Big Data (BD) innovations have become the key differentiators in any competitive analyses, and has become/is, a lifestyle. Building on the Diffusion of Innovation theory, Technology Acceptance Models 1, 2, 3, and the International Requirements Engineering Boards’ (IREB) Requirements Engineering framework, this study aims to identify perceptions towards BD innovations, to design agile feature-based and rule-based intelligent systems, adaptable to progressive changes over time in the future. To reduce variable bias, random sampling of two overlapping demographics is carried out, over two time periods. The first case study, involves perceptions towards BD Innovations, blockchain, Augmented Reality/Virtual Reality/Mixed Reality. The second case study relates to perceptions towards intelligent assistants, e.g. Alexa. For the first case study, by triangulating correlation analyses, linear regression analyses, k-means and hierarchical agglomerative clustering (HAC) results, findings reveal different degrees of acceptance/caution among age groups and gender, and the influence of job prospects on perceptions. Furthermore, HAC’s profile means is more explanatory in terms of distribution and number of clusters, while k-means’ profile means is more positive than HAC’s. For the second case study, findings indicate that personality/dispositions, Technology Acceptance Model’s (TAM) adjustment and anchor, as well as interaction styles, may influence perceptions, more than gender or ability. Together, the two case studies provide age-progressive requirements analyses, and an opportunity tree. The fuzzy characterizations can be used in computational thinking design and matrix factorization in (fuzzy) rule-based design for seniors. However, sample size is small and thus, not generalizable.