Conceptual Frameworks vs. Raw Data
Professional data scientists have a number of conceptual frameworks in their mental tools kit to process raw data.
A conceptual framework is a way or process of thinking that applies a theoretical structure of assumptions, empirical evidence, and a logical representation of relationships within phenomenon (linear and nonlinear) - and provides direction for selecting, organizing and weighing a diversity of data to achieve specific goals. Conceptual frameworks use precise terminology to impose intellectual discipline in understanding relationships between cause and effect in specific contexts.
Raw data is primary data (data that has not been processed for use) that may consist of instrument readings, symbolic representations, numbers, figures and other representations - collected from a source. Raw data may be turned into useful information by data processing and analysis within conceptual frameworks.
Seasoned data scientists seek to understand objective reality and focus on "why" or "what caused" a phenomenon to occur. Also helpful is to understand how and why subjective "perceptions" of reality result in human individual and collective actions to navigate in high causal density ecosystems.
Data processing and analysis using the wrong conceptual framework can lead to disaster. It is the data scientists sacred duty to use the appropriate conceptual framework(s) in specific contexts.
The appropriate conceptual framework processing the right smart data helps data scientists understand reality in complex environments to achieve specific goals.