Visual Analytics Challenges & Trends
in the Age of AI

Nikos Bikakis Panos K. Chrysanthis Guoliang Li George Papastefanatos Lingyun Yu

Hellenic Mediterranean University & Archimedes/Athena RC · University of Pittsburgh · Tsinghua University · Athena RC · Xi’an Jiaotong-Liverpool University

ACM SIGMOD Record, 2025
Figure: Importance of the 2020 challenges today. The survey asked participants whether each challenge from the 2020 report is still important. Scalability & efficiency has the strongest agreement, followed by visual analysis for ML applications.

Survey snapshot

Classic challenges remain important, while AI introduces new concerns around LLMs, trust, explanations, and broader user access.

94% of participants Scalability & Efficiency Challenge
Emerging topics Human-in-the-loop processing, human-centered ML, and progressive analytics tie at the top.

Takeaway: the survey highlights the broad acceptance of the importance of all the challenges identified in the community report four years ago. It argues that older issues such as scalability and visual analysis for ML remain broadly accepted, while AI introduces a new layer of concerns around LLM use, trust, explanations, and access for non-expert users.

Overview

This report summarizes a BigVis 2024 community survey on challenges, emerging topics, and opportunities in human-data interaction and visual analytics in the AI era. The survey involved experts from databases, information visualization, HCI, industry, and academia, and revisited the challenge agenda from the 2020 community report.

The central message is that many classic challenges remain relevant, especially scalability, efficiency, user understanding, guidance, and evaluation. At the same time, AI has reshaped the agenda: LLM-based visualization and analytics, fairness, trustworthiness, explainability, non-expert users, progressive analysis, and immersive visualization now stand out as prominent research directions.

Key Insights

2020 challenges persist

All ten challenges from the 2020 report still receive at least 50% agreement or strong agreement on their current importance.

Scalability remains foundational

Scalability and efficiency are still the strongest carry-over challenge, with nearly universal agreement among survey participants.

AI changes the research vocabulary

LLMs, fairness, trustworthiness, and explainability appear as new agenda-setting themes that were absent from the older challenge list.

Human-centered ML is central

Human-in-the-loop processing and interactive, human-centered ML are among the most frequently selected emerging topics.

Broader user base matters

Visualization for non-expert users is identified as a key current challenge, connecting usability, guidance, explanation, and trust.

Progressive and immersive systems grow

Progressive analytics, high-dimensional/stream data, and immersive visualization appear as newer directions in the AI-era agenda.

Challenges & Research Directions

Exploit LLMs in visualization and analytics

The most frequently mentioned current challenge is how to use LLMs effectively, safely, and meaningfully in visual analytics workflows.

Human-in-the-loop processing

Keep users actively involved in analysis, correction, interpretation, and decision-making rather than treating AI as a fully autonomous analyst.

Fairness and trustworthiness

AI-era analytics must address bias, uncertainty, provenance, and reliability across data, models, interfaces, and explanations.

Interactive and human-centered ML

Design ML systems around user goals, feedback, inspection, and iterative exploration.

Non-expert users

Broader access requires systems that guide analysis, clarify uncertainty, and explain results in understandable ways.

Progressive analytics

Incremental, responsive analysis can reduce latency and support sensemaking over large, high-dimensional, or streaming data.

High-dimensional and stream data

Large, dynamic, and complex data remain hard to analyze interactively, especially when AI models add cost and opacity.

Immersive visualization

AR/VR and spatial interfaces offer new ways to explore data, but require careful human-centered design and evaluation.

Survey Figures

Participants and 2020 challenges. The report shows a survey population concentrated in information visualization and database communities, and confirms that scalability and efficiency remain the most strongly supported 2020 challenge.
Emerging topics. Human-in-the-loop processing, interactive and human-centered ML, and progressive analytics are the most frequently selected emerging topics.

Human-AI Collaboration [2026 Report]

The recent 2026 community report Human-Data Interaction, Exploration, and Visualization in the AI Era discusses key challenges and opportunities for Human-AI Collaboration and Human-Data Interaction systems. It frames AI-era human-centered systems as a tightly coupled ecosystem of interaction, AI models, and multimodal data systems, highlighting the need for human-centered, trustworthy, and scalable analytics.

Human-AI collaboration. The figure summarizes the companion paper's view of AI-era Human-centered systems. [png] [pdf]
From paper: Jean-Daniel Fekete, Yifan Hu, Dominik Moritz, Arnab Nandi, Senjuti Basu Roy, Eugene Wu, Nikos Bikakis, George Papastefanatos, Panos K. Chrysanthis, Guoliang Li, Lingyun Yu: Human-Data Interaction, Exploration, and Visualization in the AI Era: Challenges and Opportunities. CoRR abs/2603.05542 (2026).

BibTeX

@article{bikakis2025visualAnalyticsChallengesAI,
  title   = {Visual Analytics Challenges and Trends in the Age of AI: The BigVis Community Perspective},
  author  = {Bikakis, Nikos and Chrysanthis, Panos K. and Li, Guoliang and Papastefanatos, George and Yu, Lingyun},
  journal = {ACM SIGMOD Record},
  year    = {2025}
}