Would it not be convenient to identify General Ledger data inconsistencies in your ERP Production system before your begin a System Conversion project? Data inconsistency corrections are time-consuming activities and early detection and correction is a key driver for fast and efficient project execution. SAP has recently (Q3 2020) provided the capability to automate a check on the quality of GL transaction data through the SAP Readiness Check 2.0 process installed in the source ERP system. The check analyzes GL transactional data and can detect an impressive number of common errors and/or inconsistencies. The SAP Readiness Check 2.0 application is able to incorporate this check into its holistic dashboard and is called the “Financial Data Quality” (FDQ) check.
For more details on how to enable the SAP Readiness Checks 2.0 please refer to SAP Note 2913617 – SAP Readiness Check 2.0
Financial Data Quality Check
The Financial Quality Check uses the FIN_CORR_RECONCILE transaction to analyze the quality of the financial data in your existing ERP Production System and categorize the different errors to further help the project team assess the required effort to correct the data inconsistencies.
To access the FDQ details in the Readiness Check dashboard, click on Financial Data Quality title (outlined in red):
In this example, there were three inconsistencies and two different types of errors detected.
On the Financial Data Inconsistencies tab, inconsistencies by fiscal year and company codes are displayed in a graph. Filtering options for Fiscal Year, Company Code, and Error Category as available to sort the list.
On the Financial Data Inconsistencies>Details you can find the errors message numbers and their category.
Be aware that the number of inconsistencies does not determine the correction effort because you may able to resolve thousands of errors with the execution of just one report. The message numbers (or error types) and the error Category can be directly associated with the correction effort.
The error category provides a general guide for the effort needed to resolve the error types:
|Category Type||Correction Type||Details|
Standard automated correction available – SAP Note 2956096.
Inconsistencies of this category can be analyzed and potentially resolved with the help of the FIN_CORR_MONITOR report. Refer to SAP Note 2956096 for further details. In case of questions, contact FDQ department under email@example.com.
Manual correction, correction instructions available – SAP Note 2714344
Inconsistencies of this category need to be resolved manually. For instructions, refer to the SAP Knowledge Base Article 2714344. In case of questions, contact FDQ department under firstname.lastname@example.org
|C||Contact SAP Support||
Please contact SAP Support
An SAP support specialist is required for a deeper analysis and advice on the resolution. We recommend that you contact the SAP support specialists for a deeper analysis and advice on the resolution by creating a support incident under component FI-GL-MIG. In certain cases, a resolution may require specialized SAP services and imply surcharges.
|D||Customer specific correction||
Customer specific correction
One or more system-specific corrections are required to fix this error. We recommend that you consult your implementation partner or contact SAP support by raising a message to the FI-GL-MIG
On the Expected Effort tab, you can find an overview of the important factors that normally have an impact on the required effort, duration, and cost for the data cleansing.
Another helpful view (and my preferred viewed because it has all the information consolidated) is under Expected Effort>Inconsistencies in Detail.
For more information on enabling and executing FIN_CORR_RECONCILE and the associated automated correction capabilities, please check my recent blogs S/4HANA_System Conversion_ Finance Data Consistency checks and Finance Consistency Checks: FIN_CORR_MONITOR.
In conclusion, under SAP Readiness Check 2.0 > Financial Data Quality you can check the quality of General Ledger finance data in your ERP Production system, analyze detected inconsistencies, and estimate the effort required to resolve them.
Hopefully this blog post was helpful!
Please add in the comment section any other topics you think are valuable to have included in this blog post.
– Brought to you by the S/4HANA RIG