Systematically collecting, analyzing, and applying data to make informed decisions rather than relying on intuition or assumptions alone.
Data-driven decision-making is the practice of basing choices on verified data and rigorous analysis. It spans from reading basic metrics to designing organizational data strategies, integrating data literacy, analytical reasoning, and judgment to reduce uncertainty across all contexts.
You are aware that data can support better decisions but lack the habits to use it consistently. You can identify basic data sources relevant to your work and understand simple metrics when presented, but you do not yet seek out data proactively before making choices.
What Comes Next
If you've checked off most of this list, you're ready for the Metric Reader stage, reading and interpreting metrics independently to inform your daily decisions. Kolb(1984)'s Experiential Learning theory suggests cycling through observing data in your daily work and reflecting on outcomes to build effective foundational habits.
5-level data literacy model (Unaware to Driven) used to define maturity boundaries for data utilization and derive behavioral criteria per level.
Defines progressive analytics capability path through 4 maturity stages (Descriptive→Prescriptive), used to design stage-specific behavioral criteria in checklists.
International assessment measuring adult literacy, numeracy, and problem-solving proficiency across 6 levels, providing governmental/international authority for data-driven decision competency.
Extends classical decision theory to the big data and analytics era through the DECAS framework. The concept of collaborative rationality between human judgment and machine analysis provides academic grounding for L4-L7 data-judgment integration competency checklists.
