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Here's a conundrum to discuss. We want to make Joint Monitoring Programme data on SDG achievement in WASH available to our users through custom tables, and we want to make it easy to turn into data that makes sense. But the way the data is structured makes it really hard for a standard user to visualize correctly.
The country level table is the key one we want to show. But the data contains a lot of dimensions and it splits each country's achievement in multiple ways. So for example the residence type field has options for urban, rural and total. The year field is stored as a number, etc.
So if a user tries to find the service level of water coverage for a given country, they have to know to do a lot of things at the moment: Narrow the data down - or split it - by country, by residence type, by service type, and by year for the coverage value to start making sense. Otherwise it will show a bizarre total.
So the challenge for us is to see if we can make it much easier for a user to arrive at sensible visualizations. John suggests this could be something at the expression / data source selection level where we make JMP data its own special thing. Any thoughts?
Happy to have a meeting to discuss.
The text was updated successfully, but these errors were encountered:
Here's a conundrum to discuss. We want to make Joint Monitoring Programme data on SDG achievement in WASH available to our users through custom tables, and we want to make it easy to turn into data that makes sense. But the way the data is structured makes it really hard for a standard user to visualize correctly.
We have JMP data already in custom tables:
https://portal.mwater.co/#/tables/ts110/t5
https://portal.mwater.co/#/tables/ts110/t6
The country level table is the key one we want to show. But the data contains a lot of dimensions and it splits each country's achievement in multiple ways. So for example the residence type field has options for urban, rural and total. The year field is stored as a number, etc.
So if a user tries to find the service level of water coverage for a given country, they have to know to do a lot of things at the moment: Narrow the data down - or split it - by country, by residence type, by service type, and by year for the coverage value to start making sense. Otherwise it will show a bizarre total.
So the challenge for us is to see if we can make it much easier for a user to arrive at sensible visualizations. John suggests this could be something at the expression / data source selection level where we make JMP data its own special thing. Any thoughts?
Happy to have a meeting to discuss.
The text was updated successfully, but these errors were encountered: