Given that it is estimated that refrigeration alone accounts for nearly 9% worldwide consumption of energy, the case for exploiting readily available underlying parametric refrigeration case data to achieve a significant energy savings impact, is great. This paper, through a series of examples, explores the value (economically and technically) of acquiring, harvesting, and applying big data aggregated statistical approaches to this large data set, to help the domain experts to deepen their knowledge of actual refrigeration case behaviour, not only to achieve significant energy savings through policy and process changes, but also by utilizing the savings potential of realtime case anomaly detection through the development of engineering driven key performance indicators (KPI's). Furthermore, it is presented that use of such data driven analytics have the potential to also positively impact on the direction of future maintenance support models, supplementing traditional preventative/reactive methods with more cost effective data analytics predictive maintenance support models, supplementing traditional preventative/reactive methods with more cost effective data analytics predictive maintenance approaches.