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Many applications involving machine-to-machine (M2M) communications are characterized by the large amount of data to transport. To support these M2M applications, we argue in this article that instead of focusing on serving individual machines with better quality, one should focus on solutions that can better serve the data itself. To substantiate, we consider the application of data gathering from a set of machines that communicate directly to an aggregator. Since the aggregator has limited radio resources, the problem arises as to how the resource can be effectively utilized for supporting such an application. We investigate “data-centric” resource allocation that aims to maximize information entropy of data collected through selecting the subset of machines to transmit, determining the amount of resources to allocate, and scheduling the sequence of transmissions. We present two instantiations of the problems …