Applications of data analytics, and recently machine learning, in pig farming have been investigated in literature and the results indicate great potential for data-driven decision support at various scales of the sector—from farm to the management of entire supply chains. However, there is insufficient overview of the studies conducted so far. Particularly, there is little insight into the extent of studies conducted in the context of actual business cases. In this study we conducted a systematic literature review to shed light on the state-of-the-art knowledge about data-driven decision making in the pig sector. In order to cover both classical data analysis techniques and machine learning, we used two separate search strings to search the literature. The results show that the various attributes of live pigs and slaughter data are used in analytics. Most studies focus on the occurrence and prevention of diseases, followed by DNA-related analysis and the effect of feeding strategies on growth. Among the studies we analysed, there was a large variation in herd size under study. Most studies used a selected group of pigs in an experimental environment; fewer studies used a larger number of pigs. Notably, all studies except two focussed on real-life business contexts where real-time data is used. The application of machine learning, mainly the use of random forest and neural network algorithms, took off since 2018. Current studies focus on isolated and one-off problems, and we suggest future research to consider the complexity encountered in real-life business circumstances and routine decision making through the integration of data analytics within farm information management systems.