To get started with graph data science, it's essential to investigate various use cases and become familiar with the concepts. Assembling a spearhead team of experts who can translate business needs into technical requirements is also crucial. Evaluating your "graphy" problem to identify areas where graph technology can solve interconnected information-dependent issues is vital. Next, assessing the current state of your organization or business and mapping the value of the proposed state are necessary steps. Measuring ROI, aligning stakeholders, getting project approval, conducting a proof of concept and planning for production, and connecting with the graph community will also help drive your graph project forward successfully. By following these tips and resources, you can successfully navigate the journey of implementing graph data science in your organization.