Data products (DP) are considered a key enabler of data-driven innovation. However, suitable methodologies and tools supporting DP design are still scarce. The emerging body of practitioner literature mostly focuses on analytics based products and their technical design and architecture but lacks a more comprehensive product perspective on data. To address this gap, we propose the Data Product Canvas (DPC) as a visual inquiry tool that supports cross-functional teams in understanding, designing, and analyzing DPs. The DPC was developed in an iterative design science process involving focus groups with 15 global companies and demonstrations for selected DPs. Building on the core ideas of the Business Model Canvas, the DPC outlines the critical elements for designing DPs around three key themes: desirability from the customer perspective, feasibility from the technical perspective and viability from an economic perspective. The DPC instantiates the design principles for visual inquiry tools and comprises a conceptual model, shared visualization, and directions for use. The DPC is the first step towards a systematic approach and shared language to design DPs in ways that technical experts and business users understand.