Using Polaris in Reporting models
Hi, We've been advised to use Polaris for one of our reporting models (90% sparcity). The model is mainly used to aggregate data across 4 dimensions and time, using SUM as the summary method.
One of our dimensions is a 12-levels hierarchy, and I just saw in anaplan learning that Polaris might not be effective in reducing volumetry when using such lists.
Do you have any advice/feedback ?
Thank you !
Answers
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It totally depends…Without knowing more about the 12 level hierarchy, it is difficult to tell. With that said, one of the reasons you have that 12 level hierarchy is to reduce the overall sparsity. In Polaris, you may not need to have so many levels as you would be modeling more "naturally". Also, with OnDemand Calc (ODC) in Polaris, that could definitely help "end of chain" calculations (meaning calcs for dashboards/reporting).
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@stheapaut with 90% sparsity with 4 dimensions I would highly recommend Polaris based on experience. Just curious how you managed to measure 90% sparsity though. In my case, I actually use Polaris to measure the level of sparsity lol. I'm assuming that the 4 dimensions do not have composite lists, e.g. List A is a combination of product and customer which is what you usually end up doing to manage minimise workspace consumption in Classic.
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@TristanS @rob_marshall Thank you for your answers !
The main list is a 12 levels composit list, with no possibility to reduce it. We add items every month (1 to 10 items) that's why I'm quite unsure about using Polaris in our case.
For estimated sparsity it was measured by Anaplan directly
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