To provide users with a more useful experience from big data, it's essential to apply techniques at different stages of the data funnel. Filtering in Neo4j reduces noise by removing as much data as possible early on, utilizing Cypher queries and KeyLines' integration for effective visual filtering. Aggregating data cleanses duplicates and errors, while also considering data modeling to remove unnecessary clutter. By creating a clever visual model with a small proportion of the original data, users can simplify complex patterns and gain insights. Finally, using filters, combining, and pruning techniques declutters the chart, and automated graph layouts help uncover patterns and anomalies, making it easier for users to find answers in their data.