SAP Articles
5 Data Migration Strategies to avoid SAP Project Disasters
Noel DCosta
- Last Update :
Let me tell you about a manufacturing industry company that spent 18 months and $4.5 million on their SAP implementation. Launch day arrived. Everyone was nervous but excited. Then… disaster struck i.e. Data Migrated failed!
Nothing worked right. Customer data was missing. Inventory numbers were wrong. Accounting couldn’t close the books. The CEO was furious.
What happened? Their data migration failed them. Not the software. Not the implementation team. The data.
You might be surprised to learn this isn’t rare. About 40% of SAP implementations run into serious problems, and data migration issues are the culprit in nearly half of those cases. I’ve seen companies lose millions because they treated data migration as an afterthought.
Here’s the truth. Your SAP implementation will succeed or fail based on how well you handle your data migration. It’s that simple.
In this article, I’m going to walk you through everything you need to know about SAP data migration. You’ll learn why it’s different from other migrations. I’ll show you the common problems that trip up even experienced teams. We’ll look at tools that can save you time and money.
Most importantly, I’ll give you a step-by-step strategy that works. I’ve used it with clients across manufacturing, retail, and healthcare. It’s not theory – it’s practical advice you can use right now.
So, if you are keen to know more, let’s dive in and understand how to make sure your SAP Implementation has a successful data migration.
Data migration quality directly impacts your SAP implementation's success, yet 40% of companies underestimate its complexity until it's too late.The cost of fixing data migration errors post-implementation is typically five times higher than properly investing in data governance and testing beforehand.
What Makes SAP Data Migration Different?
Not all data migrations are created equal. I’ve worked on dozens of ERP projects, and SAP data migration stands apart from the rest.
First off, SAP’s data structures are complex. Really complex. We’re not talking about simple tables with clear relationships. SAP has thousands of tables with cryptic names like MARA, MARC, and MBEW. Each module has its own structure. Materials Management doesn’t look anything like Finance. And if you map something to the wrong field, your whole system might reject it.
Some key complexity factors include:
- Over 10,000 tables in a typical SAP ERP system
- Field-level validations that can reject entire datasets
- Custom fields that need special handling
- Different structures for historical vs. current data
Then there’s the interdependency issue. In SAP, everything connects to everything else. Your customer master data links to sales orders, which link to deliveries, which link to billing documents. If you miss one connection, the whole chain breaks. This makes migration planning much harder than with standalone systems.
The volume is another challenge. Most companies I work with are shocked by how much data they actually have. One client thought they had about 50,000 material records. Turns out it was closer to 500,000 when they counted all the variants and plant-specific records. SAP needs to handle all of it without slowing down.
Don’t forget regulatory compliance. Depending on your industry, you might need to maintain data for 7+ years for audits. Healthcare clients need HIPAA compliance. Financial services have SOX requirements. SAP data migration must preserve all this historical information while maintaining compliance trails.
Many companies that I work with, think that they could use the same migration approach they used for their last ERP. That’s a big mistake. They don’t account for SAP’s validation requirements, and hence have to do a lot of rework.
Your SAP data migration needs specialized planning, tools, and expertise. It’s not something you can figure out as you go along. Trust me on this one.
Common Data Migration Mistakes I've Seen
I’ve seen smart, capable teams stumble with SAP data migration. Let me share the most common mistakes I see repeatedly. Learn from others’ pain – it’s cheaper that way.
1. Underestimating timeline and resources
Almost everyone does this. One client told me, “We’ll just export to Excel and import to SAP. Should take a month.” Fourteen months later, they were still migrating data.
SAP data migration typically takes 20-30% of your entire implementation timeline. Not 5%. Not 10%. If your project is scheduled for 12 months, expect 3-4 months for data migration work.
Resource-wise, you need dedicated people. Not teams who will work on migration “when they have time.” That never happens. You need database experts, business analysts who understand the data, and testers. One manufacturing client staffed just two part-time resources for data migration. They ended up delaying go-live by six months.
