In the earlier half of the 19th century Alexis de Tocqueville famously accused democracy, as represented by America, of a limitless love of egalitarianism: ‘for equality their passion is ardent, insatiable, incessant, invincible’ (1835, 97; second book, chapter I). A generation later, W.E.B. was both one of his age’s greatest witnesses to America’s unrealized passions for equality and also one if its greatest innovators of methods in the informational analysis of social inequalities. Du Bois is still today widely affirmed as one of our most powerful progenitors of racial equality. Late in life he wrote of his own ‘personal life crusade to prove Negro equality’ (1958).  As part of that crusade, Du Bois was also a pioneering sociologist of the late 19th and early 20th century, whose contributions to both quantitative and qualitative analysis long went unrecognized.

Du Bois’s work is, among other things, an egalitarian counter to the typological hereditarianism on such vivid display in the work of his contemporary Francis Galton (see the Our Data, Our Selves page on Galton’s statistical anthropometrics). Galton’s work fought hard to classify people into fixed racial categories based on inherited traits and statistical averages. Such methods were thought to justify racial hierarchies. Du Bois not only rejected this approach theoretically and ethically, but he also developed quantitative apparatus for representing race otherwise. His studies, beginning most prominently with The Philadelphia Negro (1899), used surveys, maps, and statistics to show the varied experiences of Black communities. These methods allowed him to highlight structural inequalities rather than reinforce entrenched racisms.

Exhibit A: Du Bois's Data Portraits

One example of this approach was Du Bois’s data portraiture at the 1900 Paris Exposition. He created a series of charts and graphs that showed the progress of Black Americans since emancipation. These visualizations included information on education, property ownership, and employment. They were designed to counter claims of Black inferiority and show that Black Americans were active participants in American society. Du Bois’s use of data in this context was both practical and political. It was meant to inform, but also to persuade.

Exhibit B: Du Bois's Data Formats

Another example is offered in Du Bois’s 1904 study Some Notes on Negro Crime, Particularly in Georgia. Here Du Bois’s team conducted a series of surveys that revealed stark disparities in perceptions of justice between Black citizens and White officials. He gathered responses from three groups: young Black children in Atlanta, older Black youth across Georgia, and a mix of Black and White officials and citizens. While nearly all White officials believed Black people were treated fairly in court, 86% of Black respondents—whom Du Bois described as intelligent and reliable—reported systemic injustice, especially in cases involving White accusers. Youth surveys echoed this distrust: 36% of younger children viewed police as unkind, and 63% of older youth said police had never helped or protected them

Rather than relying on extant criminological databases that were formatted (that is, designed both conceptually and technically) in ways that would invisibly reproduce extant racial hierarchy, Du Bois and his team sought to build different data formats. These alternative formats opened up the possibility of different kinds of data than had hitherto existed. Those different data formats more clearly highlighted the social causes of treatment (in contrast to the biological tendencies of traits) that were resulting in inequalities.

An Empirical-Strategic Counter to Social Inertia

Du Bois’s method here was both empirical and strategic. He sought to focus on divided perceptions of social context rather than reproducing uses of racialized crime rate data for unequal surveillance and policy. Crime rate data isolate individuals to monitor behavior or predict outcomes. Du Bois’s work did not aim to manage people unequally but to understand them as equals with one another. Said otherwise, he used data to support collective action and policy reform, not to produce new forms of control.

Du Bois deserves to be affirmed as a key figure in the history of racial datafication. His work shows that data can be used not only to classify but to support social change. By rejecting typological thinking and focusing on the egalitarian possibilities of quantitative measure, Du Bois developed a form of datafication that aimed at democracy. His legacy challenges current data practices and offers a path toward more ethical and inclusive approaches.

These and other of Du Bois’s data pursuits yield a crucial imperative: where we grasp and fasten persons through data, even when it is expressly for the sake of ameliorating social conditions, our very design of data must be actively and fervently trained on equality, for otherwise it is just too hard to not reproduce inequality. Contemporary philosopher of race Naomi Zack observes that “institutional racism continues through a kind of social inertia unless specific measures are taken to change it” (1998, 44). There is precision in Zack’s term inertia; it illuminates how social structuring is a process. A generalized restatement of Zack’s insight—namely, that hierarchies are inertial—implies that inequalities are not so much fixed structures as they are processes of structuring that tend to change slowly because of their immense heft. It is due to the inertia of entrenched heft that inegalitarian hierarchy can only ever be dismantled by active pursuits of equality, and never by remain­ing neutral.

Equality Within Data

The insight we can pull from Du Bois’s work for today involves a distinction between the pursuit of equality with data and the possibility of equality within data. Of course data should be used to pursue equality. Though a novel approach in Du Bois’s time, for us today this sentiment would hardly be contested. More significant, and more challenging, is the realization that anyone doing anything with data (including pursuing equality) ought to be fervently atten­tive to how inequalities may be designed into their data formats. Critical data studies scholars have amply documented how the use of data by morally up­right agents pursuing equality can and does go awfully awry. Du Bois’s work helps us understand what to do when this happens and, better yet, what to do so that it does not happen so much. The insight his data work offers is that the pursuit of equality within data is a condition of the pursuit of equality with data.