Towards a framework for realizing actionable insight from complex data: A machine-augmented cognition approach to data exploration, information synthesis, and knowledge actualization

摘要

Research in data analytics has emphasized technical advancements and largely undermined the importance of user intuition and knowledge in the analytics process. This study proposed a new data analytics framework for designing and evaluating data analytics systems that harness both the flexibility of human intuition and the rigor of machine-driven analytics techniques. This study contributes to a systematic understanding of the analyst's workflow, behaviors, and information needs. It also produces a set of empirically-tested design principles that can be used to effectively support these human elements during the data analytics process.