Research

Publication (Conferences and Seminars)

Big Data Value Engineering for Business Model Innovation

Chen, Hong-Mei; Kazman, Rick; Garbajosa Sopeña, Juan; González, Eloy
Abstract:
Big data value engineering for business model innovation requires a drastically different approach as compared with method s for engineering value under existing business model s . Tak ing 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 tigh tly 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 flu id, requirements are ill - defined, many stakeholders are unknown , desig n 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.
Year:
2017
Type of Publication:
Conferences and Seminars
Keywords:
Architecture landscape, Big Data, Eco architecture, Value engineering methodology, Big data value discovery
Organization:
50th Hawaii International Conference on System Sciences, {HICSS} 2017
ISBN:
978-0-9981331-0-2
DOI:
10.24251/HICSS.2017.713