What ChatGPT does for data automation – and what it doesn't
Natural language processors like ChatGPT have become a popular tool for automating data analytics. However, these processors are still in their early stages and should be treated much like "interns" who are not yet ready to replace high-level analysts. ChatGPT, a popular natural language model, can help in automating the process of transforming data into business intelligence.
The traditional process of data analysis is akin to searching for information on Google. This process can become more difficult and time-consuming when analysts have to go through hundreds of data dashboards or spreadsheets to find specific answers. Natural language models like ChatGPT are ideal for open-ended questions and multi-dimensional data analysis.
Newer language models don't just transform information into charts or bullet points but also introduce "writing a narrative" for users, which grants businesses the ability to make informed decisions. It is important to note that despite these models' capabilities, there is still a risk of false information and incorrect data replication, especially when pulling information from external sources.
To mitigate this risk, users must understand how to interact with natural language processors and the limitations of these models. Users must comply with the formatting of queries to draw out correct data and confirm that the information presented to them is sourced from an accurate place. The narrative, which is the centerpiece of interaction with an analyst, should be treated as a conversation where follow-up questions are necessary.
Although natural language models are growing in intelligence very quickly, businesses should not rely on them to tell them what to do. Companies must understand that these processors are still in their early stages and should be treated as an analyst in training.
