Diffusion Magnetic Resonance Imaging (dMRI) allows probing the tissue microstructure in-vivo using biophysical models. In large group-level analyses, the estimation of these models is often biased due to inter- and intra-scanner variability which results in spurious group differences. Thus to correct the distribution shift in the data acquired at different sites, data harmonization is a crucial step in the dMRI analysis pipeline. Apart from the issue of bias, dMRI data also suffers from a limited signal-to-noise ratio (SNR) which also corrupts the signal, leading to spurious estimates of the underlying tissue micro-architecture. In this work, we explore the interaction of denoising with retrospective harmonization of dMRI data. Specifically, we make use of state-of-the-art denoising and harmonization methods which work directly with the 4D dMRI data of the target and reference sites. Our results show that denoising tends to improve the harmonization performance on account of the reduced variance and improved spherical harmonics-based representations of the signal. We show this using fractional anisotropy measures derived from target and reference sites in the DIAGNOSE-CTE cohort.