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.
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 t
ry 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 i
ntegrating 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