Big Data as it exists today is not necessarily a tool that creates the Big Picture. Most systems that exists today are designed based on predicated methods. These rely on known variables, the only constraint unknown are the values of the variables. Predicated system are constrained to create newer insight as they are bounded by system boundaries defined by known variables. The new emergent variables can be considered as the non-causal agents until the newer order of the complex system begins to emerge; in which the newer variables manifests.
To develop wider and deeper insight about the behavior of the complex system as the system driven by entropy moves from one order to next; it does require to bring into its realm the possible newer variables. Lack of adequate techniques (Bayesian network included) in discerning all the variables that affect the system behavior creates uncertainty. Predicated system as such are blind sighted to such uncertainties. This creates inadequacies in methods when dealing with the randomness in the system. Emergence of newer variables that begin to affect the system behavior become the newer causes. Predicated system cannot hone on the variables that lie dormant as the non-causal agents.
The hidden non-causal agents can be discovered by creating 2nd order generative semantic models. Predicative systems as such are not designed to dramatically alter its behavior; but only reflect the behavior constrained by those variables considered in its initial design