Visual analytics – conceptual overview and research front

Visual analytics (VA) is currently a research field where human judgment is central to extracting information from big data. Through an interactive and iterative process, analytical reasoning is integrated with data analysis and visual representation. As data volumes become increasingly larger, more complex, and more dynamic, VA provides an ability to combine human insight with automated processes, creating actionable understanding. VA is not about visualizing data, but about understanding it and enabling actionable insights.

This report provides an overview of the VA research front. The field still relies on strong interdisciplinary collaboration but has gradually gained a more independent position. The main challenges today concern scalability, interaction, infrastructure, and evaluation. There is a particular emphasis on the need to further develop methods that actively integrate humans throughout the analysis process.

Publications in VA have increased significantly since the early 2000s in many applied areas such as health, finance, and cybersecurity. In the defense sector, there are concrete examples of use, but the total number of studies is still limited.

The development of methods to handle big data is a central research track. This includes both new algorithms and visualization techniques, as well as redesign of existing methods to meet requirements for scalability and interactivity. Modern VA research also includes web-based solutions, immersive technologies such as virtual reality (VR) and augmented reality (AR), and combinations with machine learning and artificial intelligence (AI).