# Data Concealment The function of a database is to conceal data such that it ultimately becomes intelligible. By Owen James — 2026-05-28 This spring, I interned at Flower as an NYU student hoping to apply my studies in philosophy to humanistic computing [1]. Through this research, I became interested in the relationship between ontological concealment and database systems [2]. As this post suggests, databases succeed because they formalize a fundamental condition of human understanding: meaning is produced through selective processing, structuring relevant information while concealing broader context [3]. Modern technology both conceals and unconceals. In ‘The Question Concerning Technology’ (1954), Martin Heidegger argues that modern technology constitutes a process of unconcealing—a ‘challenging-forth’ which orders the material world by unlocking, transforming, and distributing natural energies [4]. Modern technology is not merely a means to human ends, but a medium through which material reality is structured and unconcealed as the world we know [5]. A hydroelectric plant does not merely use a river as an independent resource but transforms it into a standing reserve of energy to be stored, regulated, and distributed on demand [a]. At the same time, modern technologies conceal the structures and systems that underlie them [6]. As a tool becomes more sophisticated and widely adopted, earlier forms of it appear further concealed; what becomes primitive conceals the older primitive forms beneath it [7]. In that sense, modern technology both unconceals material reality and conceals the forms and systems that underlie its immediate utility and appearance. The function of a database is to conceal data such that it ultimately becomes intelligible [8]. We want raw data and its ontology to remain hidden from the user; meaning is reflected not in the data ontology itself but in the systems we construct by selectively processing it [9]. Querying is fundamentally a mode of selective processing. A query retrieves meaning while simultaneously concealing the context from which relevant data is drawn. It defines the scope and relations of the data being retrieved while abstracting the ontological conditions that give it relative meaning [10]. Querying does not ‘unconceal’ data in the Heideggerian sense, nor does it merely access it, but renders it intelligible and useful in challenging-forth [11]. Databases organize the forms through which reality becomes available; querying selectively processes them. Human intelligibility is finite, selective, and structured through concealment. A database becomes more powerful insofar as it reinforces this ontological structure computationally.

Footnotes

[1]
IMAGE GOES HERE back
[2]
This research began as a historical study of the past two decades of database development. It looked at the early development stages of MongoDB, EdgeDB, SQLite, etc. to gain better insight into how they drove success, like how MongoDB placed as much emphasis on specialization as SQLite did on [The Rule of St. Benedict](https://sqlite.org/codeofethics.html). back
[3]
Selective processing falls into the same category as selective attention/perception in cognitive science, but has broader implications that are more appropriate here. The way I use it is closer to selective attention, which you can read about [here](https://www.ebsco.com/research-starters/psychology/selective-attention). back
[4]
‘Challenging-forth’ describes how nature is reduced to an object of extraction through the use of modern technology. Heidegger contrasts this to older technologies like the windmill, which does not ‘challenge’ nature insofar as it ‘does not unlock energy from the air currents in order to store it’ (Heidegger, 5). On the other hand, modern technology challenges nature by transforming the terrain into a standing reserve: ‘a tract of land is challenged in the hauling out of coal and ore. The earth now reveals itself as a coal mining district, the soil as a mineral deposit’ (Heidegger, 5). back
[5]
A good comparison to this is Marshall McLuhan’s *[Understanding Media](https://en.wikipedia.org/wiki/Understanding_Media)* back
[6]
For example, ChatGPT’s interface conceals the byzantine infrastructure that supports it. back
[7]
As William once explained it, consider the relationship between propositional logic and an array of tuples. An array of tuples is inherently more complex; in that complexity, it conceals the propositional logic beneath it. back
[8]
Data is intelligible only when irrelevant context is filtered out in a way that conceals it. This makes concealment extremely practical when the regular user of a database does not need to understand or even perceive of its underlying system to use it. back
[9]
An application only needs its desired fields from a database to create its material reality. For example, a purple dog app selectively queries from the Dog Database—a database containing all dogs—to return only purple dogs. back
[10]
A query that says, ‘show me all dogs whose names begin with the letter M’ might output ‘Max’ and ‘Mabel’. The first letter of each name is a latent relational property of all dogs, such as the relationship between letters ‘M’, ‘N’, and ‘O’; the conditions that determine that relationship are abstracted as soon as the query processes. back
[11]
The ‘humans dominate nature’ critique traditionally traces back to rationalization (Horkheimer, Adorno, Marx, etc.) Rationalization has a similar role in challenging-forth—nature is ordered through modern technology in a way that reinforces its rationalist logic. Information has to be intelligible for it to fit into this logic, which is exactly what querying ensures. back

References

[a]
M. Heidegger, “The Question Concerning Technology,” in *Basic Writings*, D. F. Krell, Ed. New York: Harper & Row, 1977. back