So Far, VR-Enabled Data Visuailzation is Nonsense

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. Instead, they tend to spout a lot of misinformation about visual perception and cognition. Those who have actually taken the time to study visual perception and cognition could take each of these claims apart with ease. VR has the cool factor going for it and vendors are capitalizing on this fact.

VR certainly has its applications. Data visualization just doesn’t seem to be one of them and it’s unlikely that this will change. If it does at some point in the future, I’ll gladly embrace it. Navigating physical reality in a virtual, computer-generated manner can indeed be useful. I recently visited the beautiful medieval town of Cesky Krumlov in the Czech Republic near the Austrian border. I could have relied solely on photographs and descriptions in a guide book, but walking in the midst of that old city, experiencing it directly with my own senses, enhanced the experience. Had I not been able to visit it personally, a VR tour of Cesky Krumlov could have provided a richer experience than photographs and words alone. Data visualizations, however, display abstract data, not physical reality, such as a city. There is no advantage that we have discovered so far, either perceptual or cognitive, to flying around inside a VR version of the kind of abstract data that we display in data visualizations. We can see and make sense of the data more effectively using 2-D or, on rare occasions, 3-D displays projected onto a flat plane (e.g., a screen) without donning a VR headset.

I was prompted to write this blog post by a recent article titled “Data visualization in mixed reality can unlock big data’s potential,” by Amir Bozorgzahed. This fellow is the cofounder and CEO of Virtuleap and host of the Global WebXR Hackathon, which puts his interest in perspective. The article quotes several software executives who have VR products to sell, and the claims that they make are misleading. They take advantage of the gullibility of people who are already susceptible to the allure of technological hyperbole that goes by such names as VR, Big Data, AI, and self-service analytics. They market their VR-enabled data visualization tools as techno-magical—capable of turning anyone into a skilled data analyst without an ounce of training, except in the use of their VR tools.

Let’s examine a few of the claims made in the article, beginning with the following:

The tech enables not only enterprises and organizations, but anyone, to use their spatial intelligence to spot patterns and make connections that breakthrough the tangled clutter of big data in a way that has been out of reach even with traditional 2D analytics.

“Anyone” can use their “spatial intelligence to spot patterns and make connections.” Wow, this is truly magical and downright absurd. While it is true that spatial perception is built into our brains, it is not true that we can use this ability to make sense of abstract data without having developed an array of data sensemaking skills.

The self-service claims of VR data visualization can get even more outlandish. Consider the following excerpt from the article, which describes WebVR’s “forthcoming seismic-upgrade:”

In fact, their platform wasn’t designed to cater to just highly-trained data scientists, but for anyone with a stake in the game. In the not so distant future, I picture the average Joe or Jane regularly making use of their spatial intelligence to slice and dice big data of any kind, because everyone has the basic skill-sets required to play Sherlock Holmes in mixed reality. All they need to get started is access to big data sets, which I also foresee as being more prevalent not too long from now.

Amazing! I suppose it’s true that everyone can “play” Sherlock Holmes, but playing at it is quite different from sleuthing with skill.

Here’s an example of a VR data visualization that was included in the article:

First of all, you don’t need VR to view data in this manner. At this moment you’re viewing this example on a screen or printed page. You do need VR hardware and software, however, to virtually place yourself in the middle of a 3-D scatter plot and fly around in it, but this wouldn’t make the data more accessible, perceptible, or understandable. Viewing the data laid out in front of us makes it easier to find and make sense of the meaningful patterns that exist within.

The spatial perception that is built into the human brain can indeed be leveraged, using data visualization, to make sense of data. It is not true, however, that it can do so independent of a host of other hard-won skills. Here’s another similar excerpt from the article:

Pattern recognition is an inherent talent that we all possess; the evolutionary edge that sets us apart from the animal kingdom. So, it’s not so much that immersive data visualization unlocks big data but, rather, that it allows us to interact with big data in a way that is natural for us.

