Guided analytics in practice
In the previous article, we described the existing types of analytics. As you may remember, the descriptive analytics answers the question “What happened?” at a particular point in time or for any period, while the diagnostic analytics answers the question “Why did it happened?”. Diagnostic analytics is aimed at identifying the main factors that influenced the analyzed event, in particular, using statistical methods of data analysis. For example, why did the sales of certain products decrease? The methods applied for diagnostic analysis will determine whether such a decline was affected by growing prices, a decrease in household incomes, or seasonality. Or, perhaps, the reason is quite simple, and the decline is caused by the only fact that sales have fallen in one particular outlet due to a change of seller or changing opening hours? To discover the reason, the fine-tuned dashboards may be sufficient without the need for complex statistical methods. In this case, the guided analytics comes to help.
In this article, we would like to dwell on what guided analytics is and share our experience. First, let's look at the following case.
There is an analytical system (our solution for the Ministry of Health of Saudi Arabia), which, among other issues, analyzes mortality by groups of clinics.
According to the graph (Figure 1), the Eastern Cluster demonstrates the highest mortality. What is the reason? First of all, it is necessary to define which of the clinics shows the highest value. To do this, the system is configured to drill down to specific clinics of the selected group. The graph (Figure 2) clearly visualizes the most “problematic” clinic.