The advancement in robotic grasping and manipulation has elicited an increased research interest in the development of household robots capable of performing a plethora of complex tasks. These advancements require the shift of robotics research from a laboratory setting to dynamic and unstructured home environments. In this work, we focus on a comprehensive data collection and analysis of key attributes involved in the selection of grasping and manipulation strategies for the successful execution of kitchen tasks. An unprecedented dataset that comprises over 7 hours of high-definition videos that were analyzed to classify more than 10,000 kitchen activities annotated with 24 attributes each has been created. Machine learning techniques were employed to automate the annotation process partially by extracting grasp types, hand, and object information from the videos. The annotated dataset was analyzed using clustering algorithms to identify underlying patterns. This study also identifies key attributes and specific data that require focus during data collection based on inter-subject variability. The insights from this study can be used to improve the speed, quality, and effectiveness of data collection. It also helps identify the strategies employed by the humans for the execution of kitchen tasks and transfer the necessary skills to a robotic end-effector enabling it to complete the tasks autonomously or collaborate with humans.