This is quite misleading. Other animals also have tremendously good pattern recognition abilities built into their brains, in many cases much better than ours. What sets humans apart in regards to pattern recognition is our ability to reason about patterns in abstract ways, sometimes called patternicity. This is both a blessing and a curse, however, for we can and often do see patterns that are entirely meaningless. We are prolific meaning generators, but separating valid from illusory meanings requires a rich set of data sensemaking skills. No tool, including and perhaps especially VR, will replace the need for these skills.

Here’s another visualization that’s featured in the article:

The caption describes this as “a volatile blockchain market.” What is the claim?

The Bitcoin blockchain in particular pushes the limits of traditional data visualization technology, as its support for transactions involving multiple payers and multiple payees and high transactional volume would create an incomprehensible jumble of overlapping points on any two-dimensional viewer.

Let’s think about this for a moment. If we view a forest from the outside, it appears as a “jumble” of trees. Due to occlusion, we can’t see each of the trees. If we walk into that forest, we can examine individual trees, but we lose sight of the forest. This is a fundamental problem that we often face when trying to visualize a large and complex data set. We typically attempt to resolve this challenge by finding ways to visualize subsets of data while simultaneously viewing how those subsets fit into the larger context of the whole. A traditional data visualization approach to this problem involves the use of concurrent “focus+context” displays to keep from getting lost in the forest while focusing on the trees. Nothing about VR helps us resolve this challenge. In fact, compared to a screen-based display, VR just makes it easier to get lost in the forest.

Here’s the ultimate expression of nonsense that I encountered at the end of Bozorgzahed’s article:

We have reached a point in time where much of the vast digital landscape of data can be now rendered into visual expressions that, paired up with artificial intelligence, can be readily deciphered and understood by anyone with simply the interest to mine big data. And all this because the underlying tech has become advanced enough to finally align with how we visually process the world.

Notice the abundant sprinkling of buzzwords in this final bit of marketing. When you combine data visualization with VR, AI, and Big Data you have a magic trick as impressive as anything that David Copperfield could pull off on a Las Vegas stage, but one that is just as much an illusion.

I will continue saying what I have said before too many times to count: data sensemaking requires skills that must be learned. No tool will replace the need for these skills. It’s time that we accept the unpopular truth that data sensemaking requires a great deal of training and effort. There are no magic bullets, including VR.

2 Comments on “So Far, VR-Enabled Data Visuailzation is Nonsense”

By Willie Northway. November 6th, 2018 at 6:29 pm

I completely agree with you that abstract data is always harder to comprehend when visualized in 3D… which is the point of VR.

However, there are various “data” visualizations which could be really valuable to be displayed in 3D. Some ideas that come to mind are:
– physical environments which would be hard to get to (e.g. the surface of Mars)
– physical environments which would be dangerous (e.g. inside the reactor in Chernobyl)
– physical environments which don’t exist yet or ever (e.g. a building that you’re proposing to be built)
– physical environments which are at a different scale (e.g. wandering through skyscrapers, or inside bodily organs or manipulating atoms and molecules)
– physical environments where you’re trying to highlight items (e.g. the nearest subway entrance or bathroom)

I could also see the feasibility of a multi-screen computer workstation being displayed in a VR world, with multiple 2D data visualizations displayed inside that world.

By Stephen Few. November 7th, 2018 at 8:51 am


“Data visualiation” (a.k.a., “information visualization”), by definition, involves abstract data. Visualizations of physical reality are typically called “scientific visualizations.” As I mentioned in the article, VR visualizations of physical reality are indeed useful at times.

Contrary to your position, I think a “multi-screen computer workstation being displayed in a VR world, with multiple 2D data visualizations” would be silly and ineffective. There is no good reason that I can think of to simulate multiple 2-D screens in a virtual 3-D environment. If you think otherwise, please explain why the visual metaphor of multiple computer screens would be the most effective way to display multiple data visualizations.

Leave a Reply