Data Analytics and AI

Data analytics and artificial intelligence are often discussed as if they were interchangeable—or as if either one could solve every organizational problem on its own. Data Analytics and AI, edited by Jay Liebowitz, takes a more grounded approach: it explores where these fields meet, what their methods can do, and how they may be applied across business, policy, healthcare, and other settings.

Connecting analytics, AI, and data science

The collection begins by clarifying the relationships among data science, artificial intelligence, and autonomy. From there, contributors consider shared technologies, current developments, and the role of machine intelligence in managerial decision-making. This framing gives readers a way to think beyond the latest tool or headline and consider how analytical capabilities fit into larger decisions.

From deep learning to language data

Several chapters focus on methods and applications, including deep learning in industry and natural language processing in data analytics. The book also examines measurement questions around the interaction of AI and analytics, bringing attention to how these concepts are understood as well as how they are used.

Applications across organizations and society

The examples range widely: Chinese AI policy, strategic risk management in smart-city development, predicting missed appointments in an academic dental clinic, machine learning in cognitive neuroimaging, and a digital transformation project at ABB. Taken together, these cases show how analytics and AI enter different environments—and why context matters when evaluating their potential.

A practical question at the center

Running through the book is a question of organizational value: how can powerful analytical techniques be translated into useful solutions? Its closing chapter proposes an AI-informed analytics cycle for unstructured data, bringing the discussion back to how data work can be organized and applied.

Who may find it useful

This edited volume may interest readers studying or working in data analytics, AI, data science, business decision-making, and digital transformation. Its mix of conceptual discussion, technical topics, policy perspectives, and case studies offers several entry points for readers who want to understand the connections between these fields.