Interoperability constraints on data analytics in city governments: Insights from two U.S. municipalities

City governments increasingly rely on data and analytics to address complex policy problems, building responsive capacity through information collection and management. This paper examines how interoperability challenges compromise data analytics practices in city governments and how interoperability theory can inform improvements in analytics delivery. Drawing on interoperability research, we develop a conceptual framework linking data analytics enablement and delivery to semantic, technical, and organizational constraints. Empirical evidence comes from semi–structured interviews with 26 data analytics practitioners in two U.S. cities–Syracuse, New York, and Kansas City, Missouri. Findings show that gaps in data architecture and shared understanding, legacy systems that reinforce path-dependent processes, and organizational resource constraints and process rigidity significantly hinder analytics efforts. At the same time, collaborative arrangements that foster shared understanding emerge as a key mechanism for addressing knowledge gaps and improving interorganizational alignment. The paper contributes an expanded interoperability framework that integrates data analytics research and highlights practical leverage points for practitioners in city governments.