The Revolution Will Not Be Statistically Derived

The Revolution Will Not Be Statistically Derived

By: Catherine D.L. Tan

There is a prevalent worship now of the data-driven paradigm. Data-driven startups. Data-driven policy choices. Data-driven impact. Data-driven storytelling. Data-driven everything—as if attaching "data-driven" to any endeavour automatically sanctifies it, makes it unquestionable. Makes it true. Data-driven, circa 2025, stands as the method to the madness. The antidote to disinformation. The buttress against the whims and vigours of authoritarian dictators.

This obsession did not emerge from a vacuum. It arose as a corrective to the catastrophic arrogance of imposed vision i.e., historical moments when characters ranging from tech evangelists to policy architects to authoritarian rulers decided they knew precisely what humanity required, and then imposed that vision from above without consent or consultation. These historical moments are not outliers; the impulse to superimpose has been a mainstay in the long durée of humanity’s story. State-making has done this to more amorphous shapes of local communities, as James C. Scott posits in his canonical Seeing Like A State. The project of international law and its utopian ambition to construct global society has done this to various parcels of the world, imposing ‘legality’ to spaces law is not meant to touch: ranging from the bedroom (through sexual norms), to scent (through legislation governing the senses), to pre-human concepts like time (by consecrating it as a legal construct through the International Meridian Conference in Greenwich 1883).

The failures of such top-down impositions have left scars across our collective memory: colonial systems that appeared rational on European maps while suffocating indigenous realities; ghost cities in China and South Korea designed with perfect geometric precision yet rejected by the human bodies meant to inhabit them; economic "solutions" derived from elegant models that devastated actual economies upon instance of implementation. In each case, the vision solidified at the top while life struggled to breathe at the bottom.

Against this background, the data-driven revolution appeared as liberation. Its promise was seductive in its simplicity: we would no longer impose; we would ask. We would measure before moving. We would build only what could be quantified, validated, proven through rigorous methodology.

What better way, after all, to erode the monolith than to break it into measurable blocks? To conduct focus groups? To interview individuals about their lived experiences? To ask them to validate your theory about their reality?

This approach seemed democratic, even radical. No different, if we really think about it, from asking people to vote. Surveying someone and soliciting their vote are both instances of preference-gathering. You're asking what they want so you can ostensibly deliver it better.

And so data became the weapon of choice for what we convinced ourselves was revolution.

Jazz poet Gil Scott-Heron said: "The revolution will not be televised." Today's data evangelists respond with confidence: "The revolution will be statistically derived!"

But alas, therein lies the fundamental paradox: Data, by its nature, measures what already exists. It validates established reality. It is a snapshot not just of what exists, but what can be legible to the human eye or the existing human apparatus. Meanwhile revolution, by definition, creates what does not yet exist: What is not yet legible to the human eye and its ways of seeing. What cannot be grasped by the existing apparatus. What escapes articulation because there’s no language for it yet. Revolution manifests new reality; and this tension cannot be resolved through better measurement techniques or larger sample sizes. It represents a fundamental incompatibility of purpose.

Consider how history's actual revolutions would fare under our contemporary obsession with data validation:

Imagine Johannes Gutenberg, before daring to build his printing press, trudging through medieval Europe with a parchment survey, approaching bewildered peasants:

"On a scale from one to ten, how frustrated are you with manuscript availability?" "If you could have a new technology, would you prefer: a) a mechanical printing press, b) improved quill pens, or c) more reliable candlelight?"

This very question would be incomprehensible. The demand for books could not exist before books were a widespread possibility. The need was there—but it was latent, invisible to any data collection method, no matter how sophisticated.

Or consider the early Internet. Picture ARPANET scientists in 1969, standing among flower children in Haight-Ashbury, distributing questionnaires: "Would you value a globally connected information network where knowledge travels at the speed of light?" "On a scale of 1-5, how likely would you be to replace libraries, record stores, and postal services with a digital screen?"

The questions themselves would have seemed like science fiction—meaningless to those who had never experienced digital connection, whose cognitive frameworks couldn't yet accommodate such possibilities.

The French Revolution provides perhaps the starkest example. Imagine if, instead of storming the Bastille, revolutionaries had commissioned a thorough demographic study:

"Please rate your satisfaction with monarchical governance on the following scale..." "If we were to organize an uprising, would you be: a) extremely likely, b) somewhat likely, or c) unlikely to participate?"

