What is SAP Data Migration? (7 Phases)

What is Data Migration?

When organizations upgrade or switch systems such as migrating to advanced options like SAP S/4HANA, the data needs to be shifted to the new system through data migration. SAP Data Migration plays a key function in the business environment since it allows the continuity of business operations while enabling upgrade of systems. When performed properly, data migration also ensures data integrity, and improved system performance.

Significance of Data Migration

Data migration basically means transferring data from legacy systems to new platforms, while ensuring that the information remains consistent, accurate, and usable. During system upgrades, cloud migrations, or when consolidating data from different sources, a smooth migration process is critical for business continuity. A successful migration reduces the risk of data loss, ensures compliance, and enables the new system to fully utilize the migrated data, hence supporting better decision-making and business efficiency.

Goals of Data Migration Projects

The main goals of data migration projects are to ensure that data is transferred accurately, efficiently, and with minimal disruption to business operations. Some of the key objectives of any data migration project are as follows:

• Maintaining Data Quality
• Minimizing Downtime
• Ensuring Compliance with Regulations
• Enabling A Smooth Transition to New Systems.
• Improving system performance
• Ensuring migrated data is utilized for analytics, reporting, and other business functions.

Key Differences: Data Migration, Data Conversion, and Data Integration

The terms data migration, data conversion, and data integration are often used interchangeably, but they have specific meanings as explained below in table:

Data Migration	Data Conversion	Data Integration
Data migration is the process of moving data from one system to another, often as part of an upgrade or system transition.	Data Conversion involves changing the format or structure of the data to make it compatible with the new system.	Data Integration refers to the process of combining data from multiple sources into a unified view for real-time or ongoing data exchange

What is SAP Data Migration?

SAP data migration is a process that deals with the transferring data from old SAP or non-SAP systems into the new SAP landscape, like SAP S/4HANA, with the primary goal of ETL – Extract Transfer Load of data while upholding the credibility of data within the bounds of the target system’s data architecture. This process enables organizations to efficiently adopt and use a new SAP system as well as enables them to maintain business continuity during the whole process of implementation or upgrading SAP.

For a successful transfer to occur, organizations need to focus on the following crucial steps:

• Proper planning
• Use of the right tools (for example the sap s/4hana migration cockpit)
• Maintenance of data quality specifications
• Regular communication among stakeholders.

Concentrating on these points, allows companies to transfer to their new ERP ecosystem with least friction.

Benefits of SAP S/4HANA for Data Migration

SAP S/4HANA has built-in tools, including Migration Cockpit, that offers various benefits as follows:

• Seamless data transfer from legacy systems
• Data consistency and quality are guaranteed by predefined templates
• Automated mapping of data fields and structures
• Stronger validation mechanisms like data consistency checks, predefined rules, and audit trails
• Reduced complexity due to a leaner data structure, centralized interface for managing migration tasks, and pre-built tools like SAP Migration Cockpit
• Faster migration processes
• Real-time data integration support
• Minimizes disruptions with guided workflows, real-time data transfer, phased migration, and support for parallel operations
• Improves data accuracy
• Advanced analytics and process automation for businesses

Data Migration Requirements for SAP S/4HANA

A successful migration project depends on addressing specific data requirements such as to ensure a seamless transition. This requires planning, maintaining data quality, adhering to standards, and fostering collaboration among stakeholders. Below are some key considerations for data migration to SAP S/4HANA.

Migrating Data Between Systems (SAP and Non-SAP)

Data migration to SAP S/4HANA involves transferring data from a systems like SAP ECC. This requires establishing reliable connectivity, accounting for differences in data formats, and ensuring compatibility with SAP S/4HANA’s data structures.

Ensuring Data Quality and Integrity

Maintaining high data quality and integrity is critical when migrating to SAP S/4HANA. This involves validating data accuracy, resolving inconsistencies, and ensuring that data adheres to the target system’s standards.

