Toward Systematic Considerations of Missingness in Visual Analytics

TL;DR

This paper explores the importance of recognizing and addressing missing data in visual analytics to improve decision-making, analyzing it from data-centric and human-centric perspectives.

Contribution

It introduces a systematic framework for considering missingness in visual analytics, highlighting data-related and human-perceived aspects, and discusses visualization roles and future research directions.

Findings

Identifies three data-related categories of missingness: composition, relationship, and usage.

Defines three levels of human-perceived missingness: observed, inferred, ignored.

Proposes visualization strategies to handle missing data in visual analytics.

Abstract

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.