It’s been fascinating to watch how fast generative AI tools have gone from being novelty tech to something many of us are actually experimenting with in real research. I’ve come across folks using them to speed up tedious data cleaning, generate synthetic data to balance datasets, or even summarize findings in more accessible ways. But with all this potential, there are also some real concerns—like how we validate results, avoid introducing bias, or stay transparent about what parts of our work were AI-assisted.
If you're using generative AI (like ChatGPT, Claude, or any open-source models) in your research or data work, I’d love to hear how it's going. What’s been useful? What’s still frustrating or unclear? And how are you thinking about the trade-offs when you introduce these tools into your process?
Generative AI can enhance data analytics by automating and accelerating key tasks. It can generate code for data processing, create visualizations, and summarize insights from large datasets. This reduces manual effort and speeds up analysis.
Examples:
- Code Generation: Tools like ChatGPT can write SQL or Python scripts to clean, transform, or query data.
- Insight Summarization: AI can read dashboards or reports and summarize trends, anomalies, and key metrics.
- Data Augmentation: Generative models can create synthetic data to fill gaps or balance datasets for training.
- Scenario Simulation: Generate hypothetical data scenarios to test models or predict outcomes.
Generative AI improves accessibility by allowing non-technical users to interact with data using natural language. It helps analysts focus on strategy and interpretation rather than routine tasks.
Dear Shamnad Mohamed Shaffi ,
A core Generative AI use case in data analytics is using large language models (LLM) to generate initial code, which accelerates the overall development lifecycle. Generative AI doesn’t replace well-structured, thoughtfully written code; but when used correctly, it helps teams work faster by generating template code or maintaining reusable repositories for common use cases.
Another practical application is converting legacy code to modern platforms, making migrations more efficient. For example, when moving from Qlik Sense reporting to Power BI, Generative AI can refactor proprietary Qlik syntax into DAX, automating the conversion of basic expressions and reducing the manual effort typically required.
https://www.analytics8.com/blog/6-use-cases-for-generative-ai/
Regards,
Shafagat
- Badges
- Science topic