The drive to visualize and simplify data is nothing new. In fact, it’s ancient, and expressed in many cultures as early as 5500 B.C. It also might be more of a familiar idea than you realize — for instance, if you ever had a scale model of the solar system as a kid, you had a data visualization tool in your own home (and one that has been around since Ancient Greece).
On the other hand, our earliest efforts resemble nothing like the computer vision problems engineers are tackling today. Yet after thousands of years, the underlying themes are the same: How can we make complex ideas simple to parse? How can we help ourselves and others better understand data in the world around us?
The Beginning: Turning Experience into Symbols
The earliest instance of data visualizations was clay tokens used in Mesopotamia, roughly 7500 years ago. They represented sales transactions, materials, agricultural data, livestock counts, and votes. There’s even indications that these clay models helped inspire the creation of the written word – another graphic representation of spoken information. A few thousand years later, in AD 132, the seismoscope (the oldest visual indicator of tectonic plate movement) was invented in China, pairing aesthetics with functional data representation.
Not only is visualized data easier to understand, it’s a concrete manifestation of ideas that can be categorized, stored, and revisited as necessary. That’s a key reason data visualization is so persistent through history.
Communication through Visualization
Fast forward to the 1800s. You might be familiar with the Marshall Islands Stick Charts, which weren’t maps of geographical features but of tidal patterns and wave swells used for canoe navigation. Similarly around this time, Inuit communities were creating 3D maps of islands to serve as hands-free navigational guides as they sailed.
Imagine trying to navigate islands solely by a written description of their features. It would be impossible! Virtually any map delivers the same information in a format that’s substantially easier to understand and apply. But maps are just one example: Data visualization is the simplest way to communicate many metrics, particularly those that incorporate large datasets, abstract concepts, and complex interactions. Think of weather reports, diagrams of population statistics by country, microbial and nano structures, complex mathematical formulae — the list goes on.
Seeing the Shape of Data Today and Tomorrow
Lastly, in more recent years, data visualization has gotten big, both in popular use and the advent of big data applications. It’s in art, environmental movements, jewelry, and more. Take, for example, these behavior landscapes — wooden sculptures representing how individuals interact with others — or these 3D Lego maps representing emigration across countries.
So what are the next steps in this field? We’ve come a long way from ancient copper representations of the solar system. The vision problem we’re now tackling at Prism is turning huge clusters of data into visual heatmaps and pathmaps — pretty cool hybrids of data visualization and machine learning.
But we’re just one technology of many being created to turn ideas into graphic representations. Could we one day be programming nano-matter to make physical objects represent themselves differently? It may not be that far fetched! But whatever the future holds, it’s certain to continue bringing us great resources (like this tool for engineering data-driven documents), and new ways to see data differently.