Some things are so big, they can only exist to cover up something else. In the ’90s and early 2000s, it was cars: SUVs that never saw an off-road trail, tanks on four wheels, needed just to roll to the grocery store. Today, it’s data: Big Data. Massive datasets, proudly paraded like a shiny status symbol.
Who needs theory, careful thinking, or painstaking research when you have Big Data? The numbers “speak for themselves,” or so the legend goes. How convenient: Science as a self-service buffet where you dump numbers into a machine and out comes Truth. Meanwhile, pesky questions like “Where did these data come from?” or “What do they actually not tell us?” can be politely ignored.
When Size Is Everything
Big Data is the academic equivalent of the 90ies “Look at my enormous car!” But Big Data is not the Rolls-Royce, it's the SUV among research practices: The more terabytes, the more prestige. Small datasets are like compact cars—cute but embarrassingly outdated. And anyone still doing qualitative research, conducting interviews, or slogging through fieldwork? They might as well show up on a bicycle: environmentally conscious, but hopelessly out of fashion.
The problem? Bigger doesn’t equal better. Analyzing millions of tweets about being hungry doesn’t teach us anything profound about humanity — it just quantifies an algorithmic truism. But as with those SUVs, it’s less about substance and more about appearance.
Garbage In, Garbage Out — But Make It Huge
Big Data researchers love to flaunt sheer volume as a badge of quality. Yet more data doesn’t automatically mean more truth. Social media datasets? Biased. Medical databases? Incomplete. Sensor logs? Context-free.
It’s like showing off a massive SUV while conveniently forgetting the engine is broken. Millions of data points, but what do they really prove? Not much. But hey, at the next conference, you can still proudly announce: “Our dataset contains 1.2 billion entries!”
The Black Box Glossed in Chrome
And then there are the famous algorithms: machine learning, deep learning, neural networks — all sounding impressive. The catch? Nobody truly understands what they do. The models are black boxes; the results are opaque. But as long as you can polish them for presentation, who cares?
Transparency, reproducibility, accountability — all left in the dust. The SUV of science has tinted windows: it looks big from the outside, but what’s going on inside? Good luck figuring that out.
When Data Becomes a Substitute Religion
Big Data promises freedom from theory. No hypotheses, no critical reflection — the data will just “reveal the patterns.” What actually emerges is not enlightenment, but data fetishism: worshipping numbers one doesn’t understand or question.
It’s like walking into the SUV showroom and praying: “Oh mighty dataset, reveal the truth!” — and then nodding reverently at whatever colorful chart the machine spits out.
Don't get me wrong. Mathematics has its limits but also its purpose. On the use and beauty of Mathematics in another article.
The Big Picture? Not So Much.
The tragedy: Big Data often does the opposite of what it promises. Instead of insights, we get trivialities. Instead of objectivity, we get biased computations. Instead of transparency, we get black boxes. Research becomes shallower, not deeper — all with a lot of fanfare.
Big Data isn’t the Rolls-Royce of science; it’s the ostentatious SUV: expensive, huge, unwieldy, epistemologically questionable. But hey, at least you sit up high and look down on all those “tiny” methods.
Conclusion:
Big Data is the status symbol of a science trying to compensate for methodological insecurity. It dazzles. It roars. It looks impressive. But for anyone actually looking closely, it’s more show than substance.
Questions:
Are we really measuring the world – or just what’s easy to count?
Can a number ever be neutral if it comes from a biased world?
Are we mistaking method-worship for science itself?
Who actually benefits from the fetish of massive datasets – science, or the careers of individual researchers?
When did we stop building theories and start just filling data silos?
And maybe the biggest question: What’s left of science if we admire size but forget depth?
Authored by Rebekka Brandt