Review of Agile SDLC for Big Data Analytics Systems in the Context of Small Organizations Using Scrum-XP

Abstract

Software development using agile System Development Life Cycles (SDLC), such as Scrum and XP, has gained important acceptance for small businesses. Agile approaches eliminate barriers to required organizational, technical, and economic resources usually necessary when rigorous software development approaches, through heavyweight methodologies (e.g., Rational Unified Process (RUP)) or heavyweight international standards (e.g., ISO/IEC 12207) are used. However, despite their high popularity in small businesses, their utilization is scarce in the emergent domain of Big Data Analytics Systems (BDAS). Consequently, small businesses interested in deploying BDAS lack systematic academic guidance regarding agile SDLC for BDAS. This research, thus, addresses this research gap, and reports an updated comparative study of three of the main proposed SDLCs for BDAS (Cross-Industry Standard Process for Data Mining CRISP-DM), Two mains were Microsoft Team Data Science Process (TDSP), and Domino Data Science Lifecycle (DDSL)) in the current BDAS development literature, against a Scrum and Extreme Programming (Scrum-XP) SDLC. For this aim, a Pro Forma of a generic Scrum-XP SDLC is used to examine the conceptual structure, i.e., roles, phases-activities, roles, and work products-of these two SDLCs. Hence, this comparative study provides theoretical and practical insights on agile SDLC for BDAS adequate for small businesses and calls for further conceptual and empirical research to advance toward an agile SDLC for BDAS supported by academia and used in practice.