Due to the impending threat of climate change, as well as omnipresent pressures to remain competitive in the global market, manufacturers are motivated to reduce the energy and resource consumption of their operations. Analysis of manufacturing data can enable large efficiency gains, but before the data can be analyzed, it must be acquired and processed. This descriptive literature review assesses existing research on data acquisition and pre-processing in the context of improving manufacturing energy and resource efficiency. A number of insights were derived from the selected literature, based on a specific set of questions. Discrete manufacturing has received more attention than process manufacturing, when it comes to data acquisition and pre-processing methodology. Typically only one or two variables are measured, namely electricity consumption and material flow. Data is most often used for real-time monitoring or for historical analysis, to find opportunities for improving energy efficiency. However, acquisition of meaningful real-time data at a high granularity remains a challenge. There seems to be a lack of robust data acquisition and pre-processing methodologies that are designed and proven applicable across machine, process and plant levels within a factory.