Strategic Prototyping for Developing Big-Data Systems
May 2016 • Presentation
Rick Kazman (University of Hawaii), Serhiy Haziyev (SoftServe, Inc.), Hong-Mei Chen (University of Hawaii), Olha Hrytsay (SoftServe, Inc.)
This session presents RASP (Risk-based, Architecture-centric Strategic Prototyping), a model for cost-effective risk management in Agile and Big Data development.
Software Engineering Institute
This presentation was created for a conference series or symposium and does not necessarily reflect the positions and views of the Software Engineering Institute.
Conventional evolutionary prototyping for Small Data system development is inadequate and too expensive for identifying, analyzing, and mitigating risks in Big Data system development. This article presents RASP (Risk-based, Architecture-centric Strategic Prototyping)—a model for cost-effective, systematic risk management—and shows how it is deployed in Agile and Big Data system development. The RASP model advocates using prototyping strategically and only in areas that architecture analysis cannot sufficiently address. In RASP, less costly MVP (minimum viable product), throw-away, and vertical evolutionary prototypes are used strategically, instead of blindly building full-scale prototypes. The RASP model is validated in an embedded case study of nine Big Data projects with a global outsourcing firm. A decision flowchart and guidelines distilled from lessons learned for whether, when, and how to do strategic prototyping are provided.