While hybrid simulation is the combined use of the simulation methods of SD, ABS and DES, hybrid modelling can refer to the use of simulation in combination with other analytical methods. Bell et al. (2019) define a hybrid modelling approach as one in which simulation models are extended, utilising methodologies, standards, tools and software from the field of computer science, applied computing, industrial engineering and data science. This could include the use of simulation in combination with other analytical methods such as data envelopment analysis (DEA) (Greasley, 2005). When examining the relationship between simulation and analytical methods, such as machine learning, we can consider the use of a model-driven approach using simulation compared to data-driven analysis usually associated with the use of large data sets termed big data and computer programs running algorithms to process that data. Understanding the different approaches to the prediction of model-driven and data-driven approaches will help us to understand what each method can and cannot do. This chapter covers model-driven and data-driven perspectives for the analysis of business processes, including the possibility of using these approaches in combination (i.e. hybrid modelling). This chapter also covers the use of simulation to facilitate a digital twin.