

Algorithmic ethics, security and governance

Blockchain and AI

Quantum computing

Moore's law of learning efficiency

Open AI initiatives

Future generations of AI

Society and AI

Concluding insights on the future of the AI landscape

Chapter 7 Data Alchemy Decoded: Data Intelligence and Architecting for Data Value 188

Environment and adaptation: evolution of naturally and artificially intelligent capabilities

Data alchemy's road to reality

Data alchemy and the Intelligence Foundry

Business strategy, value and data maturity: capabilities and competencies

Customer experience and service design

Business agility and continuous development cycles

The development of continuous delivery and integration practices to create business value

Data alchemy: the role of the AI Periodic Table

Data flow: multi-cloud architecture and data alchemy capability utilization

Human-machine value creation

Chapter 8 The Future of Data and Intelligence 220

Endnotes 228