Generative AI for Data Analytics: Value, Use Cases, and Control

Generative AI can shorten the path from raw data to business decisions, but its value depends on data quality, validation, and clear accountability. This insight explains where it is most useful in analytics, including exploratory analysis, executive reporting, forecasting support, performance management, and data preparation. It also outlines the governance foundations required for reliable use: controlled data access, human validation, transparent ownership, monitoring, and regular review. Technology and data leaders should treat generative analytics as governed decision support rather than an autonomous source of truth.