REMIXTAPE: Enriching Narratives about Metrics with Semantic Alignment and Contextual Recommendation
Year: 2025
Type: conference-paper
Abstract: The temporal dynamics of quantitative metrics or key performance indicators (KPIs) are central to conversations in enterprise organizations. Recently, major business intelligence providers have introduced new infrastructure for defining, sharing, and monitoring metric values. However, these values are often presented in isolation and appropriate context is seldom externalized. In this design study, we present REMIXTAPE, an application for constructing structured narratives around metrics. With design imperatives grounded in prior work and a formative interview study, REMIXTAPE provides a hierarchical canvas for collecting and coordinating sequences of line chart representations of metrics, along with the ability to externalize situational context around them. REMIXTAPE includes affordances to semantically align and annotate juxtaposed charts and text, as well as recommendations of complementary charts based on metrics already present on the canvas. We evaluated REMIXTAPE in a study in which six enterprise data professionals reproduced and extended partial narratives. They appreciated REMIXTAPE as a novel alternative to dashboards, galleries, and slide presentations for supporting conversations about metrics. Finally, we reflect on REMIXTAPE’s usability and potential utility, and conclude with a call to define a conceptual foundation for remixing in the context of visualization.
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4411337808