Standards for Data Mapping, Cleansing, and Conversion

Data mapping, cleansing, and conversion requires following a standardized approach to align source data with SAP S/4HANA’s data structures. This includes defining mapping rules, eliminating duplicates, and converting data into compatible formats.

Stakeholder Roles and Participation

Effective data migration requires active participation from key stakeholders, including IT teams, data stewards, and business users. Having well-defined roles and responsibilities along with regular communication and clear alignment on data requirements are key to the successful execution of the migration plan.

Data Migration Phases in SAP Activate Framework

SAP migration projects can be challenging even for experienced project managers. To ensure that SAP data migrations are implemented smoothly, SAP has developed the SAP Activate framework. The SAP Activate Methodology is a key part of the SAP Activate framework which is designed to help project managers leverage SAP solutions better. This framework has three pillars:

• SAP Best Practices
• Guided Configuration
• SAP Activate Methodology

SAP Activate Methodology

The SAP Activate methodology provides a structured framework that helps plan and implement complex SAP solutions. It consists of specific roadmaps that improve the quality of the project and enable successful implementation. SAP’s goal is to provide complete Application Lifecycle Management to accelerate the S/4HANA migration process.

Integration of Data Migration into SAP Activate Phases

The SAP Activate Methodology is divided into six phases from discovering the solution to running it successfully as below:

• Discover
• Prepare
• Explore
• Realize
• Deploy
• Run

Let’s look into each of these phases.

Discover

In this phase, users explore the features and the benefits of the solutions that are available to support the migration. This involves taking a trial to identify the solution’s capabilities, determining suitability, and assessing its value for the business. The aim here is to select a roadmap that best aligns with the needs of the enterprise.

Prepare

This phase is where the actual planning begins. From project initiation, defining governance, assigning roles and responsibilities to finalizing management plans and validating activities, the Prepare phase is all about gearing up and setting up a robust governance structure.

Explore

The Explore phase is where a fit-to-standard analysis is performed on the working system to identify the fit of the practices-based solution. The project team collects delta configuration requirements, identifies any gaps, and captures configuration values. This information becomes part of the backlog which is then implemented in the next phase.

Realize

The Realize phase takes an agile approach to incrementally build, configure, and test functionalities based on backlog priorities. Integration testing and user acceptance testing are performed to prepare for deployment.

Deploy

This is the phase where the new SAP environment goes live. To minimize disruptions, it is recommended to go live during off hours. System sustainment and hyper-care activities also form a part of this phase, once the go-live is complete.

Run

Finally, the Run phase is about continuous operations and improvement. This includes end-user support, system monitoring, and iterative improvement to optimize the solution.

Seven Key SAP Data Migration Phases

Migrating data to a new SAP environment successfully is a challenging process. Considering the volume of data and the complexity of SAP systems, it requires a significant effort across various teams to complete a migration project. While there is no hard rule to the process of migration, it can broadly be divided into seven phases as follows:

1. Data Analysis
2. Data Cleansing
3. Mapping
4. Implementation
5. Testing
6. Validation
7. Productive Load

Let’s look at each of these phases

Phase 1: Data Analysis

The analysis phase can be started with the Prepare phase of the SAP Activate methodology since it requires you to put your processes in place. This also makes a great stage to identify and analyze business objects, data migration objects, and source systems to take decision that are critical for the migration journey.

Differentiating Business Objects and Migration Objects

Business Object	Migration Object
A business object is an individual data object that is needed to create a business process, such as a material, a customer, or a purchase order.	A migration object can be a business object or a part of a business object.

There might be cases where a single business object needs to be divided into multiple migration objects because it has different data sources or a different migration interface.

