Knowledge graphs have emerged as a crucial technology in recent years, leveraging machine learning and artificial intelligence to make data more reliable, robust, trustworthy, and explainable. They organize data with context and relationships, driving intelligence into data via an "organizing principle" that adds meaning to the data. Knowledge graphs are used across various use cases, including data governance and compliance, risk management, augmented MDM, X-360, AML, root cause analysis, and many others, providing data assurance and radical visibility. They also play a key role in contextual AI, making AI more trustworthy, accurate, and allowing for better reasoning. Furthermore, knowledge graphs are transforming businesses by bridging data silos, building a data fabric, accelerating machine learning and AI adoption, and providing a blueprint for digital twins. The book "Knowledge Graphs: Data in Context for Responsive Businesses" offers a practical guide for business leaders, data scientists, and developers to harness the power of knowledge graphs.