Machine learning and predictive analytics are more accessible to the public and nonprofit space now more than ever. Local government and nonprofits strive to leverage these new technologies to improve outcomes, performance, and operations. While a willingness to collaborate and connect on common goals though a shared understanding of data needs serves to build towards a stronger culture around data, the complexities around defining critical terms in dynamic environments pose significant hurdles to be able to scale any machine learning for large cross-departmental initiatives in service of the public. I will share examples from my professional work in NYC government, and probe into challenges with data-driven processes, consensus-based motivations and outcomes.