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The transformation to SAP S/4HANA is a strategic major project for many companies. Technologies, processes, and organisations all undergo significant change with the focus often placed on system and process design. Yet one aspect is frequently underestimated: master data.
Clean, consistent, and reliable data is a fundamental prerequisite for a successful S/4HANA implementation. Especially in the case of an S/4HANA conversion, it is crucial to ensure that master data is consistent and can be migrated without issues. In some cases, data archiving also makes sense, for example to remove legacy data and avoid carrying it into the new system.
Why master data is so critical
Whether it concerns business partners such as customers and suppliers, materials, or financial data like accounts — master data forms the foundation of all business processes. Incorrect, incomplete, or duplicate data has a direct impact on operations and can lead to manual workarounds, flawed decision-making, delays in supply chain and financial processes, as well as increased operating costs and compliance risks.
In the S/4HANA context in particular, the rule is clear: only with consistent and high-quality master data can companies unlock the full potential of the new platform — whether in reporting, automation, or the integration of cloud solutions.
Challenges in the transformation
Many companies launch their S/4HANA projects without giving data quality the necessary attention. Typical pitfalls include:
- Historically grown master data with inconsistencies and redundancies
- Lack of governance: unclear responsibilities for maintenance and quality
- Insufficient preparation: data cleansing is started too late
- Technical complexity in migrating different master data objects
The risk: project delays and budget overruns, inefficient processes after go-live, and an ROI that falls short of expectations.
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Best practices for your company
It is therefore essential to consider the topic of master data both strategically and operationally in every phase of your S/4HANA transformation.
We have summarised the key points for you across the different project phases:
Discover – Creating transparency
Even in the Discover phase, it is important to take a critical look at your data landscape:
- What is the current level of data quality?
- Which objects will be relevant in the future (e.g., business partners)?
- Which data will be migrated, and which needs to be cleansed?
- Where are the interfaces? Which system is leading?
The main task in this phase is to raise awareness within the company that data quality is a key success factor for the transformation.
Various tools can help you assess the current state of your data quality. How AI can support you in cleansing your data is explained in our article Master Data Cleansing: Innovations in Duplicate Detection.
Excursus: How can I measure master data quality?
Master data quality is usually assessed using predefined criteria and key performance indicators (data quality KPIs). The aim is to evaluate data in a measurable and objective way — not just based on gut feeling. The most common dimensions are:
Completeness
Are all mandatory fields filled in (e.g., address, tax ID, material group)?
Example: 95% of all vendors have an IBAN stored.
Correctness / Accuracy
Do the data match reality or reference sources?
Example: 10% of material numbers are not linked to valid units in the system.
Uniqueness
Are there duplicates or redundant records?
Example: Two customers with the same address but different customer numbers.
Consistency
Do data align across different systems and processes?
Example: A supplier is marked as active in ERP but inactive in SRM.
Actuality
Are the data still valid and up to date?
Example: 30% of supplier contact persons are outdated.
Conformity
Do the data comply with internal guidelines and standards?
Example: Material short texts follow the prescribed maximum length of 40 characters.
Prepare – Defining governance and responsibilities
In the preparation phase, the foundation is laid:
- If not already in place: establish a dedicated master data team with clearly defined roles and responsibilities.
- Set up a dedicated workstream within the S/4HANA transformation to take ownership of master data and data migration.
- Define governance rules for maintenance and quality assurance.
- Identify legacy systems and interfaces, and begin initial field mappings.
- Select a migration strategy and supporting tools.
The focus in this phase is to make governance binding and to allocate resources for master data at an early stage.
Explore – Designing the target concept
In workshops, the relevant master data and the future master data concept are defined. Topics such as mandatory fields, approval processes, and mapping rules are specified in detail.
In this phase, it is crucial to ensure close collaboration between business and IT — because master data is not just an IT topic!
Realize – Executing the concept and ensuring quality
Now the actual implementation begins:
- Technical setup of the migration (Data Migration Cockpit, LSMW, SAP Data Services (BODS), etc.)
- Test migrations and cleansing of legacy data
- Execution of tests (functional, UAT)
- Training for business departments
- Validation and sign-off of data (quality, completeness) before go-live
Our recommendation: prioritize quality over speed — poor data quality puts the entire project success at risk.
Deploy – Cutover and production migration
In the go-live phase, precision is key:
- Execute the production migration
- Perform final checks
- Provide support for data issues after go-live
In this phase, it is essential to ensure fast response times in the event of data-related problems.
Run – Sustainable data management
After go-live, the focus shifts to continuous optimization:
- Establish data stewardship and dedicated master data teams
- Ensure ongoing quality assurance (duplicate checks, error detection, process optimization)
- Promote automation of maintenance processes
Even after your transformation, make data quality a permanent priority.
The role of management
The responsibility for master data cannot lie with IT alone. Successful companies treat master data management as a strategic issue. This is reflected, for example, in allocating budgets and resources, involving business departments in ownership, and measuring data quality on a regular basis.
Conclusion
Those who put data quality at the center of their transformation create the foundation for:
- reliable decision-making
- efficient end-to-end processes
- more automation and innovation
- a faster and more cost-effective migration
- the use of AI models
Experience shows that many S/4HANA projects do not fail because of technology, but because of poor data quality. Companies that consistently take master data into account from the very beginning reduce risks, increase efficiency, and secure the ROI of their transformation. Only with clean and reliable master data can S/4HANA unfold its full potential.
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