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Data models are foundational to the creation of data and any data-driven system. Every algorithm, ML model, statistical model, and database depends on a data model to function. As such, data models are rich sites for examining the material, social, and political conditions shaping technical systems. Inspired by literary criticism, we propose close readings of data models—treating them as artifacts to be analyzed like texts. This practice highlights the materiality, genealogy, techne, closure, and design of data systems. While literary theory teaches that no single reading is “correct,” systematic guidance is vital—especially for those in computing and data science, where sociopolitical dimensions are often overlooked. To address this gap, we introduce the CREDAL methodology for close readings of data models. We describe its iterative development and share results from a qualitative evaluation, demonstrating its usability and value for critical data studies.