  Objective. We propose an approach to reason about goals, obstacles, and to
select suitable big data solution architecture that satisfy quality goal
preferences and constraints of stakeholders at the presence of the decision
outcome uncertainty. The approach will highlight situations that may impede the
goals. They will be assessed and resolved to generate complete requirements of
an architectural solution. Method. The approach employs goal-oriented modelling
to identify obstacles causing quality goal failure and their corresponding
resolution tactics. It combines fuzzy logic to explore uncertainties in
solution architectures and to find an optimal set of architectural decisions
for the big data enablement process of manufacturing systems. Result. The
approach brings two innovations to the state of the art of big data analytics
platform adoption in manufacturing systems. Firstly, A systematic goal-oriented
modelling for exploring goals and obstacles in integrating manufacturing
systems with data analytics platforms at the requirement level and, secondly, A
systematic analysis of the architectural decisions under uncertainty
incorporating the preferences of stakeholders. The efficacy of the approach is
illustrated with a scenario of reengineering a hyper-connected manufacturing
collaboration system to a new big data architecture. Keywords. big data, big
data analytics platforms, manufacturing systems, goal-oriented modeling, fuzzy
logic
