How exactly does the analysis for discrete stories work? This article will explain the different analytic types that power each sentence in your narrative.
Discrete stories allow the user to compare values and understand the distribution of the data. The narrative will analyze distribution, averages, totals, and groupings/clusters across the data.
Take the sample narrative below, which measures Sales by product:
Let's review what analytic type each bullet uses.
The first bullet calculates the total value of your measure.
The second bullet calls out the dimension drivers. In this example, the dimension drivers are the products that contributed the most to total sales.
The third bullet uses range analysis to call out the smallest and largest values.
The next couple of bullets analyze the distribution of your data. This will analyze how the data is skewed, averages, medians, and concentration of data (if any). This answers questions like: how balanced are these grouped variables compared to one another?
This bullet uses clustering analytics to call out any measures that can be grouped together. Clustering answers questions like: are there any distinct groups that stick out in the data?
The last bullet mentions any notable outliers.
As you can see, discrete story analytics includes distribution, averages, totals, and groupings/clusters.
Note the number of bullets in your narrative can vary depending on your chosen verbosity.