Big data analytics is the process of examining big data to uncover hidden patterns, unknown correlations and other useful information that can be used to make better decisions.
Big data usually includes data sets with sizes beyond the ability of commonly used software tools to capture, curate, manage, and process data within a tolerable elapsed time.
Big data uses inductive statistics and concepts from nonlinear system identification to infer laws (regressions, nonlinear relationships, and causal effects) from large sets of data with low information density to reveal relationships, dependencies and perform predictions of outcomes and behaviors.
Simulation can provide outstanding flexibility that allows for the foundation of a model that graphically depicts the work flow. Data elements can be customized within the model to interface with existing data collection arrangements to offer improvement in the outputs as new information is usable. Every business understands the affect that variability has on its system. Modern simulation tools have the unique ability to account for variation in the workplace through its Intelligent Object modeling and a risk-based planning tool that distinguishes what the business needs versus what the business is realistically capable of achieving. As a result, these tools can be utilized to serve bring down the dependence on manual human judgment and cut down the impact of knowledge lost from employee turnover.
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