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Decision-Making in the Age of Data

Our most current research focuses on adapting the (originally) medicine focused evidence-based practice to the broad domain of organizational management. Evidence-based practice is a powerful idea, but while its focus on academic research is well justified in medicine where the bulk of new knowledge is indeed created and disseminated by the broadly sketched academic research and publishing ecosystem, it is simply not so in organizational management, where the bulk of new, decision-guiding organizational knowledge is created outside of the academic research domain. Simply put, in order for the compelling notion of 'evidence-based practice' to be embraced by business organizations it needs to be re-focused on informational sources that drive organizational sense making, most notably all manners of organizational data, in addition to the often rich reservoirs of experiential knowledge.




Evidence-Based Decision-Making: How to Leverage Available Data and Avoid Cognitive Biases (book)

The growing importance of data-driven decision-making to organizational competitiveness poses a number of vexing organizational learning related questions. In order for organizations to develop methodologically-sound and informationally-complete learning capabilities that go beyond mere accumulation, storage and cataloguing 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.


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Evidence-Based Organizational Learning (article)




The growing importance of data-driven decision-making to organizational competitiveness poses a number of vexing organizational learning related questions. In order for organizations to develop methodologically-sound and informationally-complete organizational learning capabilities that go beyond mere accumulation, 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 here 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. The 3E framework also puts forth specific evidentiary insight extraction methodologies that reflect the informational uniqueness of the two meta-categories and four categories.

Analytical Capability Development (research in progress)




Within organizational setting, generation, dissemination and utilization of data analyses derived knowledge requires an underlying, purposefully and thoughtfully constructed evidence generation capability. While nowadays many organizations have data analytic capabilities, in the sense of having teams of data analysts or data scientists, comparatively few have broader, fundamental-R&D-like research & analyses capabilities, yet in the age of ever more information and information types, the ability to be truly evidence-based demands nothing less. The focus of this research initiative is on identification and profiling of generalizable, risk estimation related evidence generation, assessment, amalgamation and deployment process steps, with the ultimate goal of developing a broad framework to guide thoughtful and purposeful creation of organizational knowledge creation and dissemination mechanisms. It is a manifestation of the underlying conviction that the conceptually compelling idea of evidence-based decision-making will remain nothing more than a compelling idea without an enabling infrastructure.

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