The Art of Puzzle-Solving: A Data Visualization Masterclass

Have you ever watched anyone try to solve a puzzle? Their first instinct is always to tackle it head-on, applying brute force where finesse is necessary. I recently watched a compelling YouTube video by UNIVEA for a very crafty metal ring puzzle. In the puzzle, two interlocking rings seem to be screaming to be pulled apart at their obvious gap. And that’s what everyone does: they start pulling, twisting, and tugging, growing more agitated and frustrated as the stubborn rings refuse to budge.

And the genius here is that physical strength is irrelevant to the solution. Those rings have precise grooves and notches that must perfectly match in order to come apart. The puzzle demands patience, logic, and spatial reasoning. These are qualities that, as it turns out, are also essential to creating effective data visualizations.

That viral video inspired me to write a post on LinkedIn recently (referenced here). It has since further inspired me to write this extended version of my initial observations. In this article, I will develop in greater detail the analogies between puzzle-solving and the art of data visualization.

The Parallel Universe of Puzzles and Data

When we create visualizations, too many of us fall into the same trap as those frustrated puzzle-solvers. We start grabbing the easiest tools; relying on standard-issue charts without considering whether they’re actually the most effective for our story. We’re concerned about the end product rather than taking the time to learn the principles behind what makes a visualization effective.

Take the famous Rubik’s Cube, for instance. The initial default approach most people take is a bid to complete one face at a time, with the discovery that this strategy inevitably disrupts whatever progress they might have already made. The true solution involves understanding how each move affects multiple faces of the cube simultaneously. In much the same way, changing one aspect of a visualization affects the entire story we’re trying to tell with our data.

Beyond the Obvious: A Tale of Two Puzzles

Speaking of brain-twisting riddles, allow me to present you with another intriguing one that perfectly demonstrates this: the Pentominoes puzzle. Picture twelve distinct forms, each composed of five edge-to-edge joined squares. Your goal? Place them all into a rectangular grid. Seems pretty easy, right?

Wrong. The Pentominoes puzzle is a great example of strategic thinking and spatial reasoning. The answer, like with our metal rings, is not found in trying to force components where they seem to fit. It’s in understanding the relationships of the pieces and recognizing that sometimes the most rational place to put a piece is exactly what’s standing in the way of you solving the puzzle.

The Data Visualization Connection

This gets us back to data visualization. Usually, when we work with complicated datasets we run across the same challenges:

1. Pattern Recognition

Just as puzzle-solving requires the ability to find subtle patterns in the shapes of pieces or in move sequences, effective data visualization demands the ability to notice patterns in data that are not immediately obvious. Often the most important information is hidden in the relationships between different points of data, as opposed to the values themselves.

2. Spatial Reasoning

Being able to mentally rearrange objects and grasp spatial relationships is not just vital for physical problem-solving; it also helps create visualizations that successfully convey complicated data. We have to think about how various components would fit together and how our audience will view these spatial interactions.

3. Strategic Planning

Both data visualization and puzzle-solving involve looking a few steps ahead, not unlike the strategic thinking necessary for playing the game of chess. Just as a puzzle solver thinks about how each move will influence future options, an effective data visualizer needs to think about how every design decision will contribute to the overall story.

The “Aha!” Moment

Here’s what I enjoy the most about puzzles and data visualization alike: the eureka moment. You know, when it all suddenly clicks? That is, when the solution to the puzzle is so obvious you can’t believe you missed it before? While it may be quite the satisfying sensation, there’s actually more going on beneath the surface: this is when your brain is busy creating new neural pathways, learning to solve problems more efficiently later on down the line.

Putting It All Together

So, what lesson do we see here? Whether we seek to unscramble two interlocking rings, solve a Pentominoes puzzle, or produce an efficient data visualization, the solution is the same: to approach the problem with patience, logic, and an openness to look beyond the most evident solution. This is the very definition of “thinking outside the box” in action.

The next time you’re bogged down on trying to make sense of a data visualization, approach it as though it were a puzzle and ask yourself some questions:

• What patterns am I not seeing?
• What relationships have I not considered yet?
• What assumptions am I making?

Sometimes the solution that’s best (and perhaps the most elegant) is the one that might seem a bit counterintuitive at the outset. And just as was the case with the metal rings puzzle, you won’t come up with a solution by simply pulling harder; but rather, you’ll figure it out when you start understanding the structure “under the hood” and approach it more precisely and intentionally.

Conclusion

Approaching challenges such as interlocking metal rings, fitting Pentominoes, or creating successful data visualizations demands growing a mindset geared on pattern recognition, spatial reasoning, and strategic planning. These abilities enable one to detect underlying connections, visualize how components interact, and predict the results of every decision. Developing an approach using this type of methodology turns moments of exasperation and frustration into opportunities for understanding and clarity.

Patience and curiosity become rather important when confronted with unsolvable puzzles, difficult problems, or even tangled datasets. Often breakthroughs come from going outside whatever might seem to be the obvious solution and challenging first impressions. Those times of revelation show a more profound awareness that improves problem solving skills and enriches the way data narratives are presented. Thinking like a puzzle master unlocks solutions and encourages a more thorough understanding and appreciation of the workmanship behind data visualization.