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Generative AI tools like @claudeai and @OpenAI's ChatGPT are no longer just buzzwords. They’re quickly becoming practical tools in the daily workflow of data professionals.

While they do not replace analytical thinking or domain knowledge, they have changed how I approach my work by enabling faster execution, clearer thinking, and more time to focus on insights.
Below are some practical ways I have applied AI in my data analysis journey.

1. Mock Data Generation
One of the biggest challenges in analytics is working without complete or accessible data. With AI, you can generate realistic mock datasets that reflect real-world scenarios. This makes it easier to test dashboards, validate logic, and experiment before real data becomes available.

2. Code Generation
From Python and SQL to Power Query (M) and DAX, AI helps me to draft code faster, especially for repetitive or complex patterns. I still review and refine everything, but development time is significantly reduced.

3. Code Debugging
Instead of staring at broken logic for hours, you can use AI to identify errors, point out inefficiencies, and suggest optimizations. It does not replace understanding the code, but it shortens the path to clarity.

4. Prototyping
AI makes it easier to move from idea to execution. I use it to prototype dashboards, data models, and analysis workflows more quickly, which helps validate ideas early and iterate efficiently.

5. Documentation
Clear documentation is essential for collaboration and long-term maintenance. With @claudeai , and @PowerBI MCP Server, I can generate explanations for code, measures, and data models, making handovers smoother and projects easier to understand.