The adoption of Digital Twins in the healthcare sector is expanding, with applications ranging from treatment personalization to advanced diagnostics, therapeutic scenario simulation, and precision medicine. In this study, we propose the design of a Digital Twin for the gut microbiome, laying the foundation for future clinical applications and experimental validations. The gut microbiome is a complex ecosystem, influenced by a variety of endogenous and exogenous factors. Its disruption is linked to numerous pathological conditions. Modelling the gut microbiome through a Digital Twin requires the integration of heterogeneous data to enable continuous monitoring of critical parameters, such as therapy, crisis episodes, and diet. In this ecosystem, a mobile application could play a key role, acting both as a sensor and an actuator for the Digital Twin. On the one hand, it collects real-time data on factors that influence microbiota balance; on the other hand, it provides personalized recommendations and facilitates dynamic adjustment of nutritional and therapeutic strategies. In our proposed design, we focus on the Data Layer, where data flows are managed and pre-processed before being sent to the digital entity. Data analytics is fundamental in transforming raw data from the physical entity into actionable insights, thereby enabling the Digital Twin functions. Through the analysis of this data, significant patterns can be identified, allowing for the prediction and timely intervention in dysbiosis conditions in the gut microbiome.