In this guide, we will go through how to characterize measures and add relationships between measures in the settings module. You can also watch the video on Characterizations Settings to learn more.
You can find measure characterizations by clicking the "Characterizations" tab in the Settings Modal. Once inside characterizations, you will be able to supply useful context to the extension to allow you to receive much more insightful narratives. We will go through each one of the settings.
1) 'How would you describe your data' - This allows a user to change the type of story produced without having to start from scratch. In this case, we are looking at sales by month (a continuous dimension analysis), so we will leave that.
2) 'Consider values to be' - Here, the narrative will adjust based on what the measure is. By default, the narrative will assume that all measure values are numbers and won't perform any additional calculations or apply any special rendering rules.
- If the values are 'Percentage' values and the user chooses "Percentage", the narrative will automatically multiply all of those measure values by 100 and append a "%" to the end of each value. In addition, rather than focusing on 'percent changes,' as would occur with numerical values, the narrative will talk more logically about 'percentage point differences'.
- If the values are 'Money', a drop-down will appear that allows users to select their preferred currency formatting option. The narrative will automatically add this formatting (e.g., “$” prefix to values for USD), and use cleaner references to the values (for example, '$1.3 million instead of $1,300,000.00')
3) 'Larger values are considered': This setting allows you to inform the extension how to treat upward movements in a measure. For example, revenue going up is usually considered a 'good' thing. Costs going up might be considered 'bad'.
4) 'Do you want to know the cumulative total' - By default, continuous analysis narratives will call out the total of the measure across the series (for example, 'the total sales across all 12 months was $1.5 billion"). However, for certain types of measure values, this analysis is not relevant. For example, if you are analyzing the daily closing stock price of a stock, you may not want to know what the sum of all the daily closing prices for the year was. In that case, you can toggle this setting to 'No.' You can also select “My data is already cumulative.”
In this example, we are measuring 'sales', which we consider 'money' and select ‘US Dollar.’ Larger values are considered 'good' and we would like to know about the total across all the periods. Our settings would look like the above. When we click the back arrow to return to the narrative, our story will look like the below: