Data-driven sewer asset management uses digital sewer representations to store inspection data and to support predictive maintenance planning. This approach requires asset managers to determine what inspection data they need to collect for the assessment of the asset conditions. Existing studies review sewer inspection methods based on their technical working principles but do not explicitly address what data about condition cues these methods provide. Consequently, literature lacks structured insights that help sewer asset managers link their data-needs with appropriate condition assessment methods. To make this link, we propose a data-needs based categorization of sewer inspection methods. Specifically, we relate data output of inspection methods to condition cues using the classification of hydraulic, structural, and environmental inspection domains. This shows that few methods exist to collect data about cues in structural and environmental domains. Future research should develop methods to satisfy these needs, and eventually, contribute to holistic data-driven asset management.