More than 80 zettabytes of data will be created, captured, copied, and consumed in the world this year, according to IDC's Global DataSphere. Extracting value from data is becoming increasingly difficult due to the growing amount of data and its complexity. To become useful information for a decision maker, data requires context and relevance. Context describes the relationship between one piece of data and other pieces of data, while information is relevant data in the context that informs one's decision or situation. Data-rich but context-poor worlds inevitably create a poverty of attention for individuals and organizations, leading to a sense of powerlessness and an inability to separate signal from noise. Digital enterprises must transform data into actionable business insights to create value, which depends on various factors such as data-driven leadership, the knowledge and skills of employees, business processes, and organizational culture. The technology used to store data also plays a crucial role in extracting value from data. Three common types of database management systems are relational, non-relational, and dynamic data management systems. Graph databases have gained traction recently due to their ability to reveal hidden structures, provide greater efficiency, and offer wide-ranging capabilities. Graphs can be applied to various problems and use cases, including fraud detection, anti-money laundering, and supply chain visibility. Graph technology has important synergies with AI/ML, enabling the creation of knowledge graphs that capture enterprise data as a graph and use AI/ML to "understand" the context of the data. The use of graph databases is growing in lockstep with AI, driven by their ability to provide contextual searches, make smarter recommendations, and offer richer insights. Graphs can help master data management by building relationships between shared data objects, enabling companies to track and visualize data lineages more effectively and discover issues with data quickly. Various organizations, such as Allianz, Boston Scientific, Lockheed Martin Space, and Airbnb, have used graph technologies to improve their data management and analytics capabilities. IDC recommends that enterprises understand the core pathways to converting data into information, knowledge, and business value, identify how graph databases can help alleviate challenges, choose the right graph database partner for their ecosystem, and capitalize on graphs by assessing vendors offering graph analytics platforms that seamlessly integrate with their current ecosystem.