Prescriptive Descriptive and Predictive Analytics
Prescriptive, descriptive, and predictive analytics are types of analytics that offer solutions on the market conveniently so that they can leverage business data. Though many answers seem to cover the different categories of analytics, many of them can be challenging to deal with in the business market. The three types and how they are related to one another form of data analytics that each uses data to answer a different question. Prescriptive analytics is analytic that takes data that is predictive to the next level. In contrast, predictive analytics is entitled to take historical data and to compute it into a machine learning model that contemplates critical trends and patterns. The application of the model to current data enables prediction of anything that will happen afterward. As for descriptive-analytic views, data statistically and tells what happened in the past. Descriptive analytics, through data visualizations, helps a business to know how it is performing. This is through the provision of context that allows stakeholders to interpret
information (Lepenioti et al., 2020).
Static models are characterized by estimation of resource deployment at compilation time, while dynamic models predict task performance while the program is running. The difference between static and dynamic when dealing with job run is that static models are not required, but operational data is required. Static models need automatic data sampling and manipulate the input data; that is, if the size can be set on. In contrast, a dynamic model is characterized by accepting automatic data sampling or a data range. The evolution of one model to another depends on the stiffness matrix updates to meet the demand of the other model. Secondly, the distribution of node masses preliminarily in light of the mass, element volume, center of mass of parts of the model, and inertia moment. Finally, optimization of the collected group using a modal parameter (Berg and Essen, 2019).
An optimistic and pessimistic approach to decision making under assumed uncertainty differs in that optimistic opt to choose an activity that is likely to yield the maximum payoff possible, that is, by comparing all the alternatives and picks the maximum of the available maximums. In contrast, the pessimistic approach takes a criterion that stands for choice between alternative courses of action, which assumes a pessimistic nature view. Furthermore, it picks the alternative with a minimum payoff, the one with a maximum alternative minimum.
Solving problems under uncertainty sometimes involves assuming that the problem is to be solved under conditions of risk. The decision-maker understands the situation and the alternatives to which there is no guarantee of how the available solution will work. Also, with uncertainty, the information is insufficient even to assign any probability of the likely outcome of the alternatives.
Preventing terrorist attacks U.S. Department of Homeland security in the war against terrorism uses dynamic models which operate on data gathered about the terrorists, this help to prevent future episodes through the identification of conditions for their appearance and also predicting the forms that they may take their locations and time of terror attack (White, 2016). This has been assimilated by other governments and agencies.
REFERENCES
Berg, P. L. V. D., & Essen, J. T. V. (2019). Comparison of static ambulance location models. International Journal of Logistics Systems and Management, 32(3-4), 292-321.
Lepenioti, K., Bousdekis, A., Apostolou, D., & Mentzas, G. (2020). Prescriptive analytics: Literature review and research challenges. International Journal of Information Management, 50, 57-70.
White, J. R. (2016). Terrorism and homeland security. Cengage Learning.