Pérez-Messina, IgnacioAngelini, MarcoCeneda, DavideTominski, ChristianMiksch, SilviaAigner, WolfgangAndrienko, NataliaWang, Bei2025-05-262025-05-2620251467-8659https://doi.org/10.1111/cgf.70115https://diglib.eg.org/handle/10.1111/cgf70115Data size and complexity in Visual Analytics (VA) pose significant challenges for VA systems and VA users. Two recent developments address these challenges: progressive VA (PVA) and guidance for VA (GVA). Both share the goal of supporting the analysis flow. PVA primarily considers the system perspective and incrementally generates partial results during long computations to avoid an unresponsive VA system. GVA is primarily concerned with the user perspective and strives to mitigate knowledge gaps during VA activities to prevent the analysis from stalling. Although PVA and GVA share the same goal, it has not yet been studied how PVA and GVA can join forces to achieve it. Our paper investigates this in detail. We structure our research around two questions: How can guidance enhance PVA and how can progressiveness enhance GVA? This leads to two main themes: Guidance for Progressiveness (G4P) and Progressiveness for Guidance (P4G). By exploring both themes, we arrive at a conceptual model of how progressiveness and guidance can work together. We illustrate the practical value of our theoretical considerations in two case studies of G4P and P4G.Attribution 4.0 International LicenseCCS Concepts: Human-centered computing→Visualization theory, concepts and paradigms; Visualization design and evaluation methodsHuman centered computing→Visualization theoryconcepts and paradigmsVisualization design and evaluation methods10.1111/cgf.7011512 pages