On a Monday morning commute disrupted by an unexpected reroute, frustrations with the Waze app highlight the complexities and challenges involved in machine learning systems, including data inconsistency and quality issues that can affect predictive accuracy. Waze relies on user-generated data and sophisticated machine learning algorithms to optimize routes, but these systems depend heavily on well-prepared, accurately formatted datasets. The process of data preparation, which includes cleaning and formatting, is critical to successful machine learning projects, as poor data quality can lead to inaccurate predictions and business decisions. Tools like Datagran aim to streamline these processes by centralizing data and simplifying model deployment, addressing common challenges faced by companies, such as resource-intensive deployment and data security. Despite advances, many data science projects struggle to reach production due to the intricate nature of building and deploying machine learning workflows, which encompasses tasks beyond model optimization to include successful implementation and utilization of predictive insights.