Auto-granularity for multi-dimensional data

1. A method for configuring data granularities for a multi-dimensional data model, the method comprising:

storing planning granularities for data elements including a first granularity for a first data combination, the first data combination comprising a combination of multiple dimensions of data, wherein a user interface is configured to display data for the first data combination at the first granularity;

calculating a variance metric between planned data at the first granularity for the first data combination and actual data observed for the first data combination, wherein the observed data is stored at a different granularity than the planned data;

calculating a frequency of occurrence metric between planned data at the first granularity for the first data combination and actual data observed for the first data combination, wherein the frequency of occurrence metric comprises a frequency of occurrence of variance between the planned data for the first data combination at the first granularity and the actual data observed for the first data combination;

automatically adjusting the planning granularity for the first data combination from the first granularity to a second granularity when the variance metric meets or exceeds a first threshold and the frequency of occurrence metric meets or exceeds a second threshold; and

receiving a selection of a cross-section of data spanning a plurality of dimensions in the multi-dimensional data model, the selection including the first data combination, wherein a user interface is configured to display data for the first data combination at the second granularity based on the adjusting.

2. The method of claim 1 , wherein the first granularity is a monthly granularity and the second granularity is a weekly granularity.

3. The method of claim 1 , further comprising:

displaying, in a user interface, data for the first data combination at he second granularity based on the adjusting.

4. The method of claim 1 , wherein the planning granularity is automatically adjusted based on a preselected setting for the first data combination by a user.

5. The method of claim 1 , wherein the frequency of occurrence metric comprises a number of occurrences over a predetermined window where the variance metric meets or exceeds the first threshold.

6. The method of claim 1 , further comprising:

calculating a contribution metric for the first data combination, wherein the contribution metric comprises a percentage at which the first data combination contributes to a higher level data combination in the selected cross-section of data, and the planning granularity for the first data combination is adjusted from the first granularity to the second granularity when the variance metric meets or exceeds the first threshold and at least one of the frequency of occurrence metric meets or exceeds the second threshold or the contribution metric meets or exceeds a third threshold.

7. The method of claim 1 , further comprising:

receiving one or more driver dimensions of the multi-dimensional data model for the selection;

generating, based on the driver dimensions and the planning granularities for data elements, a data structure that comprises a plurality of rules for evaluating a validity of combinations that include a Period dimension of the multi-dimensional data model.

8. The method of claim 7 , further comprising:

determining that a subset of data combinations indicated by the selection of the cross-section of data are determined to be invalid based on the rules; and

performing combinations of data according to the selection, wherein the subset of data combinations is filtered from the performed combinations of data.

9. The method of claim 8 , wherein the rules are updated based on the planning granularity for the first data combination being adjusted, the updated rules indicating that the first data combination is valid for the second granularity and invalid for the first granularity.

10. The method of claim 9 , wherein combinations involving the first data combination and the first granularity are filtered from the performed combinations of data based on the updated rules.

11. The method of claim 6 , wherein the contribution metric comprises a percentage at which the first data combination contributes to a parent data combination in the selected cross-section of data according to a hierarchical structure for the multi-dimensional data model.

12. A non-transitory computer readable medium having instructions stored thereon that, when executed by a processor, cause the processor to configure data granularities for a multi-dimensional data model, wherein, when executed, the instructions cause the processor to:

store planning granularities for data elements including a first granularity for a first data combination, the first data combination comprising a combination of multiple dimensions of data, wherein a user interface is configured to display data for the first data combination at the first granularity;

calculate a variance metric between planned data at the first granularity for the first data combination and actual data observed for the first data combination, wherein the observed data is stored at a different granularity than the planned data;

calculating a frequency of occurrence metric between planned data at the first granularity for the first data combination and actual data observed for the first data combination, wherein the frequency of occurrence metric comprises a frequency of occurrence of variance between the planned data for the first data combination at the first granularity and the actual data observed for the first data combination;

automatically adjust the planning granularity for the first data combination from the first granularity to a second granularity when the variance metric meets or exceeds a first threshold and the frequency of occurrence metric meets or exceeds a second threshold; and

receive a selection of a cross-section of data spanning a plurality of dimensions in the multi-dimensional data model, the selection including the first data combination, wherein a user interface is configured to display data for the first data combination at the second granularity based on the adjusting.

13. The computer readable medium of claim 12 , wherein the first granularity is a monthly granularity and the second granularity is a weekly granularity.

14. The computer readable medium of claim 12 , wherein the frequency of occurrence metric comprises a number of occurrences over a predetermined window where the variance metric meets or exceeds the first threshold.

15. The computer readable medium of claim 12 , wherein the instructions further cause the processor to:

calculate a contribution metric for the first data combination, wherein the contribution metric comprises a percentage at which the first data combination contributes to a higher level data combination in the selected cross-section of data, and the planning granularity for the first data combination is adjusted from the first granularity to the second granularity when the variance metric meets or exceeds the first threshold and at least one of the frequency of occurrence metric meets or exceeds the second threshold or the contribution metric meets or exceeds a third threshold.

16. The computer readable medium of claim 15 , wherein the contribution metric comprises a percentage at which the first data combination contributes to a parent data combination in the selected cross-section of data according to a hierarchical structure for the multi-dimensional data model.

17. A system for configuring data granularities for a multi-dimensional data model, the system comprising:

a processor; and

a memory storing instructions for execution by the processor, the instructions configuring the processor to:

store planning granularities for data elements including a first granularity for a first data combination, the first data combination comprising a combination of multiple dimensions of data, wherein a user interface is configured to display data for the first data combination at the first granularity;

calculate a variance metric between planned data at the first granularity for the first data combination and actual data observed for the first data combination, wherein the observed data is stored at a different granularity than the planned data;

calculating a frequency of occurrence metric between planned data at the first granularity for the first data combination and actual data observed for the first data combination, wherein the frequency of occurrence metric comprises a frequency of occurrence of variance between the planned data for the first data combination at the first granularity and the actual data observed for the first data combination;

automatically adjust the planning granularity for the first data combination from the first granularity to a second granularity when the variance metric meets or exceeds a first threshold and the frequency of occurrence metric meets or exceeds a second threshold; and

receive a selection of a cross-section of data spanning a plurality of dimensions in the multi-dimensional data model, the selection including the first data combination, wherein a user interface is configured to display data for the first data combination at the second granularity based on the adjusting.

18. The system of claim 17 , wherein the frequency of occurrence of variance comprises a number of occurrences over a predetermined window where the variance metric meets or exceeds the first threshold.

19. The system of claim 17 , wherein the instructions further configure the processor to:

calculate a contribution metric for the first data combination, wherein the contribution metric comprises a percentage at which the first data combination contributes to a higher level data combination in the selected cross-section of data, and the planning granularity for the first data combination is adjusted from the first granularity to the second granularity when the variance metric meets or exceeds the first threshold and at least one of the frequency of occurrence metric meets or exceeds the second threshold or the contribution metric meets or exceeds a third threshold.

20. The system of claim 19 , wherein the contribution metric comprises a percentage at which the first data combination contributes to a parent data combination in the selected cross-section of data according to a hierarchical structure for the multi-dimensional data model.