Graph data science is a rapidly growing field that uses graph-based analysis to explore deeper meaning in existing data, improve forecasts, and make better predictions. The increasing connectedness of data, breakthroughs in scaling graph technology, and the integration with machine learning (ML) and artificial intelligence (AI) solutions have contributed to its accelerating adoption in business. Graphs are a mathematical abstraction of complex systems, representing entities as nodes and relationships between them as lines. They can be used to answer tough questions, such as how things move through a network, what are the most influential points, and what patterns are significant. The field of graph data science encompasses three main areas: graph statistics, graph analytics, and graph-enhanced ML and AI. These areas aim to provide insights into relationships and structures in data to power predictions. With the rise of graph technology, organizations are increasingly using graph data science to gain knowledge from their data, and research has seen a significant increase in the use of graph technology in AI research over the past decade.