2. Poor data quality in legacy systems
Your legacy data is probably worse than you think. Duplicate customer records. Products with incorrect units of measure. Missing pricing information.
I worked with a distribution company that discovered 40% of their product descriptions contained errors. Another found thousands of defunct customers still marked as active. This garbage data will break your new SAP system if you migrate it as-is.
3. Lack of business involvement
Technical people can’t clean data alone. They don’t know which customers are real vs. test accounts. They can’t tell if a price looks reasonable.
When business users aren’t involved in data migration, bad things happen. I saw one company migrate inventory worth millions that didn’t actually exist – because the warehouse manager wasn’t consulted.
4. Insufficient testing
Many companies run one test migration and call it done. Big mistake.
You need multiple rounds:
- Initial test migration to find basic issues
- Functional testing to verify business processes work
- Integration testing across modules
- Performance testing with full data volumes
- Final verification before go-live
One retailer skipped thorough testing and found out at go-live that product costs weren’t migrating correctly. Their financial reports were useless for months.
5. Inadequate data governance
Who decides which data to migrate? Who approves the final data sets? Without clear governance, these questions cause delays and conflicts.
I’ve watched team members argue for weeks about whether to migrate historical sales data. With proper governance, that decision would have taken one meeting.
Without data governance, you also risk repeating the same data quality problems in your new system. It’s like moving into a new house and bringing all your junk with you.
Learn from these mistakes. Your SAP implementation will thank you.
Common Data Migration Mistakes and Companies That Suffered Them
| Data Migration Mistake | Company | Impact |
|---|---|---|
| Poor Data Mapping | Target | Incorrect mapping of data between systems led to data inconsistency. |
| Lack of Testing | Jet Airways | Failure to test migration process caused system downtime. |
| Inadequate Backup Plans | HealthCare Co. | Data loss occurred due to poor backup procedures. |
| Underestimating Complexity | Toyota | Underestimating the scope of migration led to delays and budget overruns. |
| Lack of Stakeholder Involvement | Deloitte | Not engaging key stakeholders resulted in misaligned expectations. |
Building an Effective SAP Data Migration Strategy
Now that we know what not to do, let’s focus on building a strategy that works. I’ve used this approach with dozens of clients, and it consistently delivers results.
1. Data Assessment and Inventory
Start with a complete inventory of your data. You can’t migrate what you don’t know you have. I recommend creating a data catalog that identifies:
- All data sources (not just your main ERP)
- Data volumes for each object type
- Data owners from the business
- Current quality levels
- Business criticality (high, medium, low)
- Age of data
This sounds basic, but it’s often eye-opening. One client discovered they had customer data in 14 different systems. Another found they were maintaining three separate product masters.
Don’t rush this step. A thorough assessment will save you countless headaches later. Take screenshots of your legacy systems’ screens and reports. They’ll be invaluable when mapping to SAP.
2. Cleansing and Preparation
Data cleaning isn’t sexy, but it’s essential. Start cleaning early – ideally, months before you begin actual migration.
Begin with your master data (customers, vendors, materials, etc.). These form the foundation of your SAP system. If they’re wrong, everything built on top will be wrong too.
I advise clients to:
- Remove duplicates (you’d be surprised how many there are)
- Standardize formatting (phone numbers, addresses, etc.)
- Add missing mandatory fields
- Archive obsolete records rather than migrating them
- Correct obvious errors
One manufacturing client reduced their material master by 60% just by eliminating obsolete products. This dramatically simplified their migration and improved system performance.
3. Mapping Legacy to SAP Structures
This is where the technical complexity really kicks in. You need to define exactly how each field in your legacy system translates to SAP.
Create detailed mapping documents that show:
- Source field name and description
- Target SAP field (table and field name)
- Transformation rules (if any)
- Default values for new SAP fields
- Validation requirements
Don’t try to map everything at once. Start with one data object (like customers) and get it right. Then move to the next.
