AI for Data and Analytics

How Data and Analytics Teams Are Using AI

Data teams exist to answer questions. The gap between “I have a question” and “here is the answer” is filled with SQL queries, data cleaning, schema lookups, visualization choices, and narrative writing. AI compresses every step of that pipeline.

SQL Generation

AI translates natural language questions into SQL queries, handling joins, aggregations, window functions, and CTEs. Analysts describe what they want (“show me monthly revenue by product line for the last 12 months, excluding refunds”), and AI generates the query. This is especially valuable for analysts working across multiple database schemas or those who need complex queries quickly.

Dashboard and Report Specification

AI drafts dashboard requirements documents: which metrics to show, how to visualize them, what filters to include, and how to define each KPI. This bridges the gap between stakeholders who know what they want to see and engineers who need a precise spec to build it.

Data Cleaning and Preparation

AI writes data cleaning scripts that handle missing values, standardize formats, detect outliers, and merge datasets. Analysts describe the problems in the data, and AI generates the transformation code. This turns a 4-hour data prep session into a 30-minute review of AI-generated code.

Insight Reporting

AI transforms query results into narrative summaries for non-technical stakeholders. Instead of handing the CMO a spreadsheet, the analyst delivers a written summary with key findings, trends, anomalies, and recommendations. AI generates the first draft; the analyst adds context and judgment.