analysability

Analysability is the degree to which a subject—such as a system, process, dataset, or problem—can be subjected to analysis in order to understand, explain, or predict its behavior. The term is used across disciplines with nuance: in engineering and quality management, analytability refers to how easily a system can be decomposed into components and modeled; in data science and statistics, it describes how well data and modeling methods support reliable inference.

Key characteristics include modularity and clear interfaces; data availability and quality; the repeatability of procedures; documentation of assumptions; and the observability and measurability of outputs. High analysability is typically aided by well-defined goals, transparent structure, stable conditions, and limited hidden dependencies or nonlinear effects.

Applications span system design and maintenance, where analysability influences debugging and validation; software engineering and project planning, where it informs complexity estimates and testability; and data analysis and decision making, where it affects identifiability and interpretability of results.

Assessment can be qualitative or quantitative. Qualitative evaluations consider how easily a system can be broken down and understood. Quantitative measures may include modularity scores, observability of variables, identifiability in statistical models, data quality metrics, and reproducibility rates. Analysability is not absolute and may depend on tools, expertise, and context.

Limitations and challenges include coupling, hidden dependencies, nonstationary or evolving behavior, incomplete information, and constraints that limit observation. Well-documented, modular, and stable systems tend to have higher analysability than complex, adaptive, or poorly observed ones.

See also: analyticity, interpretability, identifiability, modularity.