I’ve found workshops work well here. Get your legacy system experts and your SAP consultants in the same room. Walk through each field one by one. It’s tedious but necessary.
4. Validation Rules and Business Logic
SAP enforces data quality through validation rules. You need to understand these rules before you migrate.
For example, in SAP, you can’t create a sales order for a customer that doesn’t exist. Seems obvious, but I’ve seen migrations fail because customer numbers changed and sales orders weren’t updated to match.
Document all these interdependencies. Test them rigorously. One retailer I worked with spent three extra weeks just mapping product hierarchies because they hadn’t properly understood SAP’s validation requirements.
5. Migration Approach Options
You have two main options: big bang or phased.
With big bang, you migrate everything at once and go live with all modules simultaneously. It’s faster, but riskier.
With a phased approach, you migrate data for one module, go live, then move to the next. It spreads the risk but extends the timeline.
Your business complexity, risk tolerance, and resources should drive this decision. There’s no one-size-fits-all answer.
6. Timeline Planning
Create a detailed timeline with dependencies and critical paths. Work backward from your target go-live date.
Allow time for:
- Multiple mock migrations (at least three)
- Data validation by business users
- Fixing issues found during testing
- Final cutover activities
And always add buffer time. Something unexpected always happens.
Building an Effective SAP Data Migration Strategy and Their Requirements
| Component | Requirement |
|---|---|
| Data Assessment | Assess data quality, volume, complexity, and source systems. |
| Data Mapping | Define data mapping rules to ensure data compatibility between legacy systems and SAP. |
| Data Cleansing | Ensure data is accurate, complete, and consistent before migration. |
| Migration Tools | Choose the appropriate SAP migration tools for data extraction, transformation, and loading. |
| Test Migration | Perform a test migration to verify data integrity and performance. |
| Stakeholder Involvement | Ensure key stakeholders are involved to align expectations and provide necessary resources. |
| Training & Change Management | Provide training and change management to help users adapt to the new SAP system. |
Data Governance for Successful Migration
Data governance isn’t just corporate jargon. It’s the backbone of successful SAP data migration. Let me break down how to do it right.
1. Establishing Data Ownership
Every piece of data needs an owner. Full stop. Without clear ownership, decisions don’t get made and problems don’t get fixed.
I always create a RACI chart for data migration that shows who’s:
- Responsible for doing the work
- Accountable for decisions
- Consulted before changes
- Informed after changes
This clarity prevents the finger-pointing I so often see. Your customer master might need the Sales Director as the accountable owner, with sales ops people responsible for the actual work.
Be specific. “Finance owns GL accounts” isn’t enough. Name names. Put it in writing. Get everyone to agree.
2. Creating Data Standards
Your SAP system needs consistent data standards. This means agreeing on:
- Naming conventions
- Required fields
- Formatting rules
- Classification systems
- Data hierarchies
One retail client created standards for product descriptions that specified exactly what information to include and in what order. This eliminated confusion and made reporting much easier.
3. Implementing Quality Controls
You need checkpoints throughout your migration process. Don’t wait until the end to check quality.
Set up automated validation rules where possible. For example, if product codes must follow a specific format, build a check to flag exceptions.
Create dashboards to track data quality metrics. I like to use simple red/yellow/green indicators for each data object. This makes problems visible to everyone.
4. Managing Master Data
Master data deserves special attention. It’s the foundation everything else builds on.
Establish procedures for:
- Creating new master data records
- Modifying existing records
- Handling duplicates
- Managing hierarchies
- Cross-reference tables
One manufacturing client created a master data team that continued after go-live. It was one of the smartest moves they made.
5. Ongoing Maintenance Procedures
Data migration isn’t a one-time event. You need procedures for maintaining data quality after go-live.
Document how to:
- Review data quality regularly
- Clean up garbage data
- Handle new data requirements
- Manage system changes that affect data
Companies that treat data governance as a permanent function rather than a project task see much better long-term results from their SAP implementation.
