Allocation Split

Description

Allocate values based on driver data.

Data Table Settings

The Values To Allocate Table, Driver Data Table and Allocation Result Table can be selected dynamically or statically.

Dynamic Table Selection

The dynamic table option allows specification of a table using text and variables. This is useful when employing variable driven workflows where the table or view references are relative to the variables specified.

An example that uses the current_month variable to dynamically point to a table:

legal_entity/inputs/{current_month}/ledger_values

Static Table Selection

When a specific table is desired as the source, leave the Dynamic box unchecked and select the source table using the dropdown menu.

Table Explorer is always avaible with any table selection. Click on the Table Explorer button to the right of the table selection and a Table Explorer window will open.

Values To Allocate Table

This is the table that contains the values that are to be allocated. These are typically cost or revenue values.

Driver Data Table

The driver data table contains the values that the allocation step will use to allocate costs.

Examples:

• For a supply chain to assign costs to customers you might use delivery data with the number of deliveries or the weight of the deliveries as the driver.
• For an IT help desk to assign its costs to the departments it supports the driver data be the number of tickets by cost center.

Driver Data Sign Rule

Driver data can contain both positive and negative values. The Driver Data Sign Rule lets you decide how conflicting signs will be handled.

• Error on conficting signs - Allocation step will produce an error and stop if conflicting signs are encountered.
• Proceed with warning on conflicting signs - Allocation step will use both negative and positive driver values but will display a warning.
• Use only positive driver values - Allocation step will only use positive driver values, will ignore negative values.
• Use only negative driver values - Allocation step will only use negative driver values, will ignore positive values.
• Use absolute values of driver data - Allocation step will use the absolute values of the driver data.

Allocation Result Table

Append Results to Target Table

If this box is checked the allocation results will be appended to the allocation result table. If this box is not checked the allocation results table will be overwritten each time the allocation step runs.

Separate Columns for Allocated Results

If this box is checked then the results table will show the amount of each allocated record as well as the amount actually allocated to each driver record.

Allocation Source Map

The Allocation Source Map is used to map the columns from the Values To Allocate Table that will be used in the allocation step.

Inspection and Populating the Mapper

Using the Inspect Source menu button provides additional ways to map columns from source to target:

• Populate Both Mapping Tables: Propagates all values from the source data table into the target data table. This is done by default.
• Populate Source Mapping Table Only: Maps all values in the source data table only. This is helpful when modifying an existing workflow when source column structure has changed.
• Populate Target Mapping Table Only: Propagates all values into the target data table only.

If the source and target column options aren’t enough, other columns can be added into the target data table in several different ways:

• Propagate All will insert all source columns into the target data table, whether they already existed or not.
• Propagate Selected will insert selected source column(s) only.
• Right click on target side and select Insert Row to insert a row immediately above the currently selected row.
• Right click on target side and select Append Row to insert a row at the bottom (far right) of the target data table.

Role

Each column in the data mapper must be assigned a role:

• Pass Thought - These columns will appear in the allocation results table.
• Value to Allocate - This is the column that contains the values to be allocated.

Deleting Columns

To delete columns from the target data table, select the desired column(s), then right click and select Delete.

Chaging Column Order

To rearrange columns in the target data table, select the desired column(s). You can use either:

• Bulk Move Arrows: Select the desired move option from the arrows in the upper right
• Context Menu: Right clikc and select Move to Top, Move Up, Move Down, or Move to Bottom.

Reduce Result to Distinct Records Only

To return only distinct options, select the Distinct menu option. This will toggle a set of checkboxes for each column in the source. Simply check any box next to the corresponding column to return only distinct results.

Depending on the situation, you may want to consider use of Summarization instead.

The distinct process retains the first unique record found and discards the rest. You may want to apply a sort on the data if it is important for consistency between runs.

Aggregation and Grouping

To aggregate results, select the Summarize menu option. This will toggle a set of select boxes for each column in the target data table. Choose an appropriate summarization method for each column.