Master Data vs. Transaction Data

Master Data	Transaction Data
Master data is data that is the basis of business objects and it usually does not change much over a specific period. Some examples of master data objects could include: Bill of materialsProductSupplierCustomer	However, transaction data changes continuously and transaction data object typically include items like: Account dataOrdersDocuments

Phase 2: Data Cleansing

Data cleansing is usually not given as much importance as it should be during a data migration project. It can be a great opportunity to take stock of all the data in your systems and clean up data errors such as duplicate records, incomplete data, inconsistent data, invalid data, obsolete data and systematic errors. Ideally, it’s best to perform data cleansing in the source system, however, it also can be done in the data transfer phase. Conversions rules can help you convert systematic errors in your source data into clean load data while insights from machine validations such as automated rule application, duplicate detection, anomaly detection, data mapping accuracy, error correction suggestions, real-time feedback, consistency checks and historical data validation can be used to clean up data in the source systems.

Importance of Early Start

It is best to start cleaning up data as early as possible during a migration project. Most organizations already have processes in place to check the data for errors and execute a clean-up. These processes should be implemented at the very beginning of the migration so that the data is ready.

Using Machine Validations for Cleansing

Another important step is identifying current data cleansing rules and matching them with the conversion rules. When you begin the process, the initial data transfer test will provide information and insights about the quality of the source data. Use this to guide your cleansing process.

Phase 3: Mapping

The mapping phase starts once the data migration objects have been identified and it is recommended to get this phase done early. Mapping essentially involves looking at the structures and fields of your source system and mapping them to structures and fields of the target system. Since this process is done on paper, it is also called paper mapping.

Field and Structure Mapping Techniques

Field Mapping	Structure Mapping
Field mapping involves alignment of source system fields with their respective fields in the target SAP system to ensure accurate transfer of data.	Structure mapping is aligning the hierarchical data structures of the source system with those of the target SAP system. It ensures that complex data relationships, such as tables and nested records, are accurately represented in the target environment.
Technique for Field Mapping: Techniques used for structure mapping include one-to-one structure mapping, where the source and target structures directly match, and hierarchical mapping, which uses nested or layered source structures to match the target system’s requirements.	Technique for Field Mapping: Techniques used for structure mapping include one-to-one structure mapping, where the source and target structures directly match, and hierarchical mapping, which uses nested or layered source structures to match the target system’s requirements.

Integration of Conversion Rules

In most cases, the configuration documents can provide the initial direction needed to create conversion rules. These rules govern how data is transformed during migration to make sure that it conforms to the target system’s format, logic, and constraints. As mapping progresses and data migration testing begins, the post-processing resulting from these tests also provide insights that will enhance the mapping process and conversion rules.

Phase 4: Implementation

The Implementation phase focuses on developing and executing data extraction and migration programs to transfer data from source systems to the target SAP S/4HANA environment. Early in this phase, small functional tests are run to validate the accuracy of data extraction and transformation programs. These initial tests help identify and address any issues in the ETL process.

Once most of the conversion or migration rules are established, the first test loads are performed to evaluate how accurately the extracted data matches the target system’s requirements. These test loads help uncover discrepancies in data structure, format, or logic that might require refinement.

By identifying and addressing these discrepancies early, the migration team can improve the reliability of the extraction and transformation processes, which lays a strong foundation for subsequent testing and validation phases.

Phase 5: Testing

As mentioned in the previous phase, it is best to start testing at the earliest. There is no substitute to testing and the more complex your data migration object and conversion rules, the greater is the need for testing.

Functional and Load Tests

Functional Testing	Load Testing
The purpose of functional testing is to ensure that the migrated data supports the business processes as intended in the target SAP system. Purpose: The focus of this test is on verifying data integrity, accuracy, and alignment with functional requirements.	Load testing, on the other hand, evaluates the target SAP system’s performance and stability under real-world data volumes and operational loads. Purpose: This test checks if the system can handle the data size and user activity without any degradation in performance.

Strategies for Effective Data Testing

Testing is both critical and time-consuming, so it is advised to start early and plan the testing strategically:

• Plan an early data migration testing strategy, especially for multiple test cycles.
• Carefully plan the systems, timing, and sequence for testing activities.
• Ensure test systems are cleaned up or recovered after each test.
• Define a robust backup and restore concept for test systems.
• Determine the time required for backup and restore processes.
• Consider using database snapshots to capture the state of a database at specific times.

