Customer adoption of graph databases is growing rapidly and has attracted many vendors and products. Graphs, as an abstraction, are a simple and intuitive way to model information about the world. Despite this, the learning curve for building a graph-based application remains steep and daunting, as it requires the use of tooling and processes that may seem unfamiliar, even to experienced data practitioners. Most importantly, there are components other than a graph database that are needed for a knowledge graph architecture. In this presentation we discuss these components, the tooling one needs to succeed in building a knowledge graph -driven application, and we present the idea of a “data value chain": data flowing from legacy sources, through various manipulation steps, to a graph representation, ready to be consumed.