Remember: good governance prevents garbage data. And garbage data will kill your SAP system faster than any software bug.
Technical Considerations for Legacy Data Conversion
Let’s get into the technical nuts and bolts of converting your legacy data to SAP format. This is where IT teams earn their keep.
1. Data Extraction Methods
How you get data out of your legacy systems matters. Your options typically include:
- Direct database queries (fastest but requires technical expertise)
- API calls (good for cloud systems but can be rate-limited)
- Standard export functions (easiest but often limited)
- Third-party extraction tools (balanced approach)
I’ve seen companies waste weeks trying to extract data via reports when direct SQL queries would have done the job in hours. One manufacturing client had to write custom scripts to extract data from their 20-year-old COBOL system. Not fun, but necessary.
Choose extraction methods that balance speed with accuracy. Document the exact queries or procedures for repeatability during test runs.
2. Transformation Tools and Technologies
Once extracted, your data needs transformation. Popular options include:
- SAP’s Legacy System Migration Workbench (LSMW)
- SAP Data Services
- Microsoft SSIS
- Informatica
- ChainSys DataTense
- Custom scripts (Python, Perl, etc.)
Each has pros and cons. LSMW is SAP-native but has a steep learning curve. Data Services is powerful but expensive. ChainSys offers good SAP-specific templates but requires licensing. Custom scripts offer flexibility but require coding skills.
I usually recommend a hybrid approach. Use standard tools for straightforward transformations and custom code for complex business rules.
3. Loading Techniques
You have several options for loading data into SAP:
- Direct database loads (fastest but bypass validation)
- BAPI/API calls (respects business logic but slower)
- Batch input sessions (good middle ground)
- Manual entry (for small volumes or sensitive data)
Speed matters, but accuracy matters more. One retail client used direct loads for reference data but BAPIs for transaction data to ensure business rules were enforced.
4. Validation Processes
Validation should happen at multiple points:
- Pre-extraction (verify source data quality)
- Post-transformation (check mapping accuracy)
- Post-load (verify SAP integration)
Build automated checks where possible. Reconciliation reports are essential – they compare source and target system counts and key metrics.
5. Error Handling Protocols
You will encounter errors. Plan for them with:
- Clear error logging and categorization
- Defined resolution paths for common errors
- Escalation procedures for critical issues
- Rollback capabilities if loads fail
Document everything. One healthcare client created an error catalog during testing that became invaluable during the actual migration.
Your technical approach should be robust enough to handle surprises but flexible enough to adapt when the inevitable unexpected issues arise.
Technical Considerations for Legacy Data Conversion
| Technical Consideration | Requirement |
|---|---|
| Data Quality | Ensure data is accurate, complete, and consistent before migration. Perform data profiling and clean-up activities to identify and rectify errors. |
| Data Mapping | Map legacy system data fields to the new system fields, ensuring compatibility. Define transformation rules and ensure they cover all data types and dependencies. |
| Data Transformation | Transform data into the appropriate format for the target system. Utilize ETL (Extract, Transform, Load) tools to standardize, aggregate, and format data correctly. |
| Data Validation | Perform rigorous validation before, during, and after migration. Use automated tools to check for data integrity, accuracy, and completeness. |
| Scalability | Ensure that the conversion process can scale for large data volumes. This includes handling high transaction data rates, large records, and complex data structures. |
| Automation | Automate the conversion process wherever possible to minimize human errors, increase consistency, and improve migration speed. This includes using scripts and batch processes for repetitive tasks. |
| Testing & Validation | Conduct thorough testing using representative sample data. Perform parallel runs, functional testing, and user acceptance testing (UAT) to ensure all data is transferred correctly. |
| Post-Conversion Monitoring | Implement a post-conversion monitoring process to ensure that all systems are functioning as expected. Track data accuracy, system performance, and address any issues in real-time. |
| Downtime Management | Plan for minimal downtime during the migration process. Ensure that users have access to critical data and systems during the migration, or provide alternative solutions. |
| Compliance & Security | Ensure that the legacy data conversion process complies with industry standards and regulations such as GDPR, HIPAA, etc. Secure sensitive data during migration through encryption and other security measures. |
| Stakeholder Involvement | Engage all relevant stakeholders in the planning, execution, and testing phases. Regular communication is critical to managing expectations and avoiding misunderstandings. |
| Documentation | Maintain comprehensive documentation throughout the migration process. This includes documenting mapping rules, transformation logic, test cases, and system configuration for future reference. |
Data Migration Tools and Technologies for SAP
Let’s talk tools. The right tools can make your migration smoother, faster, and less painful. Here’s what you should know.
