BUS610: Business Intelligence and Analytics

The relationship between the BDA methods presented in Analytics, G. Data Classification and Analysis-Governance Analytics Knowledge Base was based on the difficulty criteria, the methods were ranked according to their difficulty and value. For example, the descriptive method is considered the easiest stage with the least value compared with the other three methods, whereas the prescriptive method has the highest value and difficulty. The research in this paper will be based on the concepts in Make better decisions and Analytics, G. Data Classification and Analysis-Governance Analytics Knowledge Base to develop the method analytical categories. Figure 4 illustrates the analytical categories based on the relation among the four methods. It shows the outcome from one method which will be the input for the next one. It also represents a development in the analytical level by finishing each stage and providing deeper analysis for the problem, as described below with supported examples adopted from Business Analytics and Intelligent: An Introduction and Consideration for Getting Started. Information Services and Technology.

Figure 4. Analytical methods category.

Stage One: Descriptive analytics stage where the information is categorised, for example gaining insight from historical data. For example, a healthcare provider will learn how many patients were hospitalised in the previous month.

Stage Two: Diagnostic analytics stage includes the data aggregation, reporting, drilling, and clustering of the data into subcategories. For instance, a healthcare provider compares patients' response to a promotional campaign in different regions.

Stage Three: Predictive analytics stage is an important stage because it helps to designate the future outcomes, by using statistical and machine-learning techniques. This stage is where the data will be analysed, classified, simulated, and summarised to give a prediction; and it gives a good understanding for the forthcoming events. For example, a health care provider predicts the risk of investing in new medical devices based on registered patients.

Stage Four: The final prescriptive analytics stage is the optimisation stage, where the best framework will be given to secure the best futuristic outcome. It also involves taking decisions using deeper learning techniques. For example, in the health care industry, you can better manage the patient population by using prescriptive analytics to measure the number of patients who are clinically obese, then add filters for risk factors like diabetes and low-density lipoproteins (LDL) cholesterol levels to determine where to focus treatment.

Figure 4 shows a well-built structured correlation chain between the analytical methods based on the four stages. It is clearly shown that these methods cannot function in parallel besides the methods that do not intersect between each other.