This paper explores existing electrical disaggregation workflows and how they can be augmented with context awareness through datasets. The goal of energy disaggregation is to educate consumers on their energy usage. Additional benefits in automation, security, and energy auditing can be realized through disaggregation. The use of statistical analysis provides specific device consumption information that can be actioned to conserve energy in a directed and methodical manner. The current landscape of disaggregation is a complex workflow involving algorithms that detect, analyze and reveal consumption patterns. Disaggregation workflows involve the acquisition of energy signals for an entire building, refining readings, detecting events, extracting features, and classification. Each step in the workflow impacts the accuracy in which individual devices are detected. Disaggregation workflows may incorporate device usage and weather patterns to improve accuracy, but crowdsourcing signatures and the incorporation of datasets that allow for context awareness are strategies yet to be adopted.