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Tackle SAP S/4HANA migration with high-quality business partner data

Product Manager
January 30, 2023
SAP-4HANA-migration-use-case

Data quality is a requirement of day-to-day business. However, this often does not work out as planned because the attention is absorbed by other projects, the business partner data changes too quickly, or there has been no suitable software solution implemented. These are among the main reasons why clean-up campaigns occur regularly during system changes, updates or mergers.

SAP requires migration to S/4HANA until 2027

All companies working on SAP Business Suite 7/ECC are requested to switch to S/4HANA if they want to continue working with SAP. According to SAP statements and also explained in this blog post there will be an intermediate phase between 2027 and 2030 during which an extended maintenance will be available to customers.

Besides the hurdles of software implementation, configuration, testing, rollouts, and training, one of the main challenges is the import of master and transaction data. Master data must be checked, validated, and prepared from legacy systems to the new system.

Centralization of IT systems

We observe companies striving to centralize IT landscapes and to have less customized IT systems: from legacy companies having a lot of different systems, e.g. CRM, ERP, WMS, TMS, etc. as their as-is-situation, e.g. per business unit, per country, per business process, etc. with extremely many customers and vendors in their master data. In many cases this will require to consolidate data from hundreds of systems to a few dozen systems.   

Business Partner object required

Starting off with S/4HANA means to work with the business partner object in master data management. What was covered by the objects customers and vendors is shifted to business partners (BP). This business model needs to be tackled and implemented. Many companies may also introduce SAP MDG to cover business partner creation, maintenance, and governance processes. Also SAP MDG comes with the business partner.

CDQ helps to significantly increase data quality and accelerate the project

Assuming that all business partners of all the systems would be put into one table, there would certainly be many duplicates. However, without a suitable system, it is almost impossible to identify the duplicates with 100% certainty:

  • How to distinguish between full names and abbreviations?
  • How to distinguish ship-to addresses and bill-to addresses of legal entities without exhaustive research?
  • In addition, almost every business partner would have slightly different information.
  • For example, depending on the system, the business partners were maintained with different conventions, or depending on the purpose of the system, additional data attributes were maintained that are missing in other systems.

CDQ helps customers identify these duplicates and apply the valuable data attributes in each case. Business partner data records of all relevant systems are imported in a Data Mirror. Within this Data Mirror all business partners are formatted equally considering the CDQ data model. This makes the business partner comparable, consistent, and standardized.

Most likely, however, the data is largely outdated or contains errors for other reasons.

Based on our experience around 30% of all business partner change within one year. Consequently, if a business partner is not validated within one year, there is a 30% risk that it is outdated or simply wrong.

Many errors are in the business partner master data right from the beginning. In order to avoid dragging along these legacy issues, it is essential to thoroughly check, validate and, if necessary, correct the business partners. This is the only way getting off a clean start with the new system.

CDQ therefore checks business partner data sets against +2.100 data quality rules and +70 registers to guarantee high data quality. This is key to improve business partner data quality significantly. Obviously, such a process must be semi- or fully automated, as no company has the time or the resources to go through millions of business partners by hand.

SAP One-Domain-Model as baseline

No matter if companies cover their future master data processes in S/4HANA or SAP MDG, business partner data is created and maintained in line with the SAP One-Domain-Model (ODM), which is the standardized data model.

CDQ delivers business partners with the out-of-the-box mapping already in the SAP ODM, so that data records can be imported directly into S/4HANA or SAP MDG.

All  activities described above help to sort, clean, and improve business partner data, as well as to import the legacy data into the right data model. Nevertheless, it´s at least as important to keep this data up to date and to create new business partners only correctly and consistently.

A cleansed business partner database will only remain current for a very short time if no further activities are undertaken. Cleanliness can only be achieved through continuous use of appropriate systems. Even when considering future acquisitions, getting data clean will always be necessary again and again to integrate new companies and datasets. Consequently get-clean projects are a necessary groundwork but need to be backed-up by robust processes.

CDQ helps with Zero Maintenance to deliver business partners updates  retrieved from registers as well as the CDQ Data Sharing Community, directly into S/4HANA. In addition, we enable creation of clean and correct business partners with CDQ First Time Right. This solution is directly integrated into the business partner creation process by delivering intelligence to +2.100 data quality rules and data of +70 registers.

Get in touch to discuss how our reliable solutions can get your process flows more predictable at a little effort.

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