The business environment is constantly changing, and it is becoming more complex. Organizations, both private and public, are under pressures that force them to respond quickly to changing conditions and to be innovative in the way they operate. Such activities require organizations to make frequent strategic, tactical, and operational decisions. Making such decisions may require considerable amounts of relevant data, information, and knowledge. Processing these must be done quickly and frequently.

To help organizations make their decisions, discuss one type of analytics (descriptive, predictive, OR prescriptive analytics) in detail with an example. You would need to cite at least 1 article outside of the given readings.

 

Prescriptive analytics emerged after the arrival of descriptive and predictive analytics. It is still fresh in the business environment, with several organizations still fearing its complexity. Moreover, its use requires proficiency in machine learning which researchers report to have multifaceted requirements. According to Lepenioti et al., prescriptive analytics is a process that encompasses the use of statistical methods based on computational findings of algorithmic models essential in decision making and generation of recommendations. This paper provides a detailed discussion on the basics of prescriptive analytics and its relevance to the modern business environment using a relevant example.

            Prescriptive analytics is different from both descriptive and predictive analytics. It is the last phase used by organizations determined to find the most appropriate solution to their problems based on "what the organization knows" in terms of data. This new field guarantees users the opportunity to prescribe a range of possible actions towards attaining the best courses of action to a problem the organization faces in its practices. Unlike the other two types of analytics, prescriptive analytics generates recommendations from several tools such as algorithms, machine learning, business rules, and computational modeling techniques. Lepenioti et al. suggest that organizations that have mastered the use of prescriptive analytics have shifted their focus towards data, which is the key to the success of this analytic. In the current business environment, businesses have various data that make the success of prescriptive analytics fairly complex to administer.

            The fundamental purpose of a prescriptive analytic process is to help an organization generate automated decisions. However, to generate reasonably accurate decisions requires the availability of a variety of data, including real-time data feeds, big data, and past and present transactional data linked to the problem that requires a solution. Using prescriptive analytics without adequately identifying the existing situation and the relevant data specific to the problem may lead to the organization obtaining poor decisions and recommendations. The issues may arise since this type of analytics uses statistical models, machine learning, algorithms, and computational modeling techniques to attain automated solutions.

              Some managers have used prescriptive analytics to obtain and use solutions that negatively affect the business' performance. Researchers have now helped solve this problem by advising managers to use prescriptive analytics to get advice on what action they should take to solve the underlying problem. An example of a situation where prescriptive analytics apply is when a training manager discovers that employees lacking practical communication skills may fail to complete a newly launched project course after using predictive analytics. The manager then wonders what action the organization should take. It is at this point that prescriptive analytics apply. The manager can use prescriptive analytics to help in generating options for action. By using prescriptive analytics, an algorithm might detect employees requiring the new course but lack communication skills. Prescriptive skill can then send an automated recommendation that the employees lacking the particular skill take additional training to acquire the skill before taking the course.