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This study explores the idea of collaborative data analysis, where it isassumed that every member of an analysis team possesses a tiny fragment of the required knowledgeand, taken together, they can use their collective intelligence for successful data analytics (Bernsteinet al. 2012). Specifically, we propose and evaluate an approach to process complex data analysisinquiries with the aid of lay statisticians and enthusiasts possessing only limited knowledge about dataanalytics. This paper proposes a collaborative data analysis framework allowing structured dataanalysis tasks to be distributed as a collaborative process to a group of people with a diverse set ofskills and knowledge. The proposed approach is examined through two hypotheses: (a) data analysisprojects can be decomposed into small enough tasks such that non-experts can successfully perform onthem and (b) teams with a mixed level of expertise perform as well as standard expert based projects.Our evaluations showed that data analysis tasks, with a focus on the pre-processing activities, might besuccessfully distributed and accomplished by the non-expert workers. Moreover, the outputs of thecrowdsourced data analysis are equivalent in quality and competitive in cost in comparison with theexpert-based work.
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