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Adjusted Level THREE - Service Overview

Level TWO HCM capability information may come from different sources and only provides a best fit guide. It does not give sufficient detail to determine a gap resolution or provide data requirements for clients to build their own custom solution. This service offering enables a more precise measurement of the options and delivers sufficient detail for clients to assemble a custom built solution.

Service Objective

The service objective is to determine exactly what is the best fit product to meet the client's HR functional needs after examining the product's capability to deliver the necessary data and information reports.


The process takes the detail provided in the portal down to Level THREE (data level) and compares it to product capability. The Level TWO score is then adjusted by a factor representing the percentage of the Level THREE score for a component divided by the required score if everything was available (for example 34 out of a possible 50 is a 68% factor). The factor is applied to the Level TWO score to give a more accurate picture of what is the best fit product and what is required in terms of effort and cost to close the functional gap..


The screen shots in this section illustrate the method used to determine a true product functional capability comparison and calculate the real cost of closing the functional gap.
Adjusted Best Fit Level THREE - Calculating the Factor

As part of the Best Fit process at Level TWO suppliers are usually credited with having all of the data that makes up a Level TWO process component. Once the analysis process goes down to Level THREE to determine what data is available in a supplier's product a new picture emerges. If not all data is provided and the client requires those items then customisation costs are incurred. A FACTOR representing the percentage of data provided needs to be applied to the Level TWO score to achieve a clearer picture. The purpose of this level of comparison is to accurately asses customisation costs and to provide a better comparison between products.

Gap Analysis Summary - Identifying the Real Gap

There are over 2,500 data elements compared in the Best Fit Analyzer. It is not practical to view the gap online in a drill down fashion: To compare requirements against capability at that level it is necessary to run reports and drop the information into a spreadsheet to produce a gap analysis summary and provide data for further analysis.

Gap Detail - Define requirements & importance of technology to HR functions

Once the gap is identified at field level the next step is to assess the cost of customisation. Information in the spreadsheet is sorted into field types. In the example opposite all the Formula fields are grouped together because they have a common development effort time. Depending upon complexity the time may vary but it is possible to apply a common time element to the field type. Some field types such as date or text require very little development time. Some Formula field** logic may be available in the CET Wiki and it is possible to copy and paste the formula in the client's field.(** Note: the development platform is used for the purposes of this exercise)

Gap Analysis Report - Producing a Summary of Effort and Cost to Close Gap

Once information is available from field type costs and efforts it is possible to produce a true Gap Analysis Report. It is possible to vary the development time if professional resources are used to make custom changes. Alternately, if internal resources are used to make custom changes it is possible to vary the hourly rate and reduce costs. If much of the material needed is available from the CET Wiki it is possible to make further cost savings. In particular, picklist content and formula logic.

The Bottom Line - Applying the Factor to Components for a More Realistic Comparison
The bottom line is a more accurate comparison of products is possible by going down to Level THREE (data level) and applying a factor to each products Level TWO score. In the example shown opposite the original score for the product under scrutiny was 79%. After checking data availability and recalculating the score a new figure of 51.16% was identified. The value in this type of analysis is it eliminates the distortion between products that supply data for the client's nice to have items, and are rewarded the same as the suppliers who deliver the must have items. The products that deliver the mandatory data requirements will find their way to the top.