Data analytics and generative AI (GenAI) are transformative technologies that play a critical role in modern decision-making and innovation. Data analytics enables organizations to extract actionable insights from vast amounts of structured and unstructured data, driving efficiency, improving customer experiences, and identifying trends. Generative AI, on the other hand, enhances creativity and problem-solving by producing new content, such as text, images, and designs, based on learned patterns. Together, they empower people and organizations to make data-driven decisions, automate complex processes, and unlock new opportunities for growth and innovation.
Generative AI 2.0 and Data Analytics explores the intersection between GenAI and data analytics and addresses its profound effects on industries and organizations across the globe. Highlights of the book include:
- Deep learning architectures for generative models in business data management
- Optimizing human-AI collaboration for strategic decision-making in business practises
- Benchmarking practices and evaluation metrics for generative AI in business data analytics
Not only covering the fundamental concepts and techniques of generative AI and their practical application, the book also investigates how these techniques foster innovation and improve the quality of data in various business domains. It examines a broad range of topics from artificial data generation, security analytics, anomaly detection, reinforcement management, ethical consideration, challenges, and future scenarios. The book also features expert opinions and case studies to provide practical direction and valuable insight.