The text discusses how graph technology has made data modeling more accessible to a wider audience, allowing anyone with basic knowledge of graphs to create rudimentary data models. However, this increased accessibility also means that data modeling design can go wrong, and weak data models can lead to poor application performance. The author will guide readers through the basics of graph technology and provide examples to illustrate common mistakes in data modeling. A key example is a fraud detection application analyzing users' email communications, which highlights the importance of capturing relevant elements and activities in a graph data model. The author provides two iterations of the data model, each addressing specific issues, such as adding nodes for emails and relationships to track sender and recipient information. The final iteration includes tracking replies and forwards, demonstrating how a robust data model can provide valuable insights into complex problems.