Phase 6: Validation

Validation is the phase where the accuracy, completeness, and consistency of the data transferred from source systems to the target SAP system is verified. The greater the quality master data and transaction data, the greater the probability of error-free operation in the new system. Machin-based solutions help to not only validate the date but also identify errors in the source data to aid the cleansing process.

Pre and Post-Migration Validation Methods

There are two different methods by which data can be validated as follows:

Pre-Migration Validation	Post-Migration Validation
Pre-Migration Validation is used to check if the source data is accurate, complete, and ready for migration. It involves preparing and verifying the data before starting the migration process.	Post-Migration Validation is performed after data has been successfully migrated. In this case, the goal is to endure that data meets the functional and business requirements in the target system. Ideally, using both methods of validation is recommended to ensure a successful migration.

Phase 7: Productive Load

This is the final phase of the data migration process and involves loading the validated and transformed data into the live SAP S/4HANA production environment. This is done after all testing, validations, and rehearsals have been completed. However, it’s more than just flipping over the switch.

Managing Downtime and Cutover Challenges

The Productive Load process starts with a well-defined cutover plan to minimize disruptions and create smooth transition to the live SAP environment. It is recommended to take a full backup of the production system as a safety measure. Monitoring the data loading process closely is key to identify and address errors in real time. All stakeholders must be informed about the transition with clear communication about the schedule and systems that are being migrated. When planning the drafting communication, make sure to take into consideration factors like public holidays, time zones, and seasonal schedules across all company locations. And lastly, always make sure the support team is on standby to address and resolve errors.

Types of SAP Data Migration

The process of migrating data is not always the same. While the overall phases are similar, depending on the type of data migration, there could be additional steps to ensure that the data is transferred safely to the new system. There are four main types of data migration as follows:

• Database Migration
• Storage Migration
• Cloud Migration
• Application Migration

Let’s look at each type of data migration.

Database Migration

Database migration is the process of moving data from one database system to another. In this type of migration, the data is modified without changing its structure. The changes can occur in the data language or protocol when the data is migrated to the new system. This type of migration requires detailed planning and takes into account activities such as assessing the database storage capacity, and schema requirements of the target system.

SAP ECC to SAP S/4HANA Migration

The migration from SAP ECC to SAP S/4HANA requires a significant database migration, since SAP S/4HANA runs exclusively on the SAP HANA in-memory database. This migration is critical for businesses looking to take complete advantage of the improved features offered by SAP S/4HANA.

Read more about Everything You Need to Know about S/4HANA migration.

Storage Migration

When data is moved from one storage system to another, it is known as Storage Migration. It is usually done to improve performance, cost-efficiency, scalability or infrastructure modernization. Storage migration is done when migrating from on-premises storage to cloud storage solutions or when organizations want to upgrade to a high-performance storage system. In the case of S/4HANA migration, it could mean migrating to the S/4HANA Cloud.

Cloud Migration

Several organizations are migrating their data, including SAP data, to the cloud because of benefits like scalability, performance, real-time analytics, and process efficiency. SAP offers multiple deployment options like SAP S/4HANA Cloud – Public Edition and SAP S/4HANA Cloud – Private Edition. This also means customers don’t need to worry about managing and maintaining the cloud infrastructure.

Application Migration

Application migration is when organizations move from their existing system to a new system or provider. This can be challenging since every application has its unique data model, and program. From the interface to the operating systems to configurations; there is a complete change in the environment. An application migration requires greater care and effort to ensure that data integrity and security in maintained during the migration process.

