The Application of Three Analytics - Reporting, Predictive Modeling, and Prescriptive Analytics - to Business – Case Study Example

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The paper “ The Application of Three Analytics   - Reporting, Predictive Modeling, and Prescriptive Analytics – to Business” is a   thoughtful variant of case study on information technology. Large corporations are increasingly facing different challenges that require major decisions to be made from time to time. Several factors are responsible for business pressure and hence the need for appropriate decision-making techniques. Analyzing and solving a problem may take a lot of time and resources depending on the model that is used. This, therefore, requires a proper understanding of the modeling techniques. During the decision-making process, different alternatives have to be selected.

This is for the purposes of ensuring that the best alternative for solving the problem is selected. The increased competition in the market requires large corporations and other businesses to develop a response and support for countering the competition. Business pressure response and support models are in place although it requires some form of expertise (Barton & Court, 2012). Technology has also been introduced in the analytical models for making a decision and solving the problems associated with business pressures. Some of the analytics technology commonly used for countering the business pressures includes predictive analytics, prescriptive analytics, and reporting.

The paper discusses the application of three analytics in relation to the case study. Predictive analyticsPredictive analytics is one of the techniques that are used by large corporations in response to business pressures. This technique involves the use of a variety of modeling, data mining, and machine learning for the analysis of current issues facing an organization in order to make predictions about the future. The predictive analytics mainly deals with the transactional data within the organization for identifying the possible risks and opportunities.

The pressures in most cases usually result in the risks which may affect the competitiveness of the corporation. When using this technique, the business is able to obtain a predictive score for each individual aspect of the business which is related to the business pressure that the organization is facing (Larose, et al, 2015). The probability is usually used to determine the potential of the business units to overcome the pressure. The predictive score is usually provided for the customers, employees, product, organizational unit, or machine.

Some of the large corporations have used predictive analytics for the purposes of fraud detection. This is considering that fraud has a lot of negative impacts on the growth and development of the organization. The manufacturing companies also use this model for analyzing and solving the problems in the manufacturing processes. The extraction of information from the available data is usually carried out when using this method in order to predict trends and behaviors. Government agencies also use this technique for making predictions.

The quality of assumptions as well as the levels of data analysis is an important determinant of accuracy when using the predictive analysis.   Amazon is one of the largest and profitable corporations that are involved in the sale of goods and services through online methods. However, the company was facing a challenge in terms of delivering the products to customers on time. The shipping cost was also high when the products were only delivered as per the order of the customers. This had negative impacts on the ability of the company to satisfy the needs of the customers.

However, the company employed the use of predictive techniques for solving this problem. The company utilized the records, website traffic, and geographical; relevance data (Lindsey, et al, 2014). This was for the purposes of predicting the trends with regard to the needs of the customers. As a result of the predictive analytics, the company was able to develop a service called anticipatory shipping and it acquired a patent for the service. Through the use of this service, the company is able to ship products even before it is ordered by the customers.

Through the use of anticipatory shipping, the company starts to ship products to different regions even before any order for the products is made. The shipping is usually carried out with the anticipation that the products will be required in the regions that it has been shipped. This process makes it easy for the products to be delivered to the customers in a particular region. The delays, as well as increased shipping costs, are reduced through the use of this service.

The corporation is able to know that the products will be required in certain geographical areas as a result of predictive analytics.

References

Larose, D. et al. (2015). Data Mining and predictive analytics. London: John Wiley and Sons.

Waller, M. et al. (2010). Data Science, Predictive Analytics, and Big Data: A Revolution That Will Transform Supply Chain Design and Management. Journal of Business Logistics, Volume 34, Issue 2. Pp 77-84.

Lindsey, C. et al. (2014). Predictive Analytics to Improve Pricing and Sourcing in Third-Party Logistics Operations. Journal of the Transportation Research Board, Volume 2410 Issue 14.

Barton, D. & Court, D. (2012). Making Advanced Analytics work for you. Harvard Business Review, Vol 90, PP. 79-83.

Perugini, D. & Perugini, D. (2014). Characterized and Personalized Predictive-Prescriptive analytics using agent-based simulation. International Journal of Data Analysis Techniques and Strategies, Volume 6, Issue 3, pp. 209-227.

Chen, H. (2011). Smart market and money: trends and controversies. IEEE Intelligent system Vol 26, No 6, pp. 82-96.

Goodnight, J. (2011). The forecast for predictive analytics: hot and getting hotter. Statistical Analysis and Data Mining, Volume 4, No 1, pp. 9-10.

Sharda R, et al. (2013). Business Intelligence: A Managerial Perspective on Analytics. Prentice-Hall Press, Upper Saddle River.

McAfee, A. & Brynjolfsson, E. (2012). Big Data: The Management Revolution. Harvard Business Review, Volume 90, pp. 60-68.

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