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Exploring Critical Success Factors in Agile Analytics Projects

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Item Summary Tsoy, Mikhail Staples, D. Sandy 2020-01-04T07:20:39Z 2020-01-04T07:20:39Z 2020-01-07
dc.identifier.isbn 978-0-9981331-3-3
dc.description.abstract Via updating Chow and Cao’s list of success factors for agile projects, attributes of potential critical success factors (CSF’s) for agile analytics projects were identified from the literature. Ten new attributes were added to Chow and Cao’s original list. Seven new attributes from the general agile project literature address: risk appetite, team diversity and availability, engagement, project planning, shared goals, and methods uncertainty. Three attributes specific to analytics projects were added: data quality, model validation, and building customers’ trust in model solution. The potential validity of the various CSF’s and attributes was explored via data from case studies of two analytics projects that varied in deployment success. The more successful project was found to be stronger in almost all the factors than the failed project. The findings can help researchers and analytics practitioners understand the environmental conditions and project actions that can help get business value from their analytics initiatives.
dc.format.extent 10 pages
dc.language.iso eng
dc.relation.ispartof Proceedings of the 53rd Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.subject Big Data and Analytics: Pathways to Maturity
dc.subject agile
dc.subject analytics projects
dc.subject business intelligence
dc.subject success
dc.title Exploring Critical Success Factors in Agile Analytics Projects
dc.type Conference Paper
dc.type.dcmi Text
dc.identifier.doi 10.24251/HICSS.2020.122
Appears in Collections: Big Data and Analytics: Pathways to Maturity

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