Getting wild with Data. Re-embodying Data Through Myth, Metonym and Biophilia

Why I’m Giving This Talk

I keep waiting for the era where we finally retire the pie chart. Or at least admit we’re tired of pretending a bar chart is a revelation. And yet, somehow, we’re still here—polishing the same geometries for every new dataset.

I suspect many of us feel that quiet tension between “just make it pretty” and “just make it clear.” Wanting something that feels alive. As data and visualization grow more abstract and AI makes image generation effortless, I worry we’re eroding lived meaning.

I’m not against cleaner charts. I’m against charts that feel distant. Against beautiful visualizations that remain emotionally detached. The central question of this talk is simple:

Does our data form actually carry the structure, behaviour, and meaning of what it represents?

Before dashboards, humans used fables, myths, and animal stories to communicate complex moral and epistemic truths. Ant colonies illustrated distributed labour. Seasonal migrations encoded time and memory. These weren’t decorative metaphors. they were cognitive structures that made complexity graspable.

This talk is about reclaiming that structural thinking, not through nostalgia, not through illustration-for-its-own-sake, but through precision. Drawing from biophilic systems (animals, insects, plants, ecological behaviours) and cultural lore, I’ll show how we can re-embody data in situated ways.

Not “data is like ants.” But “information density here follows pheromone-trail logic.”

What I’ll Present:

I’ll introduce a framework I call The Six Ecologies of Lived Data:

System – interdependence

Flux – growth and decay

Echo – memory and trace

Adversarial – tension and resistance

Affective – emotional intensity

Commons – shared meaning

Through live and applied examples, I’ll demonstrate how to reinterpret a dataset through these modes and identify what structural qualities must be preserved in form.

I’ll show how to:

Begin with a dataset and map its behaviours—flow, density, hierarchy, feedback.

Research and identify a living system or lore with structural parallels.

Deconstruct both the dataset and the living system to find precise correspondences.

Shift from metaphor to metonym—from surface similarity to structural alignment.

Use AI thoughtfully: prompt for variation, then strip away aesthetic excess to reveal the grammar beneath.

The focus is not on producing a new chart type, but on changing how we begin. On asking better questions before we render.

Who This Talk Is For

Designers, data practitioners, students, artists, researchers, and civic technologists who feel visualization today oscillates between sterile abstraction and aesthetic spectacle. Especially those working with AI who want conceptual control rather than aesthetic automation.

No advanced technical skills are assumed. This is about mindset, method, and structural thinking.

You’ll leave with:

A new mental model: data can be lived, storied, and embodied, not merely rendered.

A practical way to think in metonyms rather than metaphors.

A framework for asking whether your visualization actually carries the system it represents.

This isn’t a rejection of charts. It’s an invitation to rethink what they could become.