📊 Data & Analytics

How to Use AI in Data & Analytics

AI is now stitching together raw data, feature engineering, and visual insights in minutes rather than weeks. By automating metadata extraction, code generation, and model monitoring, teams can ship analytics products faster while keeping governance tight.

Catalog Sources with LLMs

Run an AI‑powered data catalog scan (e.g., Azure Purview with built‑in LLM tagging) across your lake or warehouse. Export the generated JSON metadata and feed it into your dbt source definitions.

Generate SQL Boilerplate

Prompt a local LLM with your schema and a business question to receive ready‑to‑run SELECT statements. Paste the result into your notebook, run a quick sanity check, and commit the query to version control.

Create Predictive Features

Feed the curated tables into an AutoML platform like H2O AutoML or Vertex AI to auto‑create feature pipelines. Schedule the resulting feature‑store update as an Airflow DAG that runs nightly.

Build AI‑Enhanced Dashboards

Connect Power BI or Looker to the feature store and model predictions, then add AI visuals such as key‑driver analysis. Publish the report and configure a 24‑hour refresh to keep insights current.

Set Up Model Monitoring

Deploy Evidently AI within Prometheus to track data drift, performance decay, and bias. Create Slack alerts for threshold breaches and schedule a monthly review cycle.

Pro Tips

Include explicit schema details in every LLM prompt; it cuts hallucinations dramatically.
Treat AI‑generated code like any other artifact: store it in Git, run linting, and run unit tests before production.
Use a lightweight embedding store (e.g., Qdrant) for internal documentation RAG, so analysts can ask unrestricted natural‑language queries without leaving the data environment.

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BI Engineer

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