Absent Data (Why Gaps, Uncertainty, and Invisible Data Matter)

Making missing, incomplete, and invisible data meaningful through visualization

Absent Data: Why Gaps, Uncertainty, and Invisible Data Matter reframes a persistent challenge for data visualization practitioners. Rather than treating incomplete datasets, absence of data, and unexplained gaps as obstacles to clean away, Tiziana Alocci, Associate Lecturer at The University of the Arts London who has collaborated with The National Gallery and The British Library, presents methodologies for making absence itself meaningful and visible within data-driven narratives.

Drawing on over a decade of experience transforming fragmented and nontraditional datasets into visual narratives for major cultural institutions and global brands, Alocci blends personal project experience with practical insight. The book offers creative approaches to noticing uncertainty and ambiguity and working with data sets that do not exist yet, by moving beyond standard imputation techniques to design visualizations where what is missing carries as much weight as what is present.

Readers will also find:

• Methodologies for dealing with data visualizations that represent incomplete, fragmented, or nontraditional data in meaningful and deliberate ways
• Approaches to data-driven storytelling that treat uncertainty and ambiguity as signals rather than noise to eliminate
• Creative frameworks for communicating what is not present in a dataset through hands-on activities
• Practical insight drawn from a wide selection of case study from art, technology, science, and music.

Data visualization professionals, analysts, designers, and researchers who regularly encounter incomplete datasets will find different approaches for treating gaps and uncertainty as meaningful signals. This book serves practitioners ready to move beyond standard data-collection routines and develop visual approaches that communicate absence with the same rigor applied to present data.