Data Visualisation in VR: first impressions

Virtual reality opens the door for new manners of exploration of data, especially for multidimensional and complex datasets.

Andrea Bravo
5 min readDec 15, 2018


We are living in the era of Big Data. With the increasing digitalisation of processes large amounts of data are generated every day. However, the human brain is not capable of processing effectively large amounts of data to extract the relevant information¹.

Data visualisation has been used for several decades as a tool to amplify cognition and favour decision-support². With the appearance of new technologies such as virtual reality (VR), novel ways of interaction appear going beyond the traditional desktop dynamics. New hardware capabilities enable multisensory input and output devices, besides a more natural way of interacting with the data provided³.

However, there is a large debate in the data visualisation community on the advantages/disadvantages of using VR as a platform to display data visualisation. For example here are some industry points of view:

  • Forbes contributor Bernard Marr describes: “By immersing the user in a digitally created space with a 360-degree field of vision and simulated movement in three dimensions, it should be possible to greatly increase the bandwidth of data available to our brains.”
  • Visual Business Intelligence blog comments: “Few data technologies are subject to more hype these days than VR-enabled data visualization. I have never seen a single example that adds value and therefore makes sense. Those who promote it don’t base their claims on actual evidence that it works.”
  • Google News Lab also explores the field of journalism by building VR data visualisations: “VR is at an exciting stage. It can help take journalism in an entirely new direction. For the News Lab data team, we wanted to try to provide journalists with another tool in the data toolkit. After all, helping readers and users feel like, in Cronkite’s words, “You Are There” is what journalism is all about.”
  • The Register informs: “For now, no one expects a single big player to steam in and create the default VR or AR data visualisation platform. Neither are the big players giving much away, beyond tightly controlled and gadget press-friendly news announcements.”

These are some voices of the industry panorama of data visualisation. From my perspective, having been dealing with data visualisation for the last months on a daily basis, the initial skepticism and the critical perspective over data visualisation in VR have slowly turned towards a more comprehensive approach of the affordances that VR brings to the field of data visualisation. Let me explain to you what I have been learning along the way.

What I learned after months of research on VR data visualisation

Basically, what I will point out in this article regards the existing controversy between 2D and 3D data visualisations.

At the start of my research I was highly critical about data visualisation in VR. With a background in visual design, I was used to desktop representations of data, which are on a general basis created in two dimensions. How could adding a third dimension increase the comprehension of the data? My initial thoughts related adding a third dimension to an increase in the complexity of the representation. Besides, being immersed in a VR headset implies first and effort in learning to use the hardware and software, until getting accustomed to the virtual environment.

However, it turns out that after reading a substantial amount of academic literature on data visualisation and VR, especially on the field of immersive analytics⁴, I realised that there were clear advantages in using VR for exploration of complex datasets.

VR enables to create representations of highly complex data and visualise them with an added coordinate: “z” or depth. This gives numerous advantages in exploring complex representations with large amounts of data, such as the case presented by Project Neo. In addition, projects with geo-located data also benefit from VR data visualisations. The Place Viewer project is a good example showcasing the evolution of pixels over time. Yet, this type of visualisation is not common (in comparison to a bar chart or a scatter plot) and are used in specific areas with a huge amount of data.

The Place Viewer project

Therefore, we should not be focusing on comparing the two types of representations (2D and 3D) because both are valid. What we need to take into account is whether adding the complexity of VR hardware for a visualisation will increase the understanding of the visualisation for the target users, rather than looking for the hype or “wow” effect of VR technology. There are some cases in which VR data visualisation will be of great use for our purposes, while in certain other cases referring to 2D visualisations is perfectly OK and makes more sense for the users of our products.

I would like to make the comparison with the architecture field, in which very different types of representations are used to convey different parts of the designing and constructing process of the building. For example, sketches are used for the initial inspiration of the building, while technical drawings are used to describe the components of the design, and finally renders are used to convey the final look of the building for the client. The different types of representations are used for different purposes and audiences.

Architectural drawing. By Norbuilding architects
Render image. By Daniel DiNuzzo

VR offers the possibility to interact with the whole body in the data-experience, in comparison to being sitting in a chair in front of a desktop computer. Advances in hardware capabilities will greatly benefit VR data visualisations to enable a more intuitive and natural approach to interact with datasets. The implications of using the body to explore datasets are worth to being investigated. We will address this topic in a future article.

I am a PhD researcher on data visualisation in VR at Engineering Systems Division, Technical University of Denmark.

Disclaimer: this post represents my own point of view and is not intended to reflect the opinions of the organizations I work for and/or represent.


  1. D. A. Keim, F. Mansmann, J. Schneidewind, and H. Ziegler, “Challenges in {Visual} {Data} {Analysis},” Tenth {International} {Conference} {Information} {Visualization}, 2006. {IV} 2006, pp. 9–16, 2006.
  2. C. Ware, “Chapter 1 — Foundations for an Applied Science of Data Visualization,” Inf. Vis., pp. 1–30, 2013.
  3. W. Büschel, P. Reipschläger, R. Langner, and R. Dachselt, “Investigating the Use of Spatial Interaction for 3D Data Visualization on Mobile Devices,” in Proceedings of the Interactive Surfaces and Spaces on ZZZ — ISS ’17, 2017, vol. 17, pp. 62–71.
  4. T. Chandler et al., “Immersive Analytics,” in 2015 Big Data Visual Analytics, BDVA 2015, 2015, pp. 1–8.



Andrea Bravo

Visual UX Designer & Researcher with a background in Cognitive Science and Virtual Reality. @eandreabravo