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

2024-06-05

arXiv.org

AI Summary

Enterprise KPIs are often presented in isolation, lacking the contextual grounding and narrative structure necessary to support collaborative interpretation. To address this, we propose the “Metric Narrative” framework, which introduces a hierarchical, KPI-centric interactive canvas integrating time-series visualizations with contextualized textual annotations. The framework ensures semantic alignment between charts and text, enables cross-modal annotation, and supports context-aware collaborative recommendation. Innovatively, it adopts a “remixable” visualization paradigm that facilitates dynamic narrative construction and evolution. Guided by qualitative interviews with data practitioners, we developed a recommendation algorithm that jointly models metric semantics and usage patterns. Evaluation with six enterprise data experts demonstrates that our approach significantly improves narrative reproducibility and extensibility compared to conventional dashboards and slide-based reports, establishing it as a novel medium for collaborative KPI sensemaking.

Technology Category

• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Natural Language Processing: Language Grounding & Multi-modal NLP
• Machine Learning: Learning Preferences or Rankings

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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.

Problem

• Enhancing metric narratives with context
• Semantic alignment of charts and text
• Recommending complementary visualizations for metrics

Innovation

• Semantic alignment of charts
• Contextual chart recommendations
• Hierarchical narrative construction