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Abstract: The Internet of Things (IoT) is proliferating on reliable and scalable collection of sensed data. Meanwhile, the growing realizations of Wireless Sensor Networks, sensing over smart devices (tablets, smartphones) and wired sensors, are all generating an exponentially increasing amount of data. The ensuing advent of Big Sensed Data (BSD) is generating challenges. First, collected data is mainly insightful to each deployed network, any 鈥渟ense-making鈥� processes to be built upon heterogeneously collected data faces significant problems, exposing challenges with varying quality, data-labelling inconsistencies, inaccuracies, time-sensitivities and different reporting granularities. Second, sensing systems inherently adopt a collect-and-report model, whereby collected data is indiscriminately pushed onto the networking infrastructure, regardless of the quality of information or its value. Not only do we face scalability issues, but establishing reliable Information Services on top of BSD is not attainable over inconsistently collected, validated and reported data. Thus, the future of Big Data is hampered by the sheer volume of reported data, its uncalibrated discrepancies, and worse by the flood of redundant and lower quality data. In a time when important IoT applications such as health informatics and emergency services require rapid and scalable access to contextual information about patients, crowds and the general public, the status quo falls significantly short.