What is SAP Data Migration: Steps, Best Practices, & Strategy

Changing to a different ERP system is one of the major options a company can take, and the success of the transition is mostly determined by the speed and efficiency of your data transfer. The process that transports your company’s most important asset, its data is referred to as SAP Data Migration in the context of SAP transformations.

No matter if you are switching from SAP ECC to SAP S/4HANA or from a different legacy system, efficient data migration lays down a strong base for your operations. Information transfer is just one part of it; the other part is changing data into transparent, reliable, and useful insights that increase the value of the company. We will guide you through the key steps of SAP Data Migration, and together we will reveal its main problems and the best practices and ways to tackle them.

What is SAP data migration

Data migration is the process of moving data from one system to another. Although this might seem like an easy update, it involves changes to the database or application as well as storage. However, the main component of the data migration technique is not the “transfer” of data. If the data is heterogeneous, the migration process will involve mappings and transformations between the source and target data. This is not to suggest that integration and data transfer are related concepts. Data quality must be confirmed before migration in order to ensure a successful deployment without data loss. Every data migration project’s success rate is determined by the variety, quantity, and quality of data transferred, as well as the data migration software that enables it.

Importance of Data Migration in SAP

It is impossible to ignore data migration as part of any SAP implementation or upgrade process as it makes sure that business-critical data is reliably transferred from the old to the new SAP environment. Sometimes the quality, consistency, and completeness of a SAP project are major factors for its success. Good data migration allows companies to access the digital world of SAP S/4HANA’s powerful analytics, automation, and real-time capabilities without disruptions, thus ensuring the continuity of their business. One of the consequences of poor migration is data loss, which can subsequently lead to non-compliance, operational problems, etc. Thus, to sum up, migration of data is a strategic enabler for digital transformation that provides your company with the guarantee that its decisions are made based on accurate, consistent, and clean data.

Types of Data Migration

Depending upon the type of data migration in an SAP environment, organizations may be required to take additional steps for data migration. There are four main types of data migration

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

Database Migration

Database migration is the process of transferring data from one database to another while ensuring the core structure and integrity remain intact. The new system may use a different data language or protocol, requiring careful transformation during the move. Using tools like the SAP S/4HANA Migration Cockpit simplifies this process by enabling efficient data transfer, mapping, and validation. Successful database migration also demands detailed planning and an assessment of the target system’s storage capacity and schema requirements..

Storage Migration

Storage Migration is the term given to the process whereby data is transferred from one storage system to another. It is generally carried out to bring about improvement in some area like performance, cost-efficient, 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.

Cloud Migration

A number of organizations, including those that deal with SAP data, are taking advantage of the cloud to the extent that they are switching to it entirely due to its scalability, performance, real-time analytics, and process efficiency among others. SAP has offered a number of deployment options like SAP S/4HANA Cloud – Public Edition and SAP S/4HANA Cloud – Private Edition among others. Hence, this not only implies that the cloud infrastructure will be taken care of but also the customers will not be involved in managing and maintaining it.

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 is maintained during the migration process.

Types of Data Migration Tools

When moving your data, there are three main types of data migration tools to consider:

On-premises Tools: On-premise services are meant to transfer data between two or more servers or databases within a big or medium enterprise/network without moving data to the cloud. Some companies choose on-premise solutions for the fear of security breaches. These solutions are right if you are doing things like changing data warehouses or moving your main data storage location, or simply consolidating data from different sources on-premise.

Open-source Tools: Open-source software can be easily recognized by the fact that it is designed for public access and therefore can be used, modified and shared. Furthermore, open-source solutions are usually free or offered at lower prices than the commercial ones. However, to work with open source, though, you may require some coding expertise. Commercial products are sometimes based on open source products and/ or provide a restricted open-source version for download.

Cloud-based Tools: Cloud-based data migration solutions are the most recent generation, and they are intended to migrate data to the cloud from an on-premise store, an application or stream, or another cloud-based store. Many businesses perceive cost savings and enhanced security in migrating data from on-premise to the cloud and require a SAP data migration solution to assist with this process. Cloud-based solutions are ideal if you already save your data on the cloud or plan to do so in the future. Furthermore, cloud-based data transfer technologies are quite adaptable regarding the sorts of data they can manage.

