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Data-driven decision making enables organizations to make decisions based on accurate data analyses and statistical methods. It promotes efficiency and precision in decision-making.
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Theoretical construct: explains a term, principle, or mental model.
What organizes, connects, or makes decisions possible.
Data-driven decision-making uses relevant data to assess options, test assumptions, and make decisions that can be explained.
The approach grew from statistics, operations research, and quality management, then spread through business intelligence and digital data collection. Research on evidence-based management carried that tradition into organizational practice.
A decision starts with a question and a testable measure. Data provide signals; people add context, weigh values and risks, then check whether the outcome matches the expectation.
Observable information that supports or challenges an assumption.
A defined measure for assessing a state or outcome.
Checking later results against the original expectation.
The approach makes assumptions visible and supports learning and prioritization. Poor data, selection bias, and missing values can mislead decisions just as much as intuition alone.
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