1. SAP’s Native Migration Tools
SAP offers several built-in options:
- Legacy System Migration Workbench (LSMW) – The old reliable. Been around forever. Great for technical folks who know their way around SAP. Complex but powerful.
- SAP Migration Cockpit – Newer option designed for S/4HANA. More user-friendly than LSMW. Uses templates. Good for standard objects but less flexible for custom needs.
- SAP Data Services – Enterprise-grade ETL tool. Handles complex transformations well. Expensive but comprehensive. Works beyond just migration scenarios.
I’ve had clients use all three successfully. Your SAP skills and complexity will determine which fits best.
2. Third-Party ETL Tools
Several non-SAP tools work well for SAP migrations:
- Informatica PowerCenter – Enterprise-grade. Expensive but powerful.
- Microsoft SSIS – More affordable. Good if you’re already a Microsoft shop.
- Talend – Open source core with paid enterprise options.
- ChainSys DataTense – Purpose-built for SAP with useful templates.
One retail client compared costs and found third-party tools were 30% cheaper than SAP Data Services for their needs.
3. Data Quality Software
Don’t overlook these:
- SAP Information Steward
- Informatica Data Quality
- Trillium
- DataCleaner
They can automate finding duplicates, standardizing formats, and validating data. Worth every penny.
4. Automation Possibilities
Look for tools that offer:
- Reusable migration mappings
- Scheduling capabilities
- Error handling automation
- Reconciliation reporting
- Test data generation
The time saved through automation quickly pays for itself.
5. Custom vs. Out-of-Box
Pre-built solutions work for standard SAP objects. But most companies need some custom elements.
I recommend a hybrid approach. Use standard tools for 80% of objects, then build custom solutions for complex scenarios.
6. Cloud-Based Migration Tools
Cloud options are growing:
- SAP Cloud Platform Integration
- AWS Glue
- Azure Data Factory
- Boomi
They offer scalability advantages but may have integration challenges with on-premise systems.
7. Cost Considerations and ROI
Good tools aren’t cheap. Expect to spend $50K to $500K depending on complexity.
But consider the ROI: One client spent $75K on tools that saved them 2,000 person-hours. Another reduced go-live defects by 70% with proper tooling.
The right tools are an investment, not an expense. Choose wisely.
