Urban administrations have invested heavily in data infrastructure over the past decade, yet frontline decision-making has not kept pace. The core problem is not data scarcity but a design failure: most urban analytics systems optimize for visibility rather than action. This paper introduces Urban Infoanalytics, a decision-centered approach that starts from specific administrative decisions and works backward to define data, models, and outputs. The framework treats governance constraints — including time pressure, legal authority, and organizational structure — as primary design inputs, and incorporates safeguards for algorithmic accountability, auditability, and vendor dependency. It also proposes the Translator: a standing institutional role responsible for aligning analytical systems with administrative workflows over time and maintaining a continuous bridge between technical systems and frontline decision-making. An illustrative case in housing code enforcement shows how a simple automated weekly ranking of high-risk properties can outperform complex GIS dashboards in operational settings where decisions must be made quickly and repeatedly. The case highlights a central claim: the form of data delivery matters as much as its accuracy.