Workflow scheduling in heterogeneous environments presents significant challenges due to complex task dependencies, particularly for data-intensive workflows where data transmission time substantially affects scheduling performance and consumes considerable bandwidth. This paper introduces a Communication-aware Duplication-based Workflow Scheduling algorithm (CDWS) that enhances scheduling efficiency through strategically duplicating critical predecessors of tasks and co-locating them on the same computational resources, followed by an elimination phase to remove redundant resource occupancy. The key idea is to reduce costly data transfers thereby improving overall makespan. Extensive simulation using synthetic data from four real-world scientific workflows shows that CDWS outperforms other approaches across key metrics such as normalized schedule length, data transmission volume and resource utilization, while maintaining low computational complexity.