Tools for SAP Data Migration

For SAP customers who are embarking on the migration journey, it’s worth taking the time to know all the tools that are available to support the process, both from SAP and other vendors in the marketing. Here’s a list of the various type tools available and what they have to offer:

On-Premise, Open-Source, and Cloud-Based Tools

On-premises Tools	Open-source Tools	Cloud-based Tools
These tools or services are used to transfer data between two or more servers or databases. Usually, they are employed by medium to big enterprises who do not want to move their data to the cloud dude to security concerns.Example: SAP Data Services (SAP DS), SAP HANA Smart Data Integration (SDI)	Since these tools are open source, they are free or available for a lower cost. The downside is that they require some level of coding expertise and may not be as easy to use as a commercial tool.Example: Talend Open Studio, Apache Nifi, Hevo Data	This category of tools is designed to enable migration of data to the cloud from an on-premises application or database, or from one cloud to another. These tools are more adept at handling different types of data and are ideal for organizations who want to migration to the cloud.Example: SAP Migration Cockpit, Azure Data Factory, Snowflake

ETL (Extract, Transform, Load) Tool

An ETL tool is a type of software used in data integration to enable Extracting, Transforming, and Loading (ETL) data from different sources into a central repository, like a data warehouse or data lake. ETL tools can be used when dealing large datasets and they help in automating the entire process thereby improving efficiency and reducing errors.

Comparison of Migration Tools

EMIGALL

EMIGALL is a specialized data migration tool offered by SAP for migrating data into SAP IS-U (Industry-Specific Utilities) systems. It focuses on bulk data transfer, particularly in utility-specific modules.

BODS (Business Objects Data Services)

SAP BODS is a comprehensive ETL (Extract, Transform, Load) tool used for data integration, cleansing, and migration. It is widely used for migrating data to SAP systems.

Batch Data Conversion (BDC)

BDC is an SAP-based legacy data migration tool that uses transaction recording to upload data into SAP systems. It supports both manual and automated data transfer methods.

Legacy System Migration Workbench (LSMW)

LSMW is an SAP-native tool designed for migrating data from non-SAP systems to SAP systems. It uses a rule-based approach to map and transform data.

Tool	Best For	Key Strength	Ideal Use Case
EMIGALL	SAP IS-U migration	Utility-specific predefined templates	Utility companies implementing SAP IS-U
BODS	Complex migrations to SAP	Advanced ETL and data quality tools	Enterprise-scale migrations with cleansing
BDC	Small-scale SAP migrations	Simplicity and low cost	Legacy data uploads via SAP transactions
LSMW	Non-SAP to SAP migrations	Wizard-based automation	Migrating data from legacy systems to SAP

SAP S/4HANA Migration Cockpit

The SAP S/4HANA Migration Cockpit is an integrated tool designed to simplify the data migration process for moving to SAP S/4HANA. It provides predefined migration objects, automated mapping, and flexible customization options to ensure accurate and efficient data transfer from legacy systems. Here are some of top benefits of SAP S/4HANA Migration Cockpit:

Pre-configured Content

Pre-configured content in the migration cockpit includes predefined migration objects for commonly used business data like customers, vendors, and transactional records. These templates are aligned with SAP best practices, enabling quick and consistent data migration.

Automated Mapping

Automated mapping in SAP S/4HANA Migration Cockpit eliminates manual field mapping by automatically aligning source data fields with SAP S/4HANA data structures, ensuring seamless and efficient data integration with minimal errors.

Use of Migration Object Modeler (LTMOM)

The Migration Object Modeler (LTMOM) is an advanced feature that allows users to customize predefined migration objects or create new ones. It provides flexibility for handling complex data structures and unique business requirements during migration.

Best Practices in SAP Data Migration

Data migration in itself is a multi-step process that requires meticulous planning as well as execution. While following best practices ensures smoother transitions, challenges such as data integrity, system compatibility, and resource allocation often arise. Understanding both the best practices and prospective challenges is critical to notice the potential risks that can impact the overall process of data migration.

Data migration does not begin during the actual execution of the tasks, but rather at the planning phase. Setting clear targets, having a grasp on the types of data, and securing inputs from the right resources during every single stage of the migration process are very important. It is crucial to conduct comprehensive tests, establish schedules, and communicate effectively with the appropriate parties to ensure that the transition goes as planned, and with minimal disruption.