Data Migration Tools and Technologies for SAP
| Tool/Technology | Features | Use Case |
|---|---|---|
| SAP Data Services | Provides data extraction, transformation, and loading (ETL) capabilities. Supports batch and real-time data processing. | Used for high-volume data migrations, data cleansing, and integrating data from multiple sources into SAP. |
| SAP S/4HANA Migration Cockpit | A tool specifically designed for migrating legacy systems to SAP S/4HANA. Provides preconfigured templates and accelerators for data migration. | Ideal for migrating from legacy ERP systems to SAP S/4HANA with minimal disruption. |
| DBTech Data Migration | A database migration tool that offers direct connections between databases and SAP systems, automating the migration of large datasets. | Used for seamless database migrations from legacy systems or other ERP solutions to SAP. |
| SAP Landscape Transformation (SLT) | Real-time replication of data from source systems into SAP HANA. Provides real-time data transfer and data quality monitoring. | Ideal for continuous data migration and data synchronization between SAP and non-SAP systems in real-time. |
| Azure Data Factory | Cloud-based ETL tool from Microsoft, enabling data migration to SAP in hybrid or cloud-based environments. | Used for cloud-based data integration and migrations, especially when SAP is deployed on cloud infrastructure. |
| Informatica PowerCenter | Data integration platform that supports large-scale data migrations, with pre-built connectors for SAP. | Used for complex data migrations from multiple sources into SAP with data transformation and validation features. |
| MuleSoft Anypoint Platform | An integration platform that enables connectivity between SAP and other systems through APIs and microservices. | Used for integrating SAP with third-party systems and ensuring data flows smoothly during migration. |
| Dell Boomi | Cloud integration platform that supports real-time data migration, mapping, and transformation between systems. | Used for integrating cloud applications with SAP and ensuring smooth data migration in cloud-based environments. |
| SAP Cloud Platform Integration (CPI) | Cloud-based integration tool that allows for data transfer between SAP and third-party applications. | Ideal for cloud-to-cloud migrations and integrating SAP systems with other cloud applications and services. |
| Qlik Replicate | Real-time data migration tool that automates the movement of data from source systems to SAP, ensuring minimal downtime. | Used for fast and real-time data migrations with minimal operational disruption. |
| Chainsys | Provides data integration, transformation, and migration capabilities with a focus on SAP systems. Offers a cloud-native solution for seamless data migration. | Used for SAP-centric migration projects, especially for large-scale data transformation, validation, and cloud migration. |
| Syniti | Syniti offers a data migration platform with AI-powered data quality and governance features, automating and streamlining data transfer to SAP systems. | Ideal for data-driven migration projects requiring AI-driven insights, automated cleansing, and governance, especially for SAP S/4HANA migrations. |
Testing and Validation Strategies
Let me tell you something important. Testing is where the rubber meets the road. It’s where you’ll find out if your data migration will actually work or crash and burn. I can’t stress this enough – if you cut corners here, you’re setting yourself up for disaster.
1. Unit Testing Approach
You’ve got to start with the basics. Test each type of data on its own before you try putting everything together.
When I’m working with customer data, I always check these things:
- Are the field mappings actually correct?
- Do the transformation rules work like they should?
- Did all the required fields get filled in?
- Do the data formats match what SAP needs?
- Can the data pass all the validation rules?
Write up test scripts for each data type. Write down what you expect to see. Then compare what you actually got.
I was working with a manufacturing company last year, and they found problems with 40% of their material mappings during unit testing. Trust me, it’s so much better to find that stuff early than when you’re trying to go live.
2. Integration Testing
Once the individual pieces work, you need to see if they play nice together. This is where relationship problems show up.
Try testing real scenarios like:
- Can you actually create sales orders using your migrated customers?
- Do inventory transactions work with your migrated materials?
- Can you post financial stuff to your migrated cost centers?
These integration issues can be a real pain. I had a healthcare client who mapped all their customer numbers correctly, but then the sales history used a totally different format. So orders couldn’t be connected to customers. Integration testing caught this before it became a huge problem.
3. User Acceptance Testing
You absolutely need to get your business users involved. They know this data way better than your IT folks do.
Have them do things like:
- Look at the migrated data and see if it seems right
- Try to use the migrated data in real business processes
- Compare reports between your old system and SAP
- Check if all the critical business rules still work
But don’t just tell them to “check if the data looks right” – that’s too vague. Instead, say something specific like “verify customer ABC’s credit limit and payment terms.”
4. Performance Testing
A lot of people forget about performance. Just because your migration works fine with 100 records doesn’t mean it’ll work with a million.
Test with your actual production data volumes. Time how long each step takes. Figure out where the bottlenecks are.
I worked with a retail client whose migration ran perfectly with test data, but then took a full 72 hours with their real data. They had to completely redo their approach.
