Disaggregated data offers a microscopic view of challenges faced by businesses, enabling pinpointing of inefficiencies, uncovering trends, and making critical decisions in areas like data quality and cloud cost optimization. By analyzing data at its most granular level, organizations can identify patterns and variances within specific subgroups, revealing disparities that inform targeted interventions. Disaggregated data has various applications across sectors such as public health, education, economic development, environmental studies, and policymaking, but it also poses challenges like data privacy and ethical considerations, issues of data quality and accuracy, resource limitations, and potential misuse. Best practices for handling disaggregated data include collection techniques, analysis tools, data security, and stakeholder engagement, which ensure that the collected data is relevant, useful, and secure. Tools like statistical software, visualization software, coding tools, and platforms specifically designed to address disaggregated data challenges are available to support organizations in harnessing its power while mitigating associated risks.