Demonstrating the “Data Analysis” GPT
More than a month ago I wrote this blogpost, about how one can build a chatbot with ChatGPT and Steamlit for querying and visualising their own datasets.
Last week OpenAI announced GPTs, apps based on their Large Language Models (LLMs). Among these apps, the most interesting to me was the “Data Analysis” GPT, available to pro subscribers. It allows users to upload their own datasets, query them and plot the data using human language without knowledge of analytics and plotting tools, offering an end-to-end data analysis experience to technical and non-technical.
If you are not a pro subscriber, here is a 5-minute video, where I demonstrate some key elements of the GPT. I use a publicly available dataset from Our World In Data.
Moral of the story; GenAI is a moving with enormous speed. Ideas are rapidly transformed into applications, reshaping how we interact with data and the world. I have witnessed this this acceleration first hand within my organisation and beyond.
Decisiveness, the ability to pivot, and the efficient allocation of resources are crucial elements for data teams and organizations to gain a competitive advantage in the GenAI race, both internally and across different organizations.
