AbstractBackground and aimThe main challenge of assessing diastolic function is the balance between clinical utility, in the sense of usability and time‐efficiency, and overall applicability, in the sense of precision for the patient under investigation. In this review, we aim to explore the challenges of integrating data in the assessment of diastolic function and discuss the perspectives of a more comprehensive data integration approach.MethodsReview of traditional and novel approaches regarding data integration in the assessment of diastolic function.ResultsComprehensive data integration can lead to improved understanding of disease phenotypes and better relation of these phenotypes to underlying pathophysiological processes—which may help affirm diagnostic reasoning, guide treatment options, and reduce limitations related to previously unaddressed confounders. The optimal assessment of diastolic function should ideally integrate all relevant clinical information with all available structural and functional whole cardiac cycle echocardiographic data—envisioning a personalized approach to patient care, a high‐reaching future goal in medicine.ConclusionComplete data integration seems to be a long‐lasting goal, the way forward in diastology, and machine learning seems to be one of the tools suited for the challenge. With perpetual evidence that traditional approaches to complex problems may not the optimal solution, there is room for a steady and cautious, and inherently very exciting paradigm shift toward novel diagnostic tools and workflows to reach a more personalized, comprehensive, and integrated assessment of cardiac function.