Artificial intelligent system is utilized for a variety of applications. One of the significant areas such systems are utilized across the Department of Defense and in the financial sectors is the analysis, characterization, and classification of large, heterogeneous, complex data sets. Current and future applications are required to grow in complexity and capability as the data requiring analysis continuing at an exponential rate, creating a serious challenge to operators who monitor, maintain, and utilize systems in an ever-growing network of assets. The growing interest in autonomous systems with cognitive skills to monitor, analyze, diagnose, and predict behaviors in real time makes this problem even more challenging. Systems today continue to struggle with satisfying the need to obtain actionable knowledge from an ever-increasing and inherently duplicative store of non-context specific, multi-disciplinary information content. Additionally, increased automation is the norm and truly autonomous systems are the growing future for atomic/subatomic exploration and within challenging environments unfriendly to the physical human condition. Simultaneously, the size, speed, and complexity of systems continue to increase rapidly to improve timely generation of actionable knowledge. Presented here are new concepts and notional architectures for a Big Data Analytical Process (BDAP) which will facilitate real-time cognition-based information discovery, decomposition, reduction, normalization, encoding, memory recall (knowledge construction), and most importantly enhanced/improved decision-making for big data systems.