How is generative AI used in data analytics?

Generative AI is used to generate SQL and Python, answer natural-language questions, summarize dashboards, classify text, extract information, explain anomalies, and prepare reports.

Can generative AI analyze structured and unstructured data?

Yes. Generative AI can support analysis of structured tables as well as reviews, emails, transcripts, tickets, documents, images, and other unstructured inputs.

Does generative AI replace data analysts?

No. Generative AI can automate repetitive querying, documentation, and summarization, but data analysts remain responsible for metric definitions, validation, interpretation, and important conclusions.

What data is needed for generative AI analytics?

Businesses need current, permissioned data with clear metric definitions. A semantic model helps the generative AI system interpret business terminology correctly.

How can generative AI answers be validated?

Create a test set of real questions with expected queries and results. Compare generated answers with verified outputs, inspect the sources and filters, and track corrections made by analysts.

What is the difference between generative AI and predictive analytics?

Predictive analytics estimates future outcomes, probabilities, or classifications. Generative AI creates or transforms queries, explanations, summaries, and other analytical content.