Undergranularity

Undergranularity is a term used to describe a situation in which the granularity of data, processes, or system components is insufficiently fine-grained for a given analytical or operational purpose. In data analysis, for example, undergranular data sets might aggregate information into large categories that obscure important variations, leading to misleading conclusions. In computational modeling, an undergranular representation of a system may simplify complex dynamics by grouping distinct states into a single generic state, potentially reducing model accuracy. The concept also appears in database design, where undergranular tables combine disparate attributes that could benefit from separate tables, impairing normalization and query efficiency. In physics, particularly in solid-state studies, undergranular analysis may treat a material as homogenous while neglecting microstructural heterogeneities that influence properties such as conductivity or strength. Understanding undergranularity is important for researchers and engineers because it highlights the trade‑off between model simplicity and descriptive fidelity. Tools that address undergranularity include multi‑resolution analysis, hierarchical clustering, and adaptive mesh refinement, which allow one to increase resolution selectively where needed. By recognizing and mitigating undergranularity, practitioners can maintain accuracy while managing computational cost or data volume.