Pie chart: normalize issue count over customer tools

Hi there.

I am implementing a Pie chart that shows the issue count for each customer we have in the company. Each customer has a certain amount of tools running, some may have 5 and other 15 and the issue count is simply the sum of all issues that were created in a certain time period for all tools for the customer.

If i look at the measure Issues created for the pie-chart with the Dimension “Tool” which contains the Customers and within them their respective Fleet, i get a Pie chart that shows absolute numbers. So independent of the amount of tools a customer has, i get all the issues created during the selected time period shown in the pie chart.

Now i would like to take this issue count for each customer and divide it by the number of tools this customer has, so i get a normalized pie chart that considers that more tools also produce more issues and the absolute values of issues is not the best indicator for quality.

I have created a new measure:

[Issue Type].[Issue]/

where i divide the issues by the aggregate of all CurrentMembers of the [Tool] Category.

So the [Tool] Category has [Customer1], [Customer2] and so on as sub-categories and these contain different amount of Tools/Machines.

I am not sure if my calculatoin is correct in fetching the number of tools that each customer has and divides each issue from that customer by the number of tools.

Please find attached also an explanation image:

so each of these customers have a specific amount of tools and in total they create a specific amount of issues during the selected time period. and i want to divide the amount of issues by the number of tools in each customer category.

Thanks for the help!
Best regards


What is the type for the Jira custom field “Tool”?
is that perhaps a cascading Jira custom field with two levels?
If so you could create a new calcualted measure using this formula:

[Measures].[Issues created]

[Measures].[Issues created],

It would divide the issues created for each column by the total number of machines in the tool (total from all weeks and from all users) that are imported in eazyBI (which means to count machines that are used among imported issues you import in eazyBI cube)

Martins / eazyBI