Economic relationships rarely follow a simple hierarchy. A company may own shares in several other companies, be indirectly controlled through intermediate entities, have cross-shareholdings, maintain relationships with investors and management teams, and simultaneously participate in networks that extend across industries and jurisdictions.

The image above illustrates the problem. What appears to be a collection of companies and individuals is, in fact, a dense network of economic relationships. Ownership percentages, direct and indirect connections, clusters and dependencies overlap in ways that quickly become difficult to follow on a conventional two-dimensional screen.

The limits of the flat page

In a 2D representation, every relationship has to compete for the same visual space. Lines cross one another, labels overlap, and increasingly complex structures turn into what looks almost like a web of spaghetti.

The problem is not necessarily the amount of data. It is the number of relationships between the data points.

A conventional table can tell us that Company A owns 51% of Company B. Another database can tell us that Company B owns shares in Company C. But understanding what these relationships mean together requires the analyst to mentally reconstruct the network.

Research into business ecosystems has shown that increasing complexity can significantly affect decision performance and that visualization needs to be appropriately matched to the analytical task.

Adding a third dimension changes the analytical space

Three-dimensional visualization does not make the underlying economic system simpler. It does something more useful: it creates additional space in which complexity can be organized.

Instead of forcing every relationship onto the same plane, entities can be positioned beside, above, below, in front of or behind one another. Different levels of ownership can occupy different spatial layers. Clusters can be separated. Important entities can become focal points. Relationships can be followed through the network rather than reconstructed mentally.

From looking at a diagram to entering an economic system

This is where spatial computing becomes more than a visual effect. In a 3D environment, the analyst can effectively enter the network. A company is no longer merely a box on a page. It becomes a point in an economic space from which relationships can be explored in different directions. One can move closer to a specific group of companies, step back to see the overall structure, follow an ownership chain, examine indirect relationships or isolate a particular cluster.

Data2Space is designed around precisely this principle: transforming complex corporate and financial data into interactive 3D environments in which ownership structures and relationships can be explored from different perspectives.

Complexity is not eliminated — it becomes perceptible

The crucial point is that 3D does not magically simplify economic reality. The network shown above remains highly complex.

What changes is the way the human mind can interact with that complexity.

A flat diagram asks the observer to decode an enormous number of intersecting lines and labels. A spatial environment allows the observer to use position, distance, depth, movement and perspective as additional dimensions of cognition. This is especially important when economic structures contain several simultaneous layers: ownership, control, investment, management, financing, transactions and indirect dependencies.

Experimental research has also found cases in which 3D mixed-reality network visualization improved situational awareness and communication compared with 2D visualization, although the benefits depend on the task and visualization design.

The economic world is multidimensional

Perhaps the most important insight is therefore not that 3D is prettier than 2D. It is that economic reality itself is multidimensional. Companies do not exist in isolation. They form networks of ownership, control, capital, information and influence. As these networks grow, representing them on a single flat surface increasingly becomes a compromise.

Spatial computing offers a way to make these relationships perceptible rather than merely readable.

The image above is therefore not simply a complicated diagram. It is an illustration of a fundamental challenge in financial and corporate analysis: when relationships become more important than individual data points, the analytical environment itself must evolve.

Data2Space aims to make that next step possible — turning complex business graphs into spaces that analysts can enter, navigate and explore.