The value of Data2Space is not simply that complex information can be displayed in three dimensions. Its deeper value lies in the possibility of making relationships visible in a form that corresponds more closely to how people actually explore complex structures.

Recent research in graph visualisation provides strong evidence for this idea. In their 2025 study “Show Me Your Best Side,” Lucas Joos, Gavin J. Mooney, Maximilian T. Fischer, Daniel A. Keim, Falk Schreiber, Helen C. Purchase and Karsten Klein investigated how people choose viewpoints when analysing three-dimensional graphs. Their experiment involved 23 participants who examined 36 different graphs in a virtual-reality environment and selected the perspectives they considered most and least useful. The authors describe this as a systematic investigation into user-preferred viewpoints for 3D graph visualisations.

One of the most important implications for Data2Space is that there is no universally “best” position from which to look at a complex network. The researchers found that users respond to several perceptual characteristics simultaneously. In particular, Stress, edge crossings, the Gabriel Ratio, edge-node overlap and the newly introduced Isometric Viewpoint Deviation proved relevant in explaining viewpoint preferences.

This is highly relevant when Data2Space is used to visualise corporate, ownership, financial or other relationship networks. A complex participation structure is not merely a collection of entities and connections. Its meaning depends on which relationships become visible, which structures remain hidden and whether the observer can construct a coherent mental model of the network.

The research indicates that people often prioritise the visibility of the overall structure even when this means accepting some additional local overlap or crossing. Participants frequently searched first for a perspective that made the global configuration understandable and only afterwards refined the view to reduce local visual problems.

For Data2Space, this suggests an important principle: visualisation should not be reduced to the elimination of every crossing or overlap. The objective is to reveal meaning.

This distinction matters particularly in financial analysis. A corporate group may contain numerous subsidiaries, shareholders, intermediate holding companies, cross-shareholdings and other relationships. A conventional two-dimensional chart can quickly become a dense collection of intersecting lines. The problem is not necessarily that the data are too complex. The problem may be that the representation does not provide enough spatial freedom to reveal the structure.

Data2Space addresses precisely this dimension. By placing relationships into an immersive three-dimensional environment, the observer can explore the structure rather than merely looking at a fixed diagram. The research shows why this interaction can be more than a technological novelty. Participants reported that stereoscopic 3D could reveal structures that would be difficult to interpret in two dimensions.

The research also provides an important warning. Three-dimensional visualisation does not automatically make information easier to understand. Viewpoint selection becomes critical because depth, occlusion and perspective can either reveal or conceal relationships. The authors therefore emphasise the need for methods that help users identify advantageous perspectives instead of leaving viewpoint selection entirely to ad-hoc exploration.

This points directly toward a powerful direction for Data2Space: intelligent visualisation. Instead of simply converting a network into 3D, a sophisticated service can evaluate different viewpoints and help the user find perspectives that expose the most relevant structures.

In this sense, the research provides an important scientific foundation for the Data2Space concept. The purpose of immersive analytics is not to make data look impressive. It is to make complex relationships cognitively accessible.

For analysts examining ownership structures, investment relationships, corporate networks or other interconnected systems, that distinction can make all the difference. Data2Space turns the question from “How can this network be drawn?” into a much more valuable question: “From which perspective can this network be understood?”

That is where three-dimensional visualisation becomes analytical rather than merely visual.