One of AR’s many areas of applicability is data visualization. The same can be said for VR, the common value being the ability to get a multi-dimensional understanding of complex data. In short, think of how a Z-axis adds dimension and meaning to traditional X/Y charts.
Of course, the Z-axis is nothing new, as standard tools like PowerPoint have offered a simulated sense of three dimensions on a 2D screen. But what immersive tech adds to the party is the ability to experience the relationship between data points with six degrees of freedom.
That’s all a fancy way of saying you can walk amongst the data to gain greater dimensional understanding. And with complex data sets, that deeper insight can translate to lightbulb moments in uncovering correlations between things like revenue and myriad hidden variables.
Flow Immersive has been innovating in this area for a while, which still offers ample opportunity as AR and VR gain enterprise penetration. This was also the subject of Microsoft’s Mike Pell’s presentation at AWE USA, and the focus of this week’s XR Talks (video and takeaways below).
Flow Immersive Comes to Magic Leap
The New Oil
As The Economist famously proclaimed, “data is the new oil.” This has also been a rallying cry of the big data movement of the past decade. A corollary to that movement is the internet of things, where sensors surround us, each producing reams of analytics, metrics and biometrics.
But all that data is a blessing and a curse….data overload is useless without insight. This brings us back to the above thread, in that we need ways to parse and draw meaning from the deluge of data that gushes from all those sensors. Otherwise, it’s just a bunch of noise.
A central principle that this all rests on is human cognition. Humans can generally understand complex data values more effectively through structures rather than numbers. And when arrayed in 3D, those structures and their spatial relationships can unlock deeper understanding.
Enterprise AR: Best Practices & Case Studies, Volume II
Beyond greater dimension, Pell points to other variables that engender a deeper understanding of complex data. Among other things, these include speed and size. For example, scale can’t be fully understood by looking at two bars on a bar chart and their relative values.
However, if those values are personified by walking amongst them, deeper understanding can be gained. In other words, immersing oneself in the data to become the same size as the smaller of two values can instill an understanding of scale in literally looking up at the larger value.
This principle of walking amongst the data with six degrees of freedom goes beyond a sense of scale. Rates of growth can be conveyed by speed rather than the slope of a trendline on a 2D chart. We’re talking moving objects whose speed and parallax provide deeper perspective.
And beyond analyzing past data, can we predict future outcomes? Like Nvidia’s Omniverse, the idea is to simulate real-world conditions to game out future outcomes. That could be everything from an industrial machine’s maintenance cycle to the progression of climate change.
We’ll pause there and cue Pell’s full talk below…