Last time we explored how GenAI works under the hood. Now let's examine what GenAI can actually do well in data analytics today, where it still struggles, and what breakthroughs are coming soon.
Current GenAI in analytics is impressive but uneven - amazing in some areas, frustrating in others.
Engagement Message
What's your most satisfying experience with GenAI for data work been so far?
Today's GenAI excels at simple, structured data tasks. Asking it to clean datasets, generate basic charts, or write SQL queries works remarkably well.
These systems handle clear data requests with standard formats with impressive accuracy - often saving hours on routine data preparation and visualization.
Engagement Message
Which basic data tasks do you find most tedious to do manually?
GenAI also shines in democratizing analytics. People without technical backgrounds can now ask questions in plain English and get meaningful insights from their data.
Natural language queries help business users explore datasets independently, while automated report generation enables faster decision-making across organizations.
Engagement Message
How might GenAI help someone you know access data insights more easily?
However, GenAI still struggles significantly with complex analytical context and domain expertise. It often misunderstands business nuances, overlooks critical data relationships, and fails with sophisticated statistical analyses.
Industry-specific metrics, regulatory requirements, and custom business logic can drastically reduce accuracy, sometimes producing misleading conclusions.
Engagement Message
Have you noticed GenAI missing important business context in your data work?
Data privacy remains a major limitation. Most GenAI requires cloud processing, meaning your sensitive business data travels to external servers for analysis.
This creates security risks and compliance concerns, especially for confidential financial, customer, or proprietary data that shouldn't leave your organization.