• Group By
• Sum
• Min
• Max
• First
• Last
• Count
• Count (including nulls)
• Mean
• Standard Deviation
• Sample Standard Deviation
• Population Standard Deviation
• Variance
• Sample Variance
• Population Variance
• Advanced Non-Group_By

For advanced data mapper usage such as expressions, cleaning, and constants, please see the Advanced Data Mapper Usage

Allocation Source Filters

To allow for maximum flexibility, data filters are available on the source data and the target data. For larger data sets, it can be especially beneficial to filter out rows on the source so the remaining operations are performed on a smaller data set.

Select Subset Of Data

This filter type provides a way to filter the inbound source data based on the specified conditions.

Apply Secondary Filter To Result Data

This filter type provides a way to apply a filter to the post-transformed result data based on the specified conditions. The ability to apply a filter on the post-transformed result allows for exclusions based on results of complex calcuations, summarizaitons, or window functions.

Final Data Table Slicing (Limit)

The row slicing capability provides the ability to limit the rows in the result set based on a range and starting point.

Filter Syntax

The filter syntax utilizes Python SQLAlchemy which is the same syntax as other expressions.

View examples and expression functions in the Expressions area.

Driver Data Map

The Allocation Driver Data Map is used to map the columns from the Driver Data Table that will be used in the allocation step.

Inspection and Populating the Mapper

Using the Inspect Source menu button provides additional ways to map columns from source to target:

• Populate Both Mapping Tables: Propagates all values from the source data table into the target data table. This is done by default.
• Populate Source Mapping Table Only: Maps all values in the source data table only. This is helpful when modifying an existing workflow when source column structure has changed.
• Populate Target Mapping Table Only: Propagates all values into the target data table only.

If the source and target column options aren’t enough, other columns can be added into the target data table in several different ways:

• Propagate All will insert all source columns into the target data table, whether they already existed or not.
• Propagate Selected will insert selected source column(s) only.
• Right click on target side and select Insert Row to insert a row immediately above the currently selected row.
• Right click on target side and select Append Row to insert a row at the bottom (far right) of the target data table.

Role

Each column in the data mapper must be assigned a role:

• Source Relation - These columns have corresponing columns in the Values To Allocate Table.
• Allocation Target - The columns will be the target of the allocation step and will appear in the Allocation Result Table.
• Split Value - This column contains the values that will be used to allocate the values in the Values To Allocate Table.

Deleting Columns

To delete columns from the target data table, select the desired column(s), then right click and select Delete.

Chaging Column Order

To rearrange columns in the target data table, select the desired column(s). You can use either:

• Bulk Move Arrows: Select the desired move option from the arrows in the upper right
• Context Menu: Right clikc and select Move to Top, Move Up, Move Down, or Move to Bottom.

Reduce Result to Distinct Records Only

To return only distinct options, select the Distinct menu option. This will toggle a set of checkboxes for each column in the source. Simply check any box next to the corresponding column to return only distinct results.

Depending on the situation, you may want to consider use of Summarization instead.

The distinct process retains the first unique record found and discards the rest. You may want to apply a sort on the data if it is important for consistency between runs.

Aggregation and Grouping

To aggregate results, select the Summarize menu option. This will toggle a set of select boxes for each column in the target data table. Choose an appropriate summarization method for each column.

• Group By
• Sum
• Min
• Max
• First
• Last
• Count
• Count (including nulls)
• Mean
• Standard Deviation
• Sample Standard Deviation
• Population Standard Deviation
• Variance
• Sample Variance
• Population Variance
• Advanced Non-Group_By

For advanced data mapper usage such as expressions, cleaning, and constants, please see the Advanced Data Mapper Usage

Driver Data Filters

To allow for maximum flexibility, data filters are available on the source data and the target data. For larger data sets, it can be especially beneficial to filter out rows on the source so the remaining operations are performed on a smaller data set.

Select Subset Of Data

This filter type provides a way to filter the inbound source data based on the specified conditions.

Apply Secondary Filter To Result Data

This filter type provides a way to apply a filter to the post-transformed result data based on the specified conditions. The ability to apply a filter on the post-transformed result allows for exclusions based on results of complex calcuations, summarizaitons, or window functions.

Final Data Table Slicing (Limit)

The row slicing capability provides the ability to limit the rows in the result set based on a range and starting point.

Filter Syntax

The filter syntax utilizes Python SQLAlchemy which is the same syntax as other expressions.

View examples and expression functions in the Expressions area.