Your response should be 250-300 words. Respond to two postings provided by your classmates. There must be at least one APA formatted reference (and APA in-text citation) to support the thoughts in the post. Do not use direct quotes, rather rephrase the author’s words and continue to use in-text citations.
Discussion 1:
Predictive analytics is the next step involved in data reduction, which enables data analysts to make predictions regarding the future. These predictions are made based on prior patterns in big data analysis. The critical difference between predictive analytics and descriptive is that it incorporates specific techniques such as machine learning and data mining to study existing data. The analyzed information can either be recent or historically, which allows analysts to make future forecasts. However, predictive analytics do not accurately predict future events because they are probabilistic (Deka, 2016). An example of this form of big data analytics is sentiment analysis, which computes likely output scores in the future.
On the other hand, prescriptive analytics is a form of predictive analytics, although it’s the current emerging technology in big data analytics. It suggests several courses of action that analysts can assess based on the indicated probable outcomes of every decision. Unlike predictive analytics, prescriptive forecasts multiple futures for extensive decision-making options. Although prescriptive data predicts the future, it can only achieve this goal through to components; actionable data and a feedback system (Deka, 2016). For example, a prescriptive model can track the outcome by predicting the consequences of each action, thus able to recommend the most suitable course of action.
Descriptive analytics is the first simplest category of big data analytics among predictive and prescriptive analytics. It allows a user to divide data into smaller parts that are highly useful information. The purpose of descriptive analytics is to summarize occurrences, for example, social analytics (Sharda, Delen, Turban, Aronson, and Liang, 2014). Descriptive analytics assess simple event encounters that represent raw data, such as the number of followers or posts on a social media platform. It mainly processes data into results that can be interpreted at a glance without high complexity in data analytics.
References:
Deka, G. C. (2016). Big data predictive and prescriptive analytics. In Big Data: Concepts, methodologies, tools, and applications (pp. 30-55). IGI Global.
Sharda, R., Delen, D., Turban, E., Aronson, J., & Liang, T. (2014). Business intelligence and analytics. System for Decision Support.
Discussion 2:
Prescriptive analytics is the type of analytics that uses data sets from the community to generate predictions based on community sentiment, users, and a collection of attributes from the users. This gives us the ability to develop predictive analytics on user and group behaviour. However, the difference is that prescriptive analytics gives us the ability to build predictive analytics without having a dataset from the community. Predictive analytics gives us the ability to build models that want to build from data sets. This allows us to build predictive analytics without having to have a model from the community. Descriptive and predictive analytics are more sophisticated statistical methods that allow for a higher degree of specificity (Ansari, Glawar, & Sihn, 2020). They are used as tools for making certain decisions about future earnings growth. They can also help inform on different types of investing strategies.
The most practical use of predictive analytics for enterprise customers is to find trends from customers. This allows insights into customer habits, marketing strategies, and customer behaviour to help build an enterprise-wide strategy and improve customer outcomes.
Predictive analytics is generally considered to be a complementary tool for analytics and predictive technology to improve analytics strategy and product development. The traditional statistical method is usually based on simple models and simple rules. This means that there is not enough control of variables, and statistical analysis is not easy. However, some of the best predictive analytics tools are based on the theory of stochastic systems and are based on predictive analytics. This is called stochastic or recursive methods and provides better control over the variables (Sharda et al., 2020). In the recursion method, variables are given as an input in order to obtain the prediction of the future. The results of predictive analytics can help to understand the patterns of customers from a predictive perspective and helps to improve their business.
References:
Ansari, F., Glawar, R., & Sihn, W. (2020). Prescriptive maintenance of CPPS by integrating multimodal data with dynamic bayesian networks. In Machine Learning for Cyber Physical Systems (pp. 1-8). Springer Vieweg, Berlin, Heidelberg.
Brauer, R., Wong, I. C. K., Man, K. K., Pratt, N. L., Park, R. W., Cho, S. Y., … & Schuemie, M. (2020). Application of a Common Data Model (CDM) to rank the paediatric user and prescription prevalence of 15 different drug classes in South Korea, Hong Kong, Taiwan, Japan and Australia: an observational, descriptive study. BMJ open, 10(1).
Sharda, R., Delen, Dursun, and Turban, E. (2020). Analytics, Data Science, & Artificial Intelligence: Systems for Decision Support. 11th Edition. By PEARSON Education. Inc.
