As DRAM and other transistor-based memory technologies approach their scalability limits, alternative storage solutions like Phase-Change Memory (PCM) are gaining attention for their scalability, fast access times, and zero leakage power. However, current memory-intensive algorithms, especially those used in big data systems, often overlook PCM's endurance limitations (10^6 to 10^8 writes before degradation) and write asymmetry. Self-balancing binary search trees (BSTs), which are widely used for large-scale data management, were developed without considering PCM's unique properties, leading to potential performance degradation. This paper introduces HART, a novel hybrid addressing scheme for self-balancing BSTs, designed to optimize PCM's characteristics. By combining DFATGray code addressing for deeper nodes with linear addressing for shallower nodes, HART balances reduced bit flips during frequent rotations at deeper levels with computational simplicity at shallow levels. Experimental results on PCM-aware AVL trees demonstrate significant improvements in performance, with a reduction in bit flips leading to enhanced endurance, increased lifetime, and lower write energy and latency. Notably, these benefits are achieved without imposing substantial computational overhead, making HART an efficient solution for big data applications.