Graph technology allows companies to leverage existing data stores, such as data lakes and relational databases, to gain insights into their connected data. Creating a graph does not require starting from scratch, but rather building on top of existing relationships between different types of data elements. Graph databases are highly scalable transactional and analytic databases that store data relationships as first-class entities, making it easy to express and persist relationships across many types of data elements. By assembling simple abstractions of nodes and relationships into connected structures, graph databases enable the creation of sophisticated models that map closely to a problem domain. The schema-optional nature of graph databases makes them simple and agile, allowing for easy changes or updates without requiring similar structure for every node. Graph technology has been adopted by companies like LinkedIn and Google, and is now becoming more accessible through mainstream, out-of-the-box graph technology. By using graphs to transfer knowledge of what the organization has done across different departments, companies can get more value from their big data technology and leverage connected data to deliver business insights and actionable results. A case study on Telia, a broadband provider, demonstrates how graph technology can be used to create a smart home platform that simplifies consumers' lives and provides entertainment options, powered by the Neo4j Graph Platform. Overall, graph analytics bring hidden connections in data to light, resulting in lightning-fast queries and numerous use cases across industries.