The drive toward smart cities alongside the rising adoption of personal sensors is leading to a torrent of sensor data. While systems exist for storing and managing sensor data, the real value of such data is the insight which can be generated from it. However there is currently no platform which enables sensor data to be taken from collection, through use in models to produce useful data products. The architecture of such a platform is a current research question in the field of Big Data and

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... art Cities. In this paper we explore five key challenges in this field and provide a response through a sensor data platform "Concinnity" which can take sensor data from collection to final product via a data repository and workflow system. This will enable rapid development of applications built on sensor data using data fusion and the integration and composition of models to form novel workflows. We summarize the key features of our approach, exploring how it enables value to be derived from sensor data efficiently.