What is the place for Artificial Intelligence in data analytics? It is important to understand that AI tools such as ChatGPT, Claude, Copilot, Gemini, etc. are not trained to know everything about your specific business or industry. However, these tools can significantly speed up data analytics work. You should not, under any circumstances, give these tools data to analyse and blindly rely on the information they give you back. You still need someone who understands data analytics to monitor the process, validate the output, and make sure the results actually make business sense. Why? Take someone working in the FMCG sector, for example, in a company that produces products across several categories. Assuming the company has 10 categories of products, with an average of 10 products in each category, that gives you around 100 SKUs. But those 100 SKUs are not the only products that will appear in your sellout data. When you receive sellout data from supermarkets, the data will also contain competitor products. Your company may only operate in 10 categories, but the supermarket could have hundreds of products across many different categories. This means that before you can properly analyse the data, someone needs to understand what category each product belongs to. For example, in the toilet cleaner category, you may have products such as Harpic, Hurricane, Ace and Vim. An AI tool can help you categorise these products, but it can also categorise them incorrectly. This is where someone with an understanding of the business and the products needs to come in and monitor the output. So, what are the benefits of AI? Going back to the same categorisation problem, suppose you need Python code or an Excel formula that categorises products based on their descriptions or product codes. This is where AI becomes extremely useful. Instead of spending hours writing the code yourself, you can explain to ChatGPT, Claude or another AI tool what you want the code to do. You can give it examples of your product descriptions, explain your categorisation rules, and tell it how you want the final output to look. Once you give the AI the right instructions, it can generate the code or formula for you and help you modify it when your requirements change. The important part is that you need to know what you are asking the AI to do. If you don't understand the business, the data or the logic behind the analysis, you may get a technically correct piece of code that produces completely wrong business results. And this is why I believe AI does not eliminate the need for a data analyst. If anything, it changes what a good data analyst can do. The analyst understands the business, understands the data, defines the problem, gives AI the right instructions, checks the output, and then turns the results into something the business can actually use. AI can make the work faster. But someone still needs to know whether the answer is actually right. That is where the data analyst comes in.
