Why Data Must Be Considered Early in an ERP Transformation Programme

Enterprise Resource Planning (ERP) transformations are complex, high-stakes initiatives that span processes, people, and technology. While much attention is rightly given to process redesign and system implementation, organisations often underestimate the role and importance of data, until it’s too late. We estimate that in terms of key challenges, 40% of all ERP programme issues are due to data (with 40% relating to Change Management issues and 20% down to the systems).

Delaying data considerations can lead to rework, timeline delays, compliance risks, and even failed go-lives. This article outlines why data must be prioritised from the outset of an ERP transformation, key data-related considerations to build into the programme, and lessons learned from past programmes that should not be repeated. 

Why Data Needs Early Attention 

Data is the Foundation of the ERP System 

ERP systems are only as good as the data that flows through them. Every business process relies on clean, structured, and accessible data. If data is inaccurate, incomplete, or inconsistently defined, even the most sophisticated ERP will fail to deliver value. 

Data Drives Configuration Decisions 

Key ERP design decisions, such as how to structure the chart of accounts, cost centres, suppliers, and product hierarchies are directly impacted by the current and future state of data. If legacy data structures are not understood early, configuration decisions may be made on false assumptions, requiring rework or remediation later. 

Data Impacts Testing, Training and Cutover 

End-to-end testing requires production-like data. Training scenarios need real examples. Cutover plans hinge on data migration. Ignoring data until these phases risks delays and significant manual effort, undermining confidence in the new system. 

Data Migration Is Not Just a Technical Exercise 

Migration from legacy systems to the ERP requires mapping, cleansing, validation, and transformation. This isn’t something that can be left to IT alone. It demands significant business involvement to define ownership, validate quality, and approve mappings and transformation logic. 

Regulatory and Compliance Dependencies 

Incorrect or non-compliant data (e.g., in financial reporting, VAT, or GDPR-sensitive records) can have regulatory consequences. Data integrity must be safeguarded early to ensure legal and compliance obligations are met. 

Key Data Considerations in ERP Transformation 

Establish a Data Strategy Early 

Define a clear data strategy aligned to business goals. This should include: 

• How much data will be migrated and what level of detail is needed 
• Data governance and ownership models 
• A framework for master and transactional data 
• Naming conventions and standards 
• The approach for cleansing, deduplication, and harmonisation 

Define Data Ownership and Governance 

Who owns customer master data? Who signs off vendor hierarchies? Without clear ownership, data quality issues get buried or go unresolved. Assign responsible data owners and governance forums to ensure accountability. 

Undertake Early Data Profiling and Assessment 

Understand your data before trying to move it. Early profiling will reveal: 

• Duplicates and inconsistencies across systems 
• Missing values and outdated entries 
• Structural mismatches with the new ERP (e.g., field lengths or hierarchies) 

This activity helps define the scale of the data quality effort required. 

Design the Target Data Model in Parallel with Processes 

ERP transformations usually drive standardisation and simplification. Data structures in the new system will likely differ from legacy systems. The design of these structures should evolve alongside process design, not after. 

Start Cleansing and Rationalisation Early 

Data cleansing is time-consuming. It often requires manual review and business sign-off. Starting too late results in either rushed cutover or the ERP inheriting bad legacy data. Begin this activity as soon as legacy profiling is complete. 

Plan Data Migration Iteratively 

Do not treat data migration as a one-off final cutover activity. Plan for: 

• Multiple dry run migrations to refine mapping, tools, and sequencing 
• Iterative testing with increasing complexity and volume 
• Validation and reconciliation processes to confirm accuracy 

Consider Integration and External Data Sources 

ERP systems rarely operate in isolation. Consider what external or third-party data needs to be fed into the system (e.g., tax engines, banks, other third parties) and ensure it’s clean, compatible, and contractually agreed. 

Leverage Automation Where Possible 

Data migration and cleansing can be partially automated through tools such as ETL pipelines, data quality platforms, and ERP-native migration accelerators (e.g., SAP’s Migration Cockpit). These should be explored early to reduce manual workload and error. 

Lessons Learned from Previous Programmes 

“We’ll Clean It Later” Always Backfires 

Many programmes defer cleansing activities under the assumption that they can “clean on the way in” during the final stages. In reality, time constraints and competing priorities during cutover make this impossible. Poor quality data ends up being loaded into the new system, compromising performance and trust. 

Lack of Business Engagement Causes Bottlenecks 

Data cannot be owned solely by IT. Business users must be engaged from the start to define rules, review data sets, and validate converted data. Programmes that fail to secure this buy-in suffer delays and pushback at go-live. 

Legacy Complexity Is Underestimated 

Most legacy environments are far more complex than assumed – multiple versions of the truth, workarounds, undocumented fields. Programmes that assume a simple 1:1 mapping to ERP structures typically run into design issues, reconciliation failures, and scope creep. 

Not Enough Dry Run Migrations 

Some transformations cut corners by limiting dry run migration cycles. This increases the risk of failure during cutover, when errors are most costly. Mature programmes plan for three or more full dry runs to flush out issues in advance. 

Unclear Data Cut-off and Freeze Points 

Without well-communicated cut-off dates for data changes in legacy systems, users continue to enter data during migration, leading to discrepancies and confusion post-go-live. Strong governance and communication are required to manage this transition. 

Training Doesn’t Reflect Real Data 

Training with dummy or irrelevant data prevents users from building confidence in the new system. Better programmes ensure training uses realistic, converted data sets that reflect actual business scenarios. 

No Plan for Ongoing Data Management 

Some programmes end at go-live without establishing a framework for long-term data governance. This allows bad habits to return, gradually degrading the ERP’s performance and reliability. 

In conclusion, data is not a bolt-on workstream or a technical detail, it’s a foundational pillar of any ERP transformation. Without early and sustained attention to data strategy, quality, governance, and migration, even the best-designed systems can fail to deliver value. The most successful programmes treat data as a first-class citizen from day one, embed business ownership, and plan iteratively and pragmatically. 

By learning from the mistakes of past transformations, delayed profiling, lack of ownership, underestimating complexity, organisations can avoid costly missteps and ensure their ERP becomes a genuine enabler of business performance. 

If you’re shaping your S/4HANA Transformation programme, midway through or facing a recovery, and would like a sounding board, please get in touch, we’d love to talk.