Research on blockchain data analytics focuses on extracting meaningful insights from the vast amounts of transactional and operational data generated within blockchain networks. By applying techniques such as graph analysis, machine learning, and statistical modeling, researchers can uncover patterns in user behavior, detect fraudulent or anomalous activities, and evaluate network performance and efficiency. Blockchain analytics also supports regulatory compliance, anti-money laundering (AML) monitoring, and risk assessment by providing transparent and traceable records of transactions. Additionally, predictive analytics and visualization tools help stakeholders understand network dynamics, optimize resource allocation, and improve decision-making in decentralized ecosystems. Overall, blockchain data analytics enhances transparency, security, and operational intelligence across diverse applications of distributed ledger technology.