Revolutionary consciousness cannot be captured by rating scales. It emerges through collective realization, through the sudden crystallization of possibilities previously unimaginable. Revolution, by definition, comes from the unthinkable. The unknown. The fringes of consciousness, collective or otherwise. Some systems theorists call it the “adjacent possible,” a timeline that is ever so asymptotic to this current timeline but can be triggered with just the right nudge. Close but not touching. Revolutionaries are the agents collapsing the distance between those two timescales – a job description that is above the pay grade of a data collector who labours not in the unknowable, but in the known.

Yet today, we insist that the future conform to the metrics of the past. We demand that paradigm-shifting ideas submit themselves to focus groups before we grant them permission to exist. We require that tomorrow's unimaginable breakthroughs first prove themselves using yesterday's measurement tools.

This phenomenon, and data fundamentalism more broadly, isn't just an invisible pulse in the ether. It makes appearances in the grit of our daily realities. This was certainly my experience in building Uncommon Future Press, a design-led epistemic insurgency against traditional academic publishing that is interested in reconfiguring the relationship between knowledge, power, and cultural production. We are an alternative academic press house. Our goal is to displace long-standing oligopolies in the space. We transform academic knowledge from paywalled journal articles into cinematic multimedia experiences—from immersive graphic novels to narrative animations to theatrical productions—accessible anywhere from Hong Kong to Cape Town to London, not unlike satellite communities in a multi-theatre knowledge insurrection. While the goal is to be alternative, the ambition is to close the gap that separates knowledge from the ordinary person, allowing it flow ever more freely from the ivory tower to whoever it needs to reach: activists, journalists, policymakers, world leaders.

While UFP is technically a startup, I don't identify as an entrepreneur. I intentionally establish distance from archetypal 'tech bro' ecologies because of my background in diplomacy and legal philosophy, which lend me access to streams of thought that help approach building the press house similar to building a grassroots initiative. Borrowing from revolutionaries before me, I plant seeds and nurture fires – wild things I occasionally tame by imbricating them across precise scaffoldings of rigorous thought. Despite these inclinations, I initially considered finding a home for UFP in incubator and accelerator programs in London and California, only to confront these ecologies’ data fundamentalist application portals ---

--- now if an application portal is a microcosm of an organization's worldview, then what does it reveal when incubators and accelerators, ostensibly at the frontier of innovation, are built around fetishistic data validation? These portals aren't mere administrative filters; they're epistemic artifacts, revealing a world where new ideas are evaluated not for their vision but for their ability to contort into predetermined metrics. When I glimpsed at the portals, what I saw were self-avowed revolutionaries who were quick to say they held Promethean fire, but secretly enjoyed thinking in terms of boxes. It became clear that these entities were not incubating and accelerating revolutions. No. They were accelerating the rapid-manufacture of even more rigid two-dimensional shapes—shapes that are no different from their rigorously guarded blocks of data.

But is revolution really a flat square? A pointy parallelogram? A lonely triangle? Or do revolutions take on quantum capabilities, superimposed and existing in multiple states like Schrödinger's cat?

This insight, clear as day, further strengthened my resolve on how to run the startup like an activist: I wasn't going to incubate UFP in data fundamentalist labs. I would incubate it in the corridors of political struggle; in my past as a heterodox economist fabricating bespoke alternative economic models; in the radical traditions of Third World Approaches to International Law (TWAIL), which inform my vision of law; and in the hidden chambers where I train younger diplomats to negotiate for equitable outer space policies at the United Nations.

It was precisely this realization—the suffocating epistemic limits of existing innovation cultures—that led me to deliberately reject these structures in building Uncommon Future Press. Never a startup in the traditional sense; rather, an epistemic insurgency with multiple theatres—a generative force constructing not just projects, but entire cultural, legal, and artistic movements. UFP refuses to inherit the epistemic scaffolding of data-driven everythings, instead incubating futures in spaces where revolutionary thinking can breathe. Where shapes are wonky, both/and, and cat-like in their Schrodinger abilities.

Their revolution is statistically derived; mine is insurgent, generative, and unapologetic. Statistics can wait for when we want to optimize our paradigms; but that only comes when we're built. Not when we're preparing the world for what we're building.

What the reader may intimate by now is that data-driven revolutions are not only methodologically flawed — they are ontologically impossible.

In our obsession with data validation, we've forgotten one of humanity's most powerful forces: performativity. It is incorrect to assume that theories, models, and visions describe existing reality.

