The article examines the hidden costs associated with maintaining a modern data stack, highlighting challenges such as training overhead, iteration lag, and the persistence of outdated ETL jobs and reports. It discusses the potential financial impact of training staff to use complex tools, the inefficiencies caused by slow report updates, and the expenses incurred from running unnecessary queries. Additionally, the article addresses issues like the "bus factor," where knowledge silos result from expert departures, and the complications of managing multiple data sources or tiered account systems. It offers mitigation strategies such as simplifying tool interfaces, hosting informal training sessions, documenting data, and encouraging distributed data team models to enhance domain expertise and minimize reliance on tiered pricing models. The piece underscores the importance of thoughtful tool selection and strategic management to optimize costs and improve the efficacy of data operations.