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Big Data Value Engineering for Business Model Innovation

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Title: Big Data Value Engineering for Business Model Innovation
Authors: Chen, Hong-Mei
Kazman, Rick
Garbajosa, Juan
Gonzalez, Eloy
Keywords: architecture landscape
Big Data
eco-architecture
value engineering methodology
big data value discovery
Issue Date: 04 Jan 2017
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.
Pages/Duration: 10 pages
URI/DOI: http://hdl.handle.net/10125/41877
ISBN: 978-0-9981331-0-2
DOI: 10.24251/HICSS.2017.713
Rights: Attribution-NonCommercial-NoDerivatives 4.0 International
Appears in Collections:Big Data Engineering Minitrack



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