5. Reconciliation Methods
You need solid proof that your migration worked. Reconciliation is how you get that proof.
Some good methods include:
- Comparing record counts (like total customers, products, etc.)
- Validating financial totals (inventory value, accounts receivable, etc.)
- Sampling (detailed comparison of important records)
- Exception reporting (listing records that failed)
Build reports that do this automatically. One of my distribution clients made dashboards showing how complete their migration was and error rates for each type of data.
Yes, thorough testing takes time. But believe me, it’s nothing compared to the time you’ll spend fixing data problems after a failed go-live. I’ve seen it happen, and it’s not pretty.
Testing and Validation Strategies for Data Migration
| Strategy | Objective | Method |
|---|---|---|
| Unit Testing | Verify individual data elements or units for accuracy and completeness. | Test each data unit independently to ensure it performs as expected. |
| System Testing | Test the entire migration process to ensure that the data flows correctly and integrates with the system. | Perform a complete end-to-end system test, validating data accuracy, completeness, and system performance. |
| Integration Testing | Test the interaction between the migrated data and the SAP system, ensuring smooth data transfer and application functionality. | Simulate data transfer between systems and check for integrity, transformation issues, and system compatibility. |
| Regression Testing | Ensure that the migrated data does not affect existing system functionalities. | Run previous test cases to verify that the data migration did not break or modify the existing system functionality. |
| Performance Testing | Evaluate how well the migrated data handles the expected system workload, including response time and resource usage. | Load test the system with large volumes of data and measure how well it responds under peak conditions. |
| User Acceptance Testing (UAT) | Ensure that the final data migration meets business needs and that end users are satisfied with the outcome. | Involve key business users to test data usage scenarios and validate that migrated data meets operational requirements. |
| Data Quality Checks | Validate data accuracy, consistency, and completeness during the migration process. | Use automated tools to perform data profiling, identifying any discrepancies or anomalies in the data. |
| End-to-End Validation | Ensure that the entire migration process, including data extraction, transformation, and loading, is accurate and complete. | Verify that all data from the legacy system has been migrated correctly, mapped properly, and is functional in the new SAP environment. |
| Reconciliation | Compare source and target data sets to ensure no data is lost during migration. | Perform reconciliation checks using sample data from both source and target systems. |
| Data Migration Audits | Perform audits to verify the integrity and completeness of the migrated data after it has been moved to the new system. | Conduct regular audits of the migration logs, tracking data movement, transformation, and any exceptions during the process. |
Conclusion
Data migration can absolutely make or break your SAP implementation. Throughout this article, I’ve shared the strategies and approaches that I’ve seen work in the real world.
Remember these key points:
- SAP data migration is uniquely complex due to interdependencies
- Data governance isn’t optional – it’s essential
- Start cleaning your data early
- Choose the right tools for your specific needs
- Test thoroughly with real data volumes
When you get data migration right, the benefits extend far beyond go-live. You’ll see improved data quality throughout your organization. Your business processes will run more efficiently. Reporting becomes reliable. User adoption increases. And your total cost of ownership decreases.
Most importantly, you avoid the nightmare scenario of a failed implementation that costs millions and damages your company’s reputation and operations.
I encourage you to start your SAP data migration planning today, even if your implementation is months away. Begin with a data assessment. Identify your data owners. Start cleaning your most critical data.
Have you gone through a SAP data migration? I’d love to hear about your experiences in the comments below. What challenges did you face? What strategies worked for you? Your insights could help others avoid the same pitfalls you encountered.
Or maybe you’re planning a migration soon and have questions? Drop them below, and I’ll do my best to answer. We’re all in this together, and sharing our knowledge makes everyone’s implementation smoother.
The sooner you begin, the better your results will be. Take the first step today. Your SAP success depends on it.
If you have any questions, or want to discuss a situation you have in your SAP Implementation team, please don't hesitate to reach out!
