importancewhat

Importancewhat is a term used to describe the practice of identifying which elements of a situation, dataset, or process are most informative or consequential to a stated goal. It serves as a lens for prioritization, guiding analysis toward what matters most rather than every variable.

Its etymology is a simple portmanteau of 'importance' and 'what,' signaling a shift from questions of when, where, or how to questions of what is significant. It appears in fields that blend qualitative judgment with quantitative analysis, such as data science, knowledge management, and policy design.

Conceptually, importancewhat complements traditional feature-importance measures by centering evaluation on a defined objective. Rather than ranking by general influence, it aims to answer: what elements, if improved or preserved, would most improve the outcome of interest?

Methods combine stakeholder input with quantitative signals. Techniques include requirement elicitation and domain-expert scoring, as well as model explanations such as SHAP values that reveal inputs most affecting a targeted outcome. Impact mapping and prioritization matrices help crystallize what to address first.

Applications span product development, risk assessment, and operational optimization. In software design, importancewhat helps teams decide which features to implement next; in data science, it guides feature selection toward variables most predictive in a given context.

Example: in a customer-support dataset, importancewhat would highlight the specific issue types—such as password resets or payment errors—that most strongly predict resolution time, guiding process improvements and user messaging.

Limitations include context dependence and potential bias in defining the objective. It should be used alongside broader analyses to ensure a complete view, and requires explicit criteria for what counts as 'important' in a given setting.