Analytics
Descriptive analytics tells the user what happened in the past. It helps the company know its performance while providing information to its stakeholders because it allows for proper guidance during the interpretation of data from the research (Bell, Raiffa, & Tversky, 2018). Predictive analytics is where the data collected is fed in machine learning tools to help in predicting what might happen in the future (Huisman, 2017). To ensure that stakeholders understand descriptive data quickly, the data is presented using visual aids like dashboards, charts, graphs, and reports. An example of descriptive statistics in the health sector where there is an increased volume of people admitted in the emergency room within a short period can be explained well using descriptive data. In this case, descriptive data can provide the actual data needed to understand the scenario by giving the exact number of people admitted, the date admitted, the patients’ details, reasons why they were admitted, and the care provided to them, among other factors. With such information available to the stakeholders, they can make an informed decision using the descriptive statistics. An example of predictive data in the hospital example is whereby the research can predict that there would be an increase in the number of patients to be admitted in the emergency room in a given period.
Prescriptive analytics recommends to the researcher actions he or she can take to bring into effect the research outcomes (Bell, Raiffa, & Tversky, 2018). Therefore, prescriptive analytics is essential since it gives historical data derived from descriptive and predictive data meaning by guiding different courses of action and provide direction on the possible suggestion for every outcome (Huisman, 2017). For example, prescriptive analytics, therefore, in our case where the researcher had identified that there was an increase in the number of admissions in the emergency room it can suggest increasing the number of staff who can handle the increased number of patients or increasing the bed capacity to cater for the increased number of patients in the predicted period.