Questions You Might Have...
1. What is SAP data migration?
SAP is an enterprise resource planning (ERP) software that organizations implement to manage business processes. In data migration, SAP refers to the target system where your business data will be moved. The migration process involves extracting data from legacy systems, transforming it to meet SAP’s specific structure requirements, and loading it into your SAP environment.
2. What are the different types of migration in SAP?
There are several types of SAP migrations:
- System migration – Moving from one SAP version to another (like ECC to S/4HANA)
- Data migration – Transferring data from legacy systems to SAP
- Platform migration – Changing the underlying hardware or database
- Cloud migration – Moving from on-premise SAP to cloud-based solutions
- Landscape migration – Restructuring your SAP system landscape
3. What is the difference between LSMW and LTMC?
LSMW (Legacy System Migration Workbench) is the traditional SAP tool for data migration. It’s technically complex but very powerful and flexible. LTMC (Legacy Transfer Migration Cockpit) is SAP’s newer migration tool specifically designed for S/4HANA. LTMC is more user-friendly with pre-defined templates but less flexible for custom objects. LSMW requires ABAP knowledge while LTMC has a more intuitive interface.
4. How to transfer data from one SAP system to another SAP system?
To transfer data between SAP systems, you can use:
- SAP migration tools like Migration Cockpit
- Client Copy (for full system copies)
- System Landscape Transformation (SLT) for real-time replication
- Transports for configuration data
- Direct database exports/imports
- BAPIs and IDocs for specific business objects
- Third-party ETL tools
The best method depends on your specific requirements, volume, and whether it’s a one-time or ongoing transfer.
5. What is SAP data migration cockpit?
SAP Data Migration Cockpit is a tool specifically designed for migrating data to S/4HANA. It provides a simplified user interface with pre-built migration templates for standard SAP objects. It supports file-based migration, direct migration from SAP systems, and staging-based migration using SAP Data Services. The tool offers monitoring, validation, and error handling capabilities to streamline the migration process.
6. What are the 3 main types of data in SAP?
The three main types of data in SAP are:
- Master Data – Relatively stable core business objects like customers, vendors, materials, and employees
- Transactional Data – Business process records like sales orders, purchase orders, and financial postings
- Configuration Data – System settings that control how SAP operates, including organizational structure, business rules, and process flows
7. What is the best ETL tool for SAP data migration?
There’s no single “best” ETL tool for SAP data migration as it depends on your specific needs. Popular options include:
- SAP Data Services – Best for SAP-centric environments
- Informatica PowerCenter – Strong for enterprise-scale projects
- Microsoft SSIS – Good for Microsoft-centric organizations
- Talend – Cost-effective with good SAP connectors
- ChainSys DataTense – Specifically designed for SAP migrations
Consider factors like your technical environment, budget, timeline, and in-house skills when selecting a tool.
8. What is data migration process?
The data migration process is a structured approach to moving data from source systems to a target system. It typically includes:
- Planning and assessment
- Data extraction from source systems
- Data cleansing and transformation
- Loading data into the target system
- Validation and reconciliation
- Testing and quality assurance
- Go-live cutover and post-migration support
Each phase requires careful planning, appropriate tools, and rigorous testing to ensure success.
9. What is data transfer in SAP?
Data transfer in SAP refers to the process of moving data into, out of, or between SAP systems. This can include initial data loads during implementation, periodic imports/exports for reporting, interfaces with other systems, or data archiving. SAP offers various tools for data transfer including Data Migration Cockpit, LSMW, BAPIs, IDocs, and direct database connections.
10. What is SAP data processing?
SAP data processing refers to how the SAP system handles, manipulates, and transforms data as it flows through business processes. This includes data entry (manual or automated), validation against business rules, processing through programmed logic, storage in the database, and retrieval for reporting. SAP’s data processing capabilities ensure data integrity, consistency, and availability throughout the organization while enforcing business rules and workflows.
