Authors: Neh Sharma
Abstract: GenAI has altered how businesses think about data and how they use it to make decisions. After 2020, better large language models (LLMs), retrieval-augmented generation (RAG), and agentic pipelines have made it possible for analytics systems to go from only reporting on data to coming up with fresh insights. But this change makes people very worried about fairness, openness, and data privacy, especially since models affect how businesses make decisions and how people from different backgrounds work together. This paper looks at new developments in architecture and talks about the ongoing ethical and evaluative problems that come up in generative analytics. A single Generative Analytics System Framework (GASF) is proposed, integrating architectural, evaluative, and ethical dimensions to achieve a balance between analytical efficacy and accountability. A simulation demonstrates that various departments and demographic user groups utilise LLM-based analytics in distinct manners. The findings indicate that user skill and contextual diversity influence factual accuracy, delay, and trust in distinct ways. This means we need to make systems that are fair and keep people's information safe. The report concludes with a proposal for research aimed at developing generative analytics ecosystems that are ethical, comprehensible, and adaptable to diverse populations.
