A new year is a great time to reflect on the past and look ahead to the future in the graph database space. According to Neo4j colleagues Amy Hodler, Michael Hunger, and Amit Chaudhry, key predictions for 2020 include increased adoption of graph-feature engineering to boost machine learning accuracy, commercial applications of graph embeddings beyond image analysis, and a shift towards considering graphs as a standard format for adding context to machine learning. The data supply chain will play an increasingly important role in ethical and responsible AI, with frameworks like the EU Ethics Guidelines being developed. Developers will face challenges such as managing complex cloud systems, ensuring accountability and responsibility around algorithmic biases, and adopting new features and languages in popular runtimes. Graph analytics will grow in importance, and GQL (Graph Query Language) is expected to emerge as a standard for graph database vendors, leading to increased interoperability, portability, and skill-set availability.