Why Leaders Shouldn’t Use ChatGPT for Data Analytics According to Brandon Southern
Data analytics is a crucial aspect of any business, and the right tools and strategies can make all the difference. Artificial Intelligence (AI) has revolutionized the industry, making it possible to analyze vast amounts of data quickly and efficiently. However, as with any new technology, there are risks involved in relying too heavily on AI for data analytics. Brandon Southern, the former head of analytics at eBay, Amazon, and GameStop, has warned leaders against using ChatGPT for data analytics, given that it poses a significant risk to businesses. In this article, we will explore why this is the case and what leaders should do instead.
AI has the potential to transform the way businesses analyze their data, but Brandon Southern believes it is not the right tool for the job. He highlights that while AI can help to uncover patterns and outliers in data, it cannot do the work of a human data analyst. Data analytics requires a deep understanding of the industry, the business, and the data itself. Humans are better equipped to make sense of complex data sets and to identify patterns that may not be immediately apparent. AI can only work with the data it is fed, but a human data analyst can ask questions and probe deeper to uncover valuable insights.
Another issue with relying too heavily on AI for data analytics is the risk of relying on a single source of truth. Southern points out that there are often multiple sources of truth in data analytics, and it takes a human analyst to evaluate them all and to make sense of the data. AI algorithms can only work with the data they are given, which means they may miss important insights if the data is incomplete or inaccurate.
Southern also warns that ChatGPT, in particular, is a risky tool for data analytics. ChatGPT is an AI tool that allows users to generate text by inputting prompts. However, Southern argues that this tool is not reliable for data analytics because it is not designed for that purpose. The tool is optimized for generating natural language text, but it may not be optimized for analyzing large data sets. Therefore, relying on it for data analytics could lead to inaccurate or incomplete insights.
So, what should leaders do instead? Southern recommends hiring human data analysts who can work alongside AI tools to get the best of both worlds. Human analysts can use their industry knowledge and experience to ask the right questions and to identify valuable insights that AI tools might miss. By combining the strengths of AI and human analysts, businesses can get a more complete and accurate picture of their data.
Data analytics is a vital part of any business, and AI tools have the potential to revolutionize the industry. However, as Brandon Southern argues, it is risky for leaders to rely too heavily on AI, especially for data analytics. While AI tools can help to uncover patterns and outliers in data, they cannot do the work of a human data analyst. Multiple sources of truth in data analytics require a human analysis, which AI algorithms cannot provide. ChatGPT, in particular, is not a reliable tool for data analytics and using it could lead to incomplete or inaccurate insights. Therefore, Southern recommends hiring human data analysts to work alongside AI tools to get the best of both worlds. By taking this approach, businesses can get a more complete and accurate picture of their data, leading to better decision-making.