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Descriptive Analytics
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Diagnostic Analytics
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- diagnostic methodologies use knowledge, usually extracted from historical data, to predict past, or otherwise unknown (e.g. to find out what happened or caused a particular cyber breach)
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Predictive Analytics
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- predictive methodologies use knowledge, usually extracted from historical data, to predict future, or otherwise unknown, events
- analytic techniques that fall into this category include a wide range of approaches to include parametric methods such as time series forecasting, linear regression, multilevel modeling, simulation methods such as discrete event simulation and agent-based modeling; classification methods such as logistic regression and decision trees; and artificial intelligence methods such as artificial neural networks and bayesian networks
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Prescriptive Analytics
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- prescriptive methodologies not only look into the future to predict likely outcomes but also attempt to shape the future by optimizing the targeted business objective while balancing constraints
- analytic techniques that fall into this category include optimization techniques such as linear programming, goal programming, integer/mixed-integer programming, and search algorithms; artificial intelligence optimization techniques such as genetic algorithms and swarm algorithms; and multi-criteria decision models such as analytic hierarchy process, analytic network, process, multi-attribute utility and value theories, and value analysis
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