Marketing analytics rises or falls on one quiet moment: the data definition phase, when teams decide what to measure and how. Despite its centrality, the field lacks a standardized methodology for doing this well. This study asks how practitioners actually run data definition and what distinguishes mature from ad-hoc practice across agencies, in-house teams, and freelancers. Evidence comes from 40 semi-structured interviews analyzed inductively with reliability checks. Four dimensions consistently shape outcomes: process formality, stakeholder collaboration, documentation and tooling, and recurring failure points such as definitional drift, onboarding friction, and governance gaps. Comparative patterns show agencies emphasize standardization, in-house results hinge on leadership and culture, and freelancer-driven gains fade without internal ownership. The study introduces a five-level Data Definition Maturity Model that specifies practical capabilities, aligned KPI glossaries, facilitated definition workshops, versioned metric repositories, and privacy checkpoints. Higher maturity reduces “whose numbers are right” disputes, speeds consensus, and improves analytic reliability. The contribution is a shared language and actionable roadmap for a phase too often improvised; we argue that rigorous data definition is a necessary precondition for reliable analytics and AI-ready marketing data ecosystems.
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