The Rise of Citizen Data Science

The emergence of ChatGPT has sparked a lively debate within the field of business analytics. While some proponents see it as a solution to a wide range of business problems, others exercise caution. Nonetheless, many companies and government organizations are eager to explore ways to integrate this technology into their daily operations.

To gain a better understanding of its capabilities, I decided to experiment with ChatGPT last weekend. After all, being aware of both the possibilities and limitations of new technologies is essential in my line of business.

While I was already impressed by ChatGPT's text analytics capabilities, I wanted to test its ability to handle numerical data and mathematical problems since business analytics often involves working with numbers.

The results of the experiment were mixed. ChatGPT was able to understand and explain complex mathematical concepts and even suggested R code examples. However, it did not perform well when asked for accurate numerical answers.

The reason for this is that ChatGPT is a language model, not a computational one. It is not performing calculations in the same way that a calculator or a computer program would. It relies on its training data and algorithms to analyze and understand natural language input and then generate appropriate responses. Therefore, ChatGPT is currently not the best tool for tasks requiring numerical accuracy.

In broader terms, while AI is rapidly transforming our world, we are still far from achieving Artificial General Intelligence (AGI) that can solve any problem. Individuals are still needed to analyze and interpret complex data sets, understand the relationships and business context of the data, and ask the right questions to uncover insights.?Similarly, business domain expertise is required to verify the results obtained by ChatGPT before relying on them for important decisions.

Nonetheless, I cannot help thinking we are on the verge of a revolution comparable to the advent of the internet.

Rapidly evolving technologies such as ChatGPT make it easier for non-technical users to interact with and analyze data, whether it is textual, numerical, or graphical. This will democratize access to data insights and enable more individuals to make data-driven decisions.

The implications for organizations are significant.

ChatGPT can provide a more personalized and efficient customer experience by analyzing customer queries, understanding context, and providing relevant and accurate responses. It can also handle a large volume of customer inquiries simultaneously, significantly reducing customer support costs for organizations. By automating repetitive and routine inquiries, ChatGPT can free up customer support staff to focus on more complex issues, leading to faster resolution of issues and improved overall productivity for organizations.

Additionally, ChatGPT's ability to support multiple languages can be valuable for organizations with a global customer base, improving customer satisfaction and expanding their reach into new markets.

The rise of citizen data science is also driving the convergence of advanced analytics and automation, as businesses use analytics to optimize workflows and automate decision-making processes. With the continued advancement of AI, we can expect more automation of tasks that previously required human intervention, leading to increased efficiency and profitability across industries.

However, this convergence also poses some challenges, such as the need for new skills and potential job displacement. As organizations adopt more AI and analytics-driven automated processes, they must navigate these challenges with care, ensuring that the transition is both smooth and equitable for all stakeholders.