Stakeholder Involvement

Including all relevant stakeholders—such as business leaders, information technology teams, and beneficiaries—in the course of the migration is a requirement for its success. This enables them to facilitate the formulation of clear-cut objectives, highlight potential risks well in advance, and make sure that users of the new system are well acquainted with the interface to increase the chances of a smooth transition.

Backup and Contingency Plans

Having a good approach to data backup is extremely important in reducing overall risk that might occur during the data transfer process. Backup of the old and new systems can be done more frequently along with a good contingency plan.

Common Challenges in Data Migration

Migrating SAP data is no easy task and here are some of the key challenges organizations need to be aware of:

Poor Legacy Data Knowledge

Assuming legacy data can easily fit into a new system can lead to failures, especially in user acceptance. Common issues like duplication, missing data, misspellings, and inaccuracies often go unnoticed, creating gaps in readiness.

Integration Issues

Data migration involves diverse teams and tools, such as spreadsheets for data definitions, which are error-prone and hard to align with transformation processes. Misaligned technologies can lead to failures in design, testing, and implementation, causing delays and additional costs.

Lack of Backup Strategies

Failing to plan for interruptions is a common pitfall in SAP data migrations. Treat data migration with the same care as transferring valuable assets, identifying potential failure points, and establishing contingency plans to protect critical data.

Tips for Effective Migration

• Plan Thoroughly: Define clear objectives, timelines, and roles to ensure a smooth migration process.
• Involve Key Stakeholders: Engage business leaders, IT teams, and end-users early to align goals and expectations.
• Test Early and Often: Conduct thorough testing at every stage to identify and address issues before going live.
• Ensure Data Quality: Cleanse and validate data to prevent errors like duplication or missing information.
• Prepare for Contingencies: Establish backup and recovery plans to mitigate risks and ensure quick recovery from failures.

Additional Factors for SAP Data Migration

In addition to following best practices, SAP migration teams need to be aware of a few other factors that might affect the migration.

Compliance with Regulations (e.g., FDA Certification)

It is important to keep in mind that the data migration process can be impacted by external factors like compliance regulations.

For example, the pharmaceutical industry requires that all the migrated master data is validated, and the validation reports must be documented to meet compliance. So, it’s critical to meet any mandatory data privacy and security regulations to ensure the success of the migration project.

Global and Local Scheduling Factors

Effective data migration requires considering both global and local scheduling factors. These include understanding regional and local working hours, public holidays, and time zones to ensure minimal disruption and proper coordination across different teams and systems.

Public Holidays

Public holidays in different regions can have an impact the availability of key resources and the migration timeline. Scheduling migration tasks around these dates can help avoid delays and ensure that the necessary resources are available when needed.

Time Zones

Time zone differences can complicate coordination between teams in various regions. Proper planning is required to schedule migration tasks at times that align with the working hours of all locations. This ensures smooth execution without unnecessary downtime.

Managing Cloud Environment Constraints

Cloud environments have specific constraints such as bandwidth limitations, resource availability, and service-level agreements (SLAs). Managing these factors during data migration ensures optimal performance, prevents bottlenecks, and minimizes the risk of disruptions.

Conclusion

Successful SAP data migration requires adopting best practices like thorough data assessment, defining clear migration goals, maintaining data quality, and using standardized processes for mapping and transformation. By focusing on these principles, businesses can minimize risks and ensure a seamless transition to SAP systems like S/4HANA.

SAP provides a range of powerful tools, including the S/4HANA Migration Cockpit, Rapid Data Migration (RDM), and LSMW, to facilitate, streamline, and automate data migration processes. These tools provide predefined templates, and strong validation mechanisms, helping organizations handle complex migrations with efficiency and accuracy.

Comprehensive planning, rigorous testing, and thorough validation are critical to a successful migration. Planning aligns the project business objectives, testing identifies potential issues before go-live, and validation ensures data integrity and compliance. These steps reduce disruptions and enable organizations to fully leverage their SAP systems.