Before starting with an S/4HANA implementation, the involved stakeholders as well as the project team will think about many challenges that they might face. Data quality is usually not among the top priorities when designing new business processes or IT landscapes.
However, the truth is that data quality cannot be taken for granted and is an often-underestimated factor with a significant potential to threaten a successful implementation.
Even the best S/4HANA configuration will become worthless when data quality is not as expected, and users cannot rely on it.
So, what needs to be done that data quality is not turning into a nightmare? First, data from legacy systems (SAP or non-SAP) needs to be cleaned, mapped and then either migrated or converted into a format that is compatible with the S/4HANA data models. Then, once the data is loaded into S/4HANA, a certain level of reconciliation between source and target is required to deliver data quality and build trust for business users.
In a nutshell, the most critical success factors related to data quality can be summarised as follows:
Source of template: www.infograpia.com
Data should be always cleaned at source (in the source system) and before migrating/converting to S/4HANA to avoid the typical “garbage in – garbage out” phenomena. This is by far the easiest and cheapest way to ensure data quality rather than implementing a huge and expensive cleansing logic in the data migration tools.
Data should be correctly mapped from source to target data models in S/4HANA. This may sound like a simple field mapping exercise, but here we find a common source of future data quality issues. This process is not just purely “technical” exercise and is often not just a simple 1:1 field mapping. Instead, a certain level of derivation logic is required and thus the business users should always be involved here to ensure accuracy and consistency.
Data should be migrated/converted to S/4HANA by using standard tools either provided by SAP (e.g. Migration Cockpit, Object Modeler) or third-party vendors (e.g. Syniti or WinShuttle). For larger amount of data with certain complexity, it is definitely not advisable to try manual approaches in Excel or use “DIY” tools and interfaces. The main reason here is the validation of migrated data which should always be based on the SAP internal logic (e.g. using standard BAPIs).
Data should be reconciled between source and target system(s) to ensure that business expectations are met accordingly. This may sound easy, but proper tools like reconciliation reports should be available for business users to verify their migrated data. Again, SAP-based tools can be leveraged (e.g. using S/4HANA reporting or HANA capabilities) or third-party tools (e.g. Syniti or WinShuttle).
No one would ever disagree that data quality plays a key role in every ERP or S/4HANA implementation. However, when it comes to actual measures to ensure data quality or allocate enough resources, reality shows a different picture. Therefore, it is highly recommended integrating data quality in all implementation areas like business processes or cross-functional topics like testing. If done accordingly, it will pay high dividends not just during the implementation, but especially in the long run after go-live.
*Please note that part of this blog post was already shared on LinkedIn
However, the truth is that data quality cannot be taken for granted and is an often-underestimated factor with a significant potential to threaten a successful implementation.
Even the best S/4HANA configuration will become worthless when data quality is not as expected, and users cannot rely on it.
How to ensure Data Quality
So, what needs to be done that data quality is not turning into a nightmare? First, data from legacy systems (SAP or non-SAP) needs to be cleaned, mapped and then either migrated or converted into a format that is compatible with the S/4HANA data models. Then, once the data is loaded into S/4HANA, a certain level of reconciliation between source and target is required to deliver data quality and build trust for business users.
In a nutshell, the most critical success factors related to data quality can be summarised as follows:
S/4HANA Data Quality
Source of template: www.infograpia.com
Data Cleansing
Data should be always cleaned at source (in the source system) and before migrating/converting to S/4HANA to avoid the typical “garbage in – garbage out” phenomena. This is by far the easiest and cheapest way to ensure data quality rather than implementing a huge and expensive cleansing logic in the data migration tools.
Data Mapping
Data should be correctly mapped from source to target data models in S/4HANA. This may sound like a simple field mapping exercise, but here we find a common source of future data quality issues. This process is not just purely “technical” exercise and is often not just a simple 1:1 field mapping. Instead, a certain level of derivation logic is required and thus the business users should always be involved here to ensure accuracy and consistency.
Data Migration
Data should be migrated/converted to S/4HANA by using standard tools either provided by SAP (e.g. Migration Cockpit, Object Modeler) or third-party vendors (e.g. Syniti or WinShuttle). For larger amount of data with certain complexity, it is definitely not advisable to try manual approaches in Excel or use “DIY” tools and interfaces. The main reason here is the validation of migrated data which should always be based on the SAP internal logic (e.g. using standard BAPIs).
Data Reconciliation
Data should be reconciled between source and target system(s) to ensure that business expectations are met accordingly. This may sound easy, but proper tools like reconciliation reports should be available for business users to verify their migrated data. Again, SAP-based tools can be leveraged (e.g. using S/4HANA reporting or HANA capabilities) or third-party tools (e.g. Syniti or WinShuttle).
Synopsis
No one would ever disagree that data quality plays a key role in every ERP or S/4HANA implementation. However, when it comes to actual measures to ensure data quality or allocate enough resources, reality shows a different picture. Therefore, it is highly recommended integrating data quality in all implementation areas like business processes or cross-functional topics like testing. If done accordingly, it will pay high dividends not just during the implementation, but especially in the long run after go-live.
*Please note that part of this blog post was already shared on LinkedIn
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