Rather, they actively generate it. Borrowing a metaphor from the economic sociologist Donald Mackenzie: Conventional wisdom treats economic models as cameras, passively capturing market behaviour. But models aren’t cameras; they’re engines. When deployed, they don’t just reflect reality. They reshape it. Deploy a model to a space, and the space can conform to the model’s shape and size. Deploy a model to a space, and its inhabitants could build a world around it, or even take from its internal compositions.

Depending on the potency of the artefact, as well as key environmental conditions, the previously foreign object can even remake the community in its own image. Consider neoliberalism and its ability to forge new markets, new institutions, new human subjects. Consider social media’s ability to construct new forms of labour (hello, influencers), and rewire the very notion of connection.

While the examples I mobilised here are to do with economic models, ‘performativity’ has import across other spheres of life. We can’t forget that ‘performativity’ first surfaced in discussions of language, to describe its very incantatory qualities. Someone says “I pronounce you man and wife,” and two people become man and wife. Someone says “I declare this null and void,” and depending on their legal standing, the thing becomes null and void. From economic models to mundane legal pronouncements, it is part of our roster of cosmic powers to incant. To manifest. To generate. To incarnate.

Data fundamentalism strips us of this generative power. It tells us we can only build what already exists, what can be measured — a paradox that makes real innovation impossible. It reverses the creative order, insisting that vision must follow validation rather than the other way around. Data-driven thinking, when taken as dogma, denies us this agency. It chains us to the world as it is, severing us from the world as it could be.

Data can optimize existing reality. It cannot invent a new one. Data can refine what is. It cannot imagine what could be. Data can tell us where we are. It cannot show us where we've never gone.

The problem isn't data itself, but data fundamentalism—the quasi-religious belief that nothing deserves to exist unless it can first be quantified, measured, proven through statistical significance. This fundamentalism represents a profound failure of imagination, a collective surrender to the tyranny of the measurable. It embodies what philosophers call a category error — applying the tools of optimization to the fundamentally different domain of creation. Right purpose, wrong tool.

The most transformative forces in human history — art, revolution, scientific paradigm shifts, spiritual awakenings — did not emerge from statistical validation. They were powered by intuition, by conviction, by a bone-deep, ineffable sense that a different reality is possible. They began as quantum possibilities before collapsing into measurable forms.

This is not to romanticize intuition or dismiss empiricism. Both have their domains of relevance, their appropriate applications. Data serves us brilliantly when refining what exists, when optimizing within established paradigms, when measuring incremental change.

But when we need to break paradigms entirely? When we need to imagine what has never been? It’s in our best interest to recognize data's fundamental limitations. It’s a necessary onus to acknowledge that what is truly revolutionary cannot be measured before it exists. Otherwise, we hack ourselves at the Achilles’ heel.

There is a profound irony in data's current stranglehold on innovation. The data-driven movement itself began as liberation—a democratizing force against the tyranny of imposed vision, a way to empower the voices of many against the dictates of the few. It was meant to be emancipatory, a tool for the marginalized to speak truth to power.

Yet we now face the familiar pattern of revolutionary tools becoming new orthodoxies. The very mechanism that promised to free us from the limitations of top-down thinking has calcified into its own form of tyranny—a tyranny of the measurable, the proven, the already-existing.

This pattern appears with such consistency throughout history that we might consider it a law of revolutionary physics: movements that begin by opening new possibilities inevitably develop boundaries that close them. The radical becomes routine. The liberating becomes limiting. The revolution becomes tradition.

The data revolution has followed this trajectory with startling precision. What began as resistance to arbitrary authority has become an authority unto itself—perhaps more insidious for its claims to objectivity. "The data says" has replaced "God says" or "The king says" as the unquestionable foundation of truth, dismissing as irrational any vision that cannot produce supporting statistics.

So no, the revolution will not be statistically derived.

And if we continue insisting that it must be, we risk creating a future that is merely an optimized version of the past—a future without the wild, transformative leaps that have defined our most profound moments of progress. The path forward lies not in rejecting data, but in recognizing its proper place in a larger ecology of knowledge—one that embraces both the measurable and the immeasurable, both validation and vision, both what is and what could be. Data is symbiotic to revolution; but is not the revolution itself.

This is, it bears repeating, not to reject data's value but to recognize that revolutionary tools require revolutionary consciousness to maintain their liberating potential. We must continuously question not just what the data tells us, but what it cannot tell us—what possibilities lie beyond its capacity to measure.

The future waits for those of us who know when to measure and when to leap beyond measurement entirely—for those who understand that the most profound transformations begin not with statistical significance, but with the courage to imagine what no survey could possibly capture.