Data-driven decision making has been a common task in today's big data era, from simple choices such as finding a fast way to drive home, to complex decisions on medical treatment. It is often supported by visual analytics. For various reasons (e.g., system failure, interrupted network, intentional information hiding, or bias), visual analytics for sensemaking of data involves missingness (e.g., data loss and incomplete analysis), which impacts human decisions. For example, missing data can cost a business millions of dollars, and failing to recognize key evidence can put an innocent person in jail. Being aware of missingness is critical to avoid such catastrophes. To fulfill this, as an initial step, we consider missingness in visual analytics from two aspects: data-centric and human-centric. The former emphasizes missingness in three data-related categories: data composition, data relationship, and data usage. The latter focuses on the human-perceived missingness at three levels: observed-level, inferred-level, and ignored-level. Based on them, we discuss possible roles of visualizations for handling missingness, and conclude our discussion with future research opportunities.

翻译：数据驱动决策是当今大数据时代的一项共同任务,从寻找快速回家的路等简单选择,到复杂的医疗决策,往往得到视觉分析的支持。出于各种原因(例如系统故障、网络中断、故意隐藏信息或偏向),数据感化分析涉及缺失(例如数据丢失和不完全分析),影响人类决策。例如,数据缺失可能花费数百万美元,而关键证据无法被识别,可以将无辜者送进监狱。意识到失踪是避免此类灾难的关键。要做到这一点,作为第一步,我们考虑视觉分析中的缺失,从两个方面:以数据为中心的和以人为中心的方面。前者强调三个数据相关类别中的缺失:数据构成、数据关系和数据使用。后者侧重于三个层面的人类感知缺失:观察层面、推断层面和忽视层面。基于这三个层面,我们讨论视觉化在处理失踪方面可能发挥的作用,并结束我们未来的研究机会。