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The most basic and vital use of FDMEE is its mapping functionality. Mapping is so integral to the application that without it users have no way of integrating or validating their data into HFM. This blog will serve as an introduction to a three-part series that covers what to consider when developing your mapping process.

Member Mappings

Let’s begin by covering the surface and defining member mappings. In a simple view, member mappings act as the “middle man” between the source dimension members and target dimension members within a single dimension. By defining this relationship, member mappings transform the data in the general ledger to its designated HFM target value. Let’s say you have a general ledger account code of 30256, Sales Direct. In HFM, the input account member is 400008, Sales Trade – US to US. Member mapping is the process of taking this source data and pushing it into the HFM member.

Easy to understand, right? To a certain extent, yes. Essentially, FDMEE’s mapping process ensures that the raw data from the source system is validated and integrated correctly into the appropriate target members that you have set up in Data Load Mapping. The tricky part to this is configuring your maps in a way that will allow you to process your data in the most efficient way possible. Let’s take a closer look at the capability of member mappings. There are five types of member mappings:

  1. Explicit: Also referred to as “one-to-one” mappings. Each source value has a designated target value.  a. G/L account 30256 = HFM account 400008, Sales Trade – US to US.
  2. Between: A continuous range of source values mapped to a single target value. a. G/L accounts 30255-30300 = HFM account 400008, Sales Trade – US to US.
  3. In: A non-continuous range of source values mapped to a single target value. a. G/L accounts 30200, 30255, 30300 = HFM account 400008, Sales Trade – US to US.
  4. Multi-Dimension: A new feature in FDMEE that allows a combination of source values to a single target value. a. G/L account 30256 and UD2 member 200 = HFM account 400008, Sales Trade – US to US.
  5. Like: Also referred to as a “wildcard” mapping. A like mapping uses special characters to match a string in the source value to map it to a target value. a. G/L accounts beginning with 30 = HFM account 400008, Sales Trade – US to US.

FDMEE_Mappings.png

Mappings for a dimension can utilize any one of the types above. A map can be comprised of a combination of member mappings, or simply a single one. It is not uncommon to see clients use either tactic. I often come across the usage of Explicit and Like mappings together!  As you develop your maps, be wary of what type of member mappings you select to use in order to avoid unnecessary and bothersome maintenance down the road. To dodge future mapping issues, and ensure that your maps are both efficient and effective, it is important to know how maps are processed.

The Process

In FDMEE, the mapping process takes place during the import process. When a file is loaded and imported, FDMEE processes the dimensions in the order that they are configured. From there, member mappings are processed individually and in a distinct order. The order of precedence is Explicit, In, Multi-Dimension, Between, and Like. If a map consists of more than one type of mapping, then those mappings that are not an Explicit continues to the next type, Explicit is processed first followed by In, Multi-dimension, Between, and Like mappings. Some mappings can overlap, such as Between and Like, which can cause source values to be mapped to an incorrect HFM target value. This is when it’s best to utilize Implicit (Like) rule names to determine precedence. Similar to how mapping types are processed, Like rule names are processed alphabetically as well as by the use of numbers to assist with the ordering. 

Having this process down and understanding member mappings are extremely useful baselines and essential prior to the initial analysis of your maps. If your maps are not efficient and stable, you will experience one too many “kick outs” to not be at least a little aggravated. When maps are not maintained and governed properly, they can grow to a disastrous size and working on cleaning up a substandard map can take weeks or months.

Now that some of the core basics of the FDMEE mapping process have been covered, the next blog will review the types of issues encountered in the mapping process.

Looking for Financial Data Quality Management information? Join us at the Klondike16 Conference for a in-depth FDM to FDMEE discussion or our upcoming webinar.