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OneStream Composite Transformation Rules – Usages

  • , Consultant

The OneStream Transformation Rules provide the ability to transform or “Map” source data to the desired target OneStream reporting intersection. OneStream transformations are comprised of 5 different types of Mappings:

  • a) One-to-One
  • b) Composite
  • c) Range
  • d) List
  • e) Mask

This article will focus on the Composite type of Mapping, which provides for multiple source dimensions, to work in tandem, to define the target OneStream reporting intersection.

Why would a client need to use Composite Transformation Rules?

There are multiple reasons why an implementation would require the use of Composite Transformation Rules. This blog will show how they can be utilized in the following ways:

  • a) When challenged to map inconsistent historical data.
  • b) When mapping needs to vary by Entity.
  • c) When mapping needs to vary by Time period.
  • d) Where Account and Department drive the Target Account.
  • e) Where Account and Entity drive the I/C partner.

When challenged to map inconsistent historical data.

When loading multiple years of historical data, there may be times when the source system(s) had allowed data to be recorded inconsistently. As that data is being imported into a more robust reporting system like OneStream, the implementation team will have to “fashion” the mappings to ensure consistent reporting year over year.

When mapping needs to vary by Entity.

A Data Source may require unique mappings based upon the source Entity. If the source data is inconsistent by Entity (accounts or departments “UD2”) as an example, composite mappings will need to be used.

The following example shows how composite mapping can be used:

Composite Mapping

In this example, the composite mapping for account 8033 and department 750 are being mapped to 2 different UD2 targets, based upon the Entity. For Texas, it is mapped to 518, while Houston is mapped to 750.

When mapping needs to vary by Time period.

A Data Source may require unique mappings based upon the source Time Year. If the source data is inconsistent by Time (some years may have been recorded in the wrong department) as an example, composite mappings will need to be used.

The following example shows how composite mapping can be used:

Composite Mapping Example

In this example, the composite mapping for account 8033 and department 750 are being mapped to 2 different UD2 targets, based upon the Year. For 2018 it is mapped to 518, while 2019 is mapped to 750.

Where Account and Department drive the Target Account.

A Data Source may require unique mappings based upon the source Account and Department. If the combination of Account and Department defines the target Account, composite mappings will need to be used.

The following example shows how composite mapping can be used:

OneStream Composite Mapping Example

In this example, the composite mapping for account 4310 is being mapped to 2 different account targets, based upon the department. For department 750 it is mapped to account 3035, while for department 518 it is mapped to account 3026.

Where Account and Entity drive the I/C partner.

A Data Source may require unique mappings based upon the source Account and Entity. If the combination of Account and Entity defines the target I/C Partner, composite mappings will need to be used.

The following example shows how composite mapping can be used:

OneStream Composite Mapping

In this example, the composite mapping for I/C partner is based upon the source Entity and Account. Any account that begins with 655 is being mapped to 2 different I/C partner targets, based upon the entity. For entity Texas, it is mapped to I/C partner Dallas, while for entity Houston it is mapped to I/C partner Texas.

Summary

Composite Transformation Rules provide the ability to map data utilizing multiple dimensions when the need can vary greatly based upon the source data challenges. The examples shown here have been built based on OneStream Software as a consolidation tool.

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