CREDAL: Close Reading of Data Models

TL;DR

CREDAL introduces a systematic methodology inspired by literary criticism for critically analyzing data models, emphasizing their material, social, and political contexts to foster more reflective data practices.

Contribution

This paper presents the novel CREDAL methodology for systematically conducting close readings of data models, addressing a gap in critical data studies.

Findings

CREDAL is usable and effective for critical analysis.

It helps uncover socio-political aspects of data models.

The methodology enhances understanding of data system origins.

Abstract

Data models are necessary for the birth of data and of any data-driven system. Indeed, every algorithm, every machine learning model, every statistical model, and every database has an underlying data model without which the system would not be usable. Hence, data models are excellent sites for interrogating the (material, social, political, ...) conditions giving rise to a data system. Towards this, drawing inspiration from literary criticism, we propose to closely read data models in the same spirit as we closely read literary artifacts. Close readings of data models reconnect us with, among other things, the materiality, the genealogies, the techne, the closed nature, and the design of technical systems. While recognizing from literary theory that there is no one correct way to read, it is nonetheless critical to have systematic guidance for those unfamiliar with close readings. This is especially true for those trained in the computing and data sciences, who too often are enculturated to set aside the socio-political aspects of data work. A systematic methodology for reading data models currently does not exist. To fill this gap, we present the CREDAL methodology for close readings of data models. We detail our iterative development process and present results of a qualitative evaluation of CREDAL demonstrating its usability, usefulness, and effectiveness in the critical study of data.