The analysis of network data is essential alongside the growth of digitalization and the Internet of Things (IoT). Managing complex connections in modern networks poses significant challenges for traditional relational databases due to their tabular structure and computational limitations. Graph databases, like Neo4j, provide flexible data management by modelling relationships with nodes and edges. This paper shows how Neo4j enhances data analysis efficiency in complex networks, enabling faster anomaly detection, pattern recognition, and predictive model development. It highlights the advantages of graph databases, including quick querying and effective handling of complex relationships, and presents real-world applications using various algorithms.