The continuous increase of data volumes poses several challenges to established infrastructures in terms of resource management and expenses. One of the most important challenges is the energy-efficient enactment of data operations in the context of data-intensive applications. Computing, generating and exchanging growing volumes of data are costly operations, both in terms of time and energy. In the late literature, different types of compression mechanisms emerge as a new way to reduce time spent on data-related operations, but the overall energy cost has not been studied. Based on current advances and benefits of compression techniques, we propose a model that leverages nonlossy compression and identifies situations where compression presents an interest from an energy reduction perspective. The proposed model considers sender, receiver, communications costs over various types of files and available bandwidth. This strategy allows us to improve both time and energy required for communications by taking advantage of idle times and power states. Evaluation is performed over HPC, Big Data and datacenter scenarios. Results show significant energy savings for all types of file while avoiding counter performances, resulting in a strong incentive to actively leverage non-lossy compression using our model.