RemixTape: Enriching Narratives about Metrics with Semantic Alignment and Contextual Recommendation

TL;DR

REMIXTAPE is a tool that helps enterprise users create structured, contextual narratives around metrics by aligning, annotating, and recommending visualizations, enhancing conversations beyond traditional dashboards.

Contribution

It introduces a hierarchical canvas for organizing metric narratives with semantic alignment, contextual externalization, and recommendation features, filling a gap in current business intelligence tools.

Findings

Users found REMIXTAPE a valuable alternative to dashboards.

Participants successfully reproduced and extended narratives.

The tool supported richer conversations about metrics.

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. We conclude with a reflection on our design choices and process, with a call to define a conceptual foundation for remixing in the context of visualization.