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Big Data Value Engineering for Business Model Innovation

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dc.contributor.author Chen, Hong-Mei
dc.contributor.author Kazman, Rick
dc.contributor.author Garbajosa, Juan
dc.contributor.author Gonzalez, Eloy
dc.date.accessioned 2016-12-29T02:11:06Z
dc.date.available 2016-12-29T02:11:06Z
dc.date.issued 2017-01-04
dc.identifier.isbn 978-0-9981331-0-2
dc.identifier.uri http://hdl.handle.net/10125/41877
dc.description.abstract Big data value engineering for business model innovation requires a drastically different approach as compared with methods for engineering value under existing business models. Taking a Design Science approach, we conducted an exploratory study to formulate the requirements for a method to aid in engineering value via innovation. We then developed a method, called Eco-ARCH (Eco-ARCHitecture) for value discovery. This method is tightly integrated with the BDD (Big Data Design) method for value realization, to form a big data value engineering methodology for addressing these requirements. The Eco-ARCH approach is most suitable for the big data context where system boundaries are fluid, requirements are ill-defined, many stakeholders are unknown, design goals are not provided, no central architecture pre-exists, system behavior is non-deterministic and continuously evolving, and co-creation with consumers and prosumers is essential to achieving innovation goals. The method was empirically validated in collaboration with an IT service company in the Electric Power industry.
dc.format.extent 10 pages
dc.language.iso eng
dc.relation.ispartof Proceedings of the 50th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject architecture landscape
dc.subject Big Data
dc.subject eco-architecture
dc.subject value engineering methodology
dc.subject big data value discovery
dc.title Big Data Value Engineering for Business Model Innovation
dc.type Conference Paper
dc.type.dcmi Text
dc.identifier.doi 10.24251/HICSS.2017.713
Appears in Collections: Big Data Engineering Minitrack


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