SAP Data Migration Best Practices

A solid SAP data migration best practices process plays an important role in the successful transition to a new SAP system like S/4HANA. This process comprises data quality assessment, extraction of data, data transformation and cleansing, loading data into the new system, and finally, validating the migrated data and testing it. A good plan that comprises unambiguous objectives, a fair timeline, and specified roles not only assists in minimizing the risks but also in upholding the data integrity.

Planning and Preparation

To ensure data migration from SAP is successful, then the elements of effective planning and preparation are the key ones. During this phase, companies are able to set very specific objectives, reduce the chance of big problems occurring during the project by spotting them at an early stage, and have all the people involved agree on the project’s scope, timeline and expectations. The migration process is carefully planned in such a way that it is less disruptive, less downtime of the system and more business continuity.

Define Clear Objectives

The migration goals should be expressed clearly – such as improving the quality of the data, reducing the costs or making the business processes more efficient.

Comprehensive Assessment

The current data landscape has to be assessed in a detailed manner so that migration accuracy is not affected by inconsistencies, redundancies, or missing information that has not been identified.

Develop a Detailed Plan

The first step entails designing a detailed plan with a structured roadmap that identifies key milestones, data sources, responsibilities, and risk mitigation strategies for a seamless transition.

Stakeholder Engagement

The early involvement of business and IT stakeholders ensures not only alignment and ownership but also effective communications throughout the migration journey.

Choose the Right Deployment Option

The best SAP deployment model, on-premise, cloud, or hybrid, needs to be picked according to one’s business goals, infrastructure, and scalability requirements.

Data Management

Proper and strong data management practices are the core requirement to ascertain that the new SAP system will contain nothing but the exact, uniform and pertinent information. During this phase, the data undergoes cleansing, mapping, validating, and archiving processes which are all focused at maintaining data integrity and compliance during migration.

Data Cleansing and Harmonization

Remove duplicates, fix mistakes and standardize data formats, which will result in having consistency and trustworthiness throughout all the business’s systems.

Data Mapping

Match the data fields in the source system with the structures in SAP so that the new environment receives the information correctly.

Data Archiving

To comply with regulations and secure access to historical data, it is advisable to store legacy or non-critical data securely and reduce system load accordingly.

Data Validation

Examine and confirm the accuracy, completeness, and compliance of the migrated data thus guaranteeing smooth operations and error-free reporting in the post-migration phase.

Testing and Validation

The migrated data to the new SAP system must be thoroughly tested and validated to guarantee it works as intended. This process reveals errors, validates the correctness of the functions, and provides confidence prior to the go-live. Testing with rigor protects the integrity of data and reduces the inconveniences related to post-migration.

Iterative Testing

One should carry out several rounds of testing to find and fix the problems right at the start, making sure that there is a continuous improvement through the migration period.

Functional and Regression Testing

To make sure that indeed all transferred data is advantageous to the business functions and that the new settings do not disturb the already existing procedures, ready-to-use verification has to be applied.

User Acceptance Testing (UAT)

Have end-users involved in the process of system validation by allowing them to test the system under real-world conditions. This will establish the system’s compliance with the business requirements and readiness for use.

Change Management

It prevented the employees’ transition to the new system and new processes would be full of hiccups. Organizations that provide training and keep the users informed will be able to diminish resistance, accelerate acceptance, and reap the migration’s full advantages.

User Training

Make training and resources available to staff in order to give them confidence in their ability to use the SAP system.

Communication

Gradually provide stakeholders with information about migration status, major changes, and advantages through open, regular communication.

Post-Migration

The post-migration phase takes care of your SAP system functioning smoothly after go-live and still giving value. This period is devoted to support, optimization, and performance monitoring so that data integrity is kept and system efficiency is maximized.

Support and Optimization

Provides continuous technical and functional support while the processes will be further refined. This way, the system

Monitor Performance

Track key metrics, system usage, and data quality to identify issues early and ensure the SAP environment meets business objectives.

