Could Conversational AI-Driven Data Analytics Finally Solve the Data Democratization Riddle?

A data leader is sitting in a modern office, analyzing revenue data on her computer, and struggling to derive insights from it. This scene highlights the challenges of data democratization, making data accessible to everyone in an organization, regardless of their technical skills. Despite decades of efforts, data analysis remains the realm of data analysts, data scientists, and business analysts. However, the release of ChatGPT and its plugin Code Interpreter has opened up new possibilities for conversational AI-driven data analytics. This technology allows users to interact with data using natural language, without needing to write code. While some predict that conversational AI-driven data analytics will replace the need for data analysts, others believe it will bridge the technical gap between data and its democratization. Currently, tools like Power BI Q&A and Snowflake Cortex Analyst are incorporating conversational AI interfaces, but they have limitations. Power BI Q&A is limited to querying specific datasets, while Cortex Analyst can query entire databases but requires a fully vetted semantic layer. The biggest challenges to data democracy are not technological but rather prerequisites like a strong data infrastructure, data literacy, data quality, and data governance. Many companies still struggle with these aspects, and data literacy is a significant issue, with only 34% of companies offering data literacy training. A successful data democratization effort requires a multi-pronged approach, including a strong data infrastructure, an organizational data-first mindset, and appropriate technologies. Conversational AI-driven data analytics can play a significant role in this effort, particularly in the hands of business analysts who have domain knowledge and data literacy.