The advent of precision medicine has revolutionized clinical research and practice globally by leveraging subject-specific biomarkers to avoid ineffective therapies and minimize adverse effects. In neuroimaging, there is a growing focus on identifying biomarkers with greater specificity and sensitivity. Diffusion magnetic resonance imaging (dMRI), in particular, offers unique insights into the brain's microstructural and network-level connectivity organization, making it a promising tool for biomarker discovery. However, the limited sample sizes of individual studies and the heterogeneous nature of many brain disorders present significant challenges, often leading to inconsistent or contradictory findings. While collaborative efforts have produced large-scale datasets, the field still lacks robust analytical methods to effectively address site-related variability and interpersonal heterogeneity. This thesis introduces a suite of innovative analytical tools aimed at overcoming the challenges of dMRI-based biomarker identification. These tools harness the power of big data by integrating multi-site datasets and employing advanced computational techniques such as machine learning, normative modeling, and network-based approaches. We begin by developing new harmonization methods that facilitate the integration of multi-site dMRI data, ensuring a comprehensive representation of brain disorders to improve the generalizability of dMRI markers while minimizing site-related biases. Biomarkers will be derived at multiple levels, including the regional microstructural level and the whole-brain network level. This multidimensional characterization aims to capture the complexity and heterogeneity of dMRI-based patterns in brain disorders. We apply these methods to autism datasets, exploring how the derived biomarkers can enhance understanding and facilitate subtyping of the disorder. Ultimately, this work advances biomarker research, particularly for heterogeneous conditions, and contributes to more robust discoveries through collaborative research in the era of team science and big data.