SAP Data Migration Steps

Data migration to SAP S/4HANA can be broadly be divided into seven phases as follows:

Data Analysis

The data analysis phase can be started with the Prepare phase of the SAP Activate methodology since it requires you to put your processes in place. Data analysis also acts as a great stage to identify and analyze business objects, data migration objects, and source systems to make decisions that are critical for the migration journey. In the context of phase 1 of data analysis, it’s also important to differentiate between business and migration objects, along with master and transaction data.

• Business Objects: A business object is the necessary individual data object that takes part in the creation of a business process next to, for instance, material, customer, or purchase order.
• Migration Object: The migration object is either the business object or part of the business object. There might arise scenarios where a single business object has to be split into several migration objects because it has multiple data sources or different migration interfaces.

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.

Data Cleansing

Data cleansing is a great opportunity to 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.

Data Mapping

The mapping phase of data migration starts once the data migration objects have been identified. It primarily involves mapping the structures and fields of your source system to the target system. Since this process is done on paper, it is also referred to as paper mapping. Mapping essentially involves two different techniques

• Field Mapping: In field mapping, the efficient transfer of data is ensured by the proper matching of the source system fields with their respective fields in the target SAP system.
• Structure Mapping: The structure mapping is about aligning the hierarchical data structures of the source system with those of the target SAP system. Complex data relationships, such as tables and nested records, are represented accurately in the target environment.

Implementation

The implementation phase is mainly concerned with the production of data extraction and migration programs that will transfer the data from the source SAP or non-SAP systems to the specified 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.

After the bulk of the conversion or migration rules are set, trial loads are done for the first time to test how closely the extracted data conforms to the target system’s criteria. Such test loads are useful in revealing issues about data structure, format, or logic that may need adjustments.

By detecting and correcting these issues, the migration team can enhance the trustworthiness of the extraction and transformation processes, thus, establishing a firm basis for the later testing and validation phases.

Testing

As mentioned in the previous phase, it is best to start testing at the earliest. There is no substitute to testing. The more complex your data migration object and conversion rules are, the greater is the need for testing. Typically, two types of testing are conducted as follows:

• Functional Testing: The aim of functional testing is to verify that the migrated data is in accordance with business processes in the target SAP system. Purpose: The main aspect of this test is to check the integrity, accuracy and compliance of the data with the functional requirements.
• Load Testing: Assesses the performance and robustness of the SAP system in the target environment under normal data and operational loads. Purpose: This test ensures the system is capable of managing the maximum data size along with the expected user activities without any performance decline.

Validation

Validation is the phase where the accuracy, completeness, and consistency of the data transferred from source SAP or non-SAP systems to the target SAP S4/HANA system is verified. The greater the quality of master data and transaction data, the greater the probability of error-free operation in the new system. Machine-based solutions help to not only validate the date but also identify errors in the source data to aid the cleansing process.

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

• Pre-Migration Validation: Helps to determine whether the data in the source has been recorded correctly, totally and is in a state of being migrated. Data preparation and verification is followed to propel the migration process.
• Post-Migration Validation: 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.

Productive Load

Productive load 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.

Common Challenges of Data Migration

Data migration from an SAP or non-SAP source system to S/4Hana can be very complex and leads to a number of challenges.

Poor Legacy Data Knowledge

Thinking that the old data can be easily fit into the new system can be a risk, especially in the aspect of user acceptance. Typical problems such as duplication, missing data, misspelling and inaccuracies are often ignored and thus contribute to the unpreparedness.

Integration Issues

Data migration is characterized by the involvement of various teams and tools, including the use of spreadsheets for data definitions which are of high error probability and at the same time difficult to align with the transformation processes. Unaligned technologies can cause mistakes in the areas of design, testing and implementation which in turn may lead to prolonged timeframes and increased costs.

Lack of Backup Strategies

One of the reasons SAP data migration fails is the failure to plan for interruptions. Handle data migration as transferring valuable assets, identify potential failure points and set up contingency plans to safeguard non-critical data.