Publicación (Conferencias y Seminarios)

Big Data Value Engineering for Business Model Innovation

Chen, Hong-Mei; Kazman, Rick; Garbajosa Sopeña, Juan; González, Eloy
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.
Tipo de publicación:
Conferencias y Seminarios
Palabras clave:
Architecture landscape, Big Data, Eco architecture, Value engineering methodology, Big data value discovery
50th Hawaii International Conference on System Sciences, {HICSS} 2017