Characterizing Automated Data Insights
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Abstract (arXiv)
Many researchers have explored tools that aim to recommend data insights to users. These tools automatically communicate a rich diversity of data insights and offer such insights for many different purposes. However, there is a lack of structured understanding concerning what researchers of these tools mean by "insight" and what tasks in the analysis workflow these tools aim to support. We conducted a systematic review of existing systems that seek to recommend data insights. Grounded in the review, we propose 12 types of automated insights and four purposes of automating insights. We further discuss the design opportunities emerged from our analysis.
Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Aug 2020.
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Citing papers per year: 2021: 2; 2022: 0; 2023: 1; 2024: 3; 2025: 4; 2026: 2.
12 papers.
- 2021Accessible Visualization via Natural Language Descriptions: A Four-Level Model of Semantic Contentcited by 38
- 2023WizMap: Scalable Interactive Visualization for Exploring Large Machine Learning Embeddingscited by 28
- 2025An LLM-Based Approach for Insight Generation in Data Analysiscited by 12
- 2025ChartInsighter: An Approach for Mitigating Hallucination in Time-series Chart Summary Generation with A Benchmark Datasetcited by 9
- 2021Multivariate Data Explanation by Jumping Emerging Patterns Visualizationcited by 4
- 2025KAHAN: Knowledge-Augmented Hierarchical Analysis and Narration for Financial Data Narrationcited by 1
- 2024Feature-Action Design Patterns for Storytelling Visualizations with Time Series Datacited by 0
- 2024PhilHumans: Benchmarking Machine Learning for Personal Healthcited by 0
- 2024"Show Me What's Wrong!": Combining Charts and Text to Guide Data Analysiscited by 0
- 2025Unveiling Interesting Insights: Monte Carlo Tree Search for Knowledge Discoverycited by 0
- 2026Behavioral Intelligence Platforms: From Event Streams to Autonomous Insight via Probabilistic Journey Graphs, Behavioral Knowledge Extraction, and Grounded Language Generationcited by 0
- 2026EvidenceLens: A Claim-Evidence Matrix for Auditing Financial Question Answeringcited by 0
- 2026EvidenceLens: A Claim-Evidence Matrix for Auditing Financial Question Answering19 Jun 2026
- 2026Behavioral Intelligence Platforms: From Event Streams to Autonomous Insight via Probabilistic Journey Graphs, Behavioral Knowledge Extraction, and Grounded Language Generation12 Mar 2026
- 2025Unveiling Interesting Insights: Monte Carlo Tree Search for Knowledge Discovery1 Oct 2025
- 2025KAHAN: Knowledge-Augmented Hierarchical Analysis and Narration for Financial Data Narration21 Sep 2025
- 2025An LLM-Based Approach for Insight Generation in Data Analysis20 Feb 2025
- 2025ChartInsighter: An Approach for Mitigating Hallucination in Time-series Chart Summary Generation with A Benchmark Dataset16 Jan 2025
- 2024"Show Me What's Wrong!": Combining Charts and Text to Guide Data Analysis1 Oct 2024
- 2024PhilHumans: Benchmarking Machine Learning for Personal Health4 May 2024
- 2024Feature-Action Design Patterns for Storytelling Visualizations with Time Series Data5 Feb 2024
- 2023WizMap: Scalable Interactive Visualization for Exploring Large Machine Learning Embeddings15 Jun 2023
- 2021Accessible Visualization via Natural Language Descriptions: A Four-Level Model of Semantic Content8 Oct 2021
- 2021Multivariate Data Explanation by Jumping Emerging Patterns Visualization21 Jun 2021