Deep analytics is an intelligent, complex, hybrid, multi-phased and multi-dimensional data analysis system. The basic steps of computation are data sourcing, data filtering/preprocessing, data ensembling, data analysis and knowledge discovery from data. The authorized data analysts select an optimal set of input variables, features and dimensions (e.g. scope, system, structure, security, strategy, staff-resources, skill-style-support) correctly being free from malicious attacks (e.g. false data injection, shilling); input data is sourced through authenticated channels accordingly. The sourced data is filtered, preprocessed (e.g. bagging, boosting, cross-validation) and ensembled. It is rational to adopt an optimal mix of quantitative (e.g. regression, prediction, sequence, association, classification and clustering algorithms) and qualitative Scope Structure Strategy Skill-Style-Support Staff-Resources Security System Collective intelligence Security intelligence Collaborative intelligence Business intelligence Machine intelligence Input data Technical Input data Business Data sourcing & filtering Data ensembling Deep Analytics Knowledge discovery from data (KDD) Analytics Quantitative analysis Big data, Prediction, Classification, Clustering, Time series, Sequence, Association, Collaborative analytics Qualitative analysis Perception Case-based reasoning SWOT Reference of document: EBOOK/ SCHAKRABORTY/BA/ V3.0/15082019 Page 5 (e.g. case-based reasoning, perception, process mapping, SWOT, CSF and value chain analysis) methods for multi-dimensional analysis. The analysts define intelligent training and testing strategies in terms of selection of correct soft computing tools, network architecture – no. of layers and nodes; training algorithm, learning rate, no. of training rounds, cross-validation and stopping criteria. The hidden knowledge is discovered from data in terms of collective, collaborative, machine, security and business intelligence. The analyst's audit fairness and correctness of computation and also reliability, consistency, rationality, transparency, and accountability of the analytics.