Predictive Analytics encompasses methodologies that leverage statistical algorithms and data mining to forecast future outcomes based on historical data. Even though Predictive Analytics is a growing field, its technology adoption is more prominent in big tech companies and young startups. This academic article explores the design, development, implementation and adoption of predictive forecasting applications as human-centric digital technologies and with socio-technical alignment. This study first examines the socio-technical context of these software systems which support at least one business process or logically related task to execute or achieve specific business objectives in data-driven decision-making. We start with a structured literature review which was conducted to gain an overview of analytics business applications in the current literature. After that, we conducted qualitative expert interviews which investigate the factors of technical adoption. The study design discusses Diffusion of Innovation~(DOI) theory, the Technical Acceptance Model~(TAM), Algorithmic Aversion and the Technology-Organization-Environment~(TOE) framework. We conduct an extensive qualitative study which shows the factors which facilitate predictive algorithms with business application data and we develop a comprehensible framework with practical guidelines for the use of Predictive Analytics and cloud-based Business Analytics.