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Bringing Data into the Conversation: Adapting Content from Business Intelligence Dashboards for Threaded Collaboration Platforms

Hyeok Kim, Arjun Srinivasan, Matthew Brehmer

TL;DR

A set of six strategies for adapting content from business intelligence dashboards into appropriate formats for sharing on collaboration platforms, formats that are referred to as dashboard snapshots are introduced, serving to reduce the workload of data professionals.

Abstract

To enable data-driven decision-making across organizations, data professionals need to share insights with their colleagues in context-appropriate communication channels. Many of their colleagues rely on data but are not themselves analysts; furthermore, their colleagues are reluctant or unable to use dedicated analytical applications or dashboards, and they expect communication to take place within threaded collaboration platforms such as Slack or Microsoft Teams. In this paper, we introduce a set of six strategies for adapting content from business intelligence (BI) dashboards into appropriate formats for sharing on collaboration platforms, formats that we refer to as dashboard snapshots. Informed by prior studies of enterprise communication around data, these strategies go beyond redesigning or restyling by considering varying levels of data literacy across an organization, introducing affordances for self-service question-answering, and anticipating the post-sharing lifecycle of data artifacts. These strategies involve the use of templates that are matched to common communicative intents, serving to reduce the workload of data professionals. We contribute a formal representation of these strategies and demonstrate their applicability in a comprehensive enterprise communication scenario featuring multiple stakeholders that unfolds over the span of months.

Bringing Data into the Conversation: Adapting Content from Business Intelligence Dashboards for Threaded Collaboration Platforms

TL;DR

A set of six strategies for adapting content from business intelligence dashboards into appropriate formats for sharing on collaboration platforms, formats that are referred to as dashboard snapshots are introduced, serving to reduce the workload of data professionals.

Abstract

To enable data-driven decision-making across organizations, data professionals need to share insights with their colleagues in context-appropriate communication channels. Many of their colleagues rely on data but are not themselves analysts; furthermore, their colleagues are reluctant or unable to use dedicated analytical applications or dashboards, and they expect communication to take place within threaded collaboration platforms such as Slack or Microsoft Teams. In this paper, we introduce a set of six strategies for adapting content from business intelligence (BI) dashboards into appropriate formats for sharing on collaboration platforms, formats that we refer to as dashboard snapshots. Informed by prior studies of enterprise communication around data, these strategies go beyond redesigning or restyling by considering varying levels of data literacy across an organization, introducing affordances for self-service question-answering, and anticipating the post-sharing lifecycle of data artifacts. These strategies involve the use of templates that are matched to common communicative intents, serving to reduce the workload of data professionals. We contribute a formal representation of these strategies and demonstrate their applicability in a comprehensive enterprise communication scenario featuring multiple stakeholders that unfolds over the span of months.
Paper Structure (6 sections, 5 figures)

This paper contains 6 sections, 5 figures.

Table of Contents

  1. Introduction
  2. Background and Prior Work
  3. Design Considerations
  4. Six Strategies for Adapting Dashboard Content
  5. Demonstration Scenario
  6. Discussion, Limitations, and Future Work

Figures (5)

  • Figure 1: The relationships between snapshots (A), components (B), templates (C), template specifications (D), and dashboard selections (E). Components load data, measures, breakdowns, filters, and worksheet from the original dashboard selection, which are propagated to a template specification for a template-based component.
  • Figure 2: Example components made from dashboard selections and their formal representations in YAML.
  • Figure 3: An example snapshot composed of the components in \ref{['fig:scenario']} and its formal representation in YAML.
  • Figure 4: Example templates for simple breakdown (A), a breakdown with a goal (B), and a time-series with a threshold (C).
  • Figure 5: The Philo interface for specifying dashboard snapshots: (A) Component creator and (B) Snapshot composer. Formal properties are annotated next to the corresponding interface elements.