Emily Clarke

Is ChatGPT Reliable for Data Analysis?

In early 2023, OpenAI's ChatGPT burst onto the Internet, seemingly out of nowhere. The generative pre-trained transformer (GPT) technology used in ChatGPT, itself a large language model (LLM), blew the minds of users as the chatbot seemed to possess near-human intelligence in conversation, data manipulation and more.

Although ChatGPT seems quite powerful and appears to have several uses for business owners and tech enthusiasts, questions have arisen regarding the LLM's reliability. After all, entrusting a machine to handle sensitive data and make crucial decisions isn't something to take lightly, especially when the results can affect a company's bottom line.

ChatGPT Admits Its Weaknesses

To figure out if ChatGPT or other AI data analysis tools are reliable, one way to know is simply to ask. Most AI data analysis tools, including ChatGPT, will readily admit that they make mistakes. When experienced data analysts check the work of AI tools, they may find mistakes or false statements.

ChatGPT Makes Things Up

Aside from making mistakes, sometimes ChatGPT simply makes things up. The issue here isn't one of malice. Instead, it's simply a matter of how these tools work.

An LLM basically predicts what it thinks comes next in a response. This works similarly to how predictive text works when using a search engine or sending an email or text message. By predicting what the LLM thinks you are looking for, it may produce results that sound convincing, but they're actually completely fabricated.

Should You Risk It?

Because ChatGPT isn't perfect and AI tools don't possess true intelligence, most experts caution against relying only on ChatGPT for data analysis. As tools, LLMs can be very useful, but they can't guarantee accuracy. As a result, you need to recognize that there is risk involved in relying only on AI for data analysis.

Instead, consider using AI tools for an initial pass of your data and then have a trained human review the results. This can help to catch mistakes or call attention to results that seem out of place. A human can then go in and analyze questionable data manually.

Author Resource:-

Emily Clarke writes about the best data catalog tools and data analysis softwares. You can find her thoughts at data analysis blog.