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Evidence-Based Decision-Making: How to Leverage Available Data and Avoid Cognitive Biases.


The growing importance of data-driven decision-making to organizational competitiveness poses a number of vexingorganizational learning related questions.In order for organizations to develop methodologically-sound and informationally-complete learning capabilities that go beyond mere accumilation, storage and cataloging of data and other informational assets, more explicit information amalgamation and synthesis focused frameworks are needed. The Empirical & Experiential Evidence (3E) framework outlined in our new book is built around the idea that since the primary utility of organizational knowledge is to support organizational decision-making, topically-related data and information can be considered decision-guiding evidence. The framework’s evidence synthesis logic parallels the general 6-step process of identifying, assessing, aggregating, weighing, agglomerating and incorporating distinct but related information, but that process is nested within a 3-tier evidence classificatory schema which categorizes all available decision inputs into two broad meta-categories, four more narrowly scoped categories, and twelve even more operationally meaningful sub-categories. While emphasizing utilization of broadly defined operational data, the approach detailed in Evidence-Based Decision-Making is nonetheless expressly assimilative, insofar as it also encapsulates other common sources of organizational insights, most notably applicable scientific research, industry norms and standards, and objectified expert judgment.

Evidence-Based Decision-Making

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