Data teams are facing significant challenges due to the sheer volume of requests they receive, with a staggering 97% of data teams being strapped for capacity. The main issue lies in the friction associated with accessing and managing data platforms, which takes up a huge chunk of their time. Additionally, data workers spend over 60% of their working hours dealing with data requests, while the role of the data team has evolved to function as an internal product manager, delivering solutions at scale to stakeholders. However, this comes with its own set of challenges, including the tradeoff between customization and capacity, and the need for end-to-end products that cater to individual needs. Furthermore, the friction in executing data requests due to overly complex data stacks, lack of data quality, and sheer volume of requests is a significant burden on data teams. To address this, solutions such as platform consolidation, top-down leadership, and AI-powered no-code tools are often recommended, but these may not fully meet the needs of real-life scenarios. Instead, investing in agility, using agile formats, keeping platforms connected, and leveraging AI in a controlled manner can help ease the burden on data teams and foster an environment of innovation.