Decision-making in companies, which often involves complex analyses to weigh potential outcomes, can be streamlined using what-if scenarios and impact analyses, particularly in networked environments. While impact analysis and what-if scenarios essentially aim to predict decision outcomes, they are utilized differently across various roles and industries. Traditional database systems struggle with efficiently executing these analyses in network contexts due to limitations in handling complex graph analytics. Memgraph, a graph database platform, addresses these limitations by integrating analytics directly with storage, allowing users to perform complex impact analyses and what-if scenarios using query modules. This approach, demonstrated through a practical example in a chemical plant, enables efficient testing and optimization of network topologies without data loss or corruption by encapsulating storage and data processing within the same transaction. Memgraph supports the creation of custom analytics, allowing for modifications and experiments to be easily conducted and rolled back if necessary, paving the way for enhanced decision-making processes across various applications.