You've learned a lot about GenAI for data work. But there's a problem - many people have dangerous misconceptions about using GenAI for data analysis!
Using GenAI correctly in data work helps you avoid costly mistakes and build trust with stakeholders who rely on your insights.
Engagement Message
What GenAI data myths have you heard in your workplace?
Here's a costly misconception: "GenAI can replace data analysts completely." Wrong! GenAI is a powerful assistant, not a replacement.
You still need human expertise to ask the right questions, validate results, and understand business context that GenAI can't grasp.
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Have you seen organizations try to automate away their analytics teams?
Another dangerous myth: "GenAI always produces accurate data insights." This leads to major business disasters!
GenAI can generate plausible-looking but completely wrong SQL queries, calculations, or interpretations. Always validate outputs before making decisions.
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What would happen if your dashboards showed fake trends to executives?
The data quality trap: "GenAI can work magic with messy data." Not true! Garbage in, garbage out still applies.
GenAI might miss data quality issues that would be obvious to experienced analysts - like duplicate records, missing values, or inconsistent formats.
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How much time do you spend cleaning data before analysis?
"GenAI understands my business context automatically." Be careful! GenAI lacks domain knowledge about your industry.
It might suggest technically correct but business-inappropriate analyses. You need to provide context and validate that suggestions make sense for your specific use case.
Engagement Message
When you use GenAI for analysis, do you explain your business goals first?
