The need for self-serve analytics has not been fully addressed by current business intelligence (BI) tools, which have struggled to keep up with the increasing volumes of data and the growing complexity of analysis required. The divide between technical and non-technical users has created a natural barrier, with stakeholders often relying on others to dig deeper into data, leading to bottlenecks and tool sprawl. Large language models (LLMs) are being touted as a solution to bridge this gap, but they do not directly address the issue; instead, they offer a crucial piece of the puzzle. The key is for platforms to allow creators of data products to produce data apps and dashboards that cater to both data practitioners and business stakeholders, enabling more natural interaction with data through various modalities such as SQL, Python, chat-like experiences, or drag-and-drop exploration.