Intensive Workflows are composed of large number of complex tasks and require a large amount of datasets that are located in different Storage Computing Servers (SC). The massive data movement between these servers can cause intensive data access, and then high communication and data movement cost. In this paper, a data placement strategy based on the Formal Concept Analysis approach (E-DPSIW-FCA ) is proposed aiming to minimize the data movement between these SC, reduce the consumed energy, and the workflow execution cost. FCA allows to group the maximum of datasets and tasks in a minimum number of SC as close as possible to each other and this by the navigating through the lattice. E-DPSIW-FCA considers the original datasets and the different communication levels of a data center. The simulation results show that our approach can effectively improve energy consumption for both data communication and computing time of the overall workflow, in addition to reducing its cost.