Generative AI 2.0 and Data Analytics

Generative AI is changing how organizations create, interpret, and act on information. This edited collection looks at what happens when that technology meets data analytics—not just in business decision-making, but across fields including education, healthcare, security, and connected devices.

Rather than following one application from start to finish, the book assembles contributions on the ideas, methods, and real-world questions shaping generative AI and analytics. Its range makes it a useful point of comparison for readers interested in how these tools are being studied and applied in different settings.

From generative models to business decisions

Several chapters focus directly on business data management and analytics. They examine deep-learning architectures for generative models, human–AI collaboration in strategic decisions, and benchmarking practices for evaluating generative AI in business analytics. Together, these topics bring attention to both the capabilities of the technology and the question of how its performance should be assessed.

A wider view of AI in practice

The contents also reach beyond business. Topics include AI in education, face-recognition systems, explainable and trustworthy AI, generative adversarial networks in brain-tumor imaging, cancer classification, agricultural data, and smart-home applications. This cross-sector perspective helps place analytics and generative AI within a broader landscape of research and use.

Questions about quality, security, and responsibility

Artificial data generation, security analytics, anomaly detection, ethical considerations, and future challenges all form part of the book’s stated scope. These themes matter because data-driven systems raise practical questions alongside technical ones: how useful is the data, how can performance be judged, and what issues should organizations consider as they adopt AI?

For readers exploring AI and analytics

With contributions from multiple authors, case studies, and expert perspectives, this volume may suit students, researchers, and professionals seeking an interdisciplinary introduction to current discussions around generative AI and data analytics. Readers can follow the chapters most relevant to their interests, from business applications to education, health, and other applied settings.