This is part of an ongoing blog series on SAP SuccessFactors HCM implementation. In this post, we are going to talk about the key areas of the Data Migration workstream. Data migration is one of the most complex workstreams in the SAP SuccessFactors HCM Implementation project. It is often the primary factor that determines whether an implementation is a resounding success or a mess.
I have covered all critical areas at a high level but will write a detailed blog on the critical section with all details.
1) Data Migration Scope : When initiating the Data Migration workstream, the highest priority is to finalize the Data Migration Scope. This is a critical foundational step, as the defined scope directly influences migration complexity, effort, resource requirements, sequencing, and the overall project timeline.
Key areas that play a critical role in scope definition include:
- Existing system architecture and source systems for data
- Modules and business processes in scope
- Data sources and data ownership
- Historical data retention and migration requirements
- Compliance, audit, and regulatory considerations
- Reporting and analytics requirements
- Data quality and data volume considerations
- Integrations and downstream dependencies
- Security and access requirements
2) Data Migration Tools :Identifying the right migration tool is a critical success factor for the Data Migration workstream. The selection of the migration approach and tooling largely depends on the source systems, data structure, transformation requirements, data volume, and overall integration landscape.
Based on the Customer’s existing systems and migration requirements, the project team must evaluate and select the most appropriate migration tools and frameworks. The chosen toolset should support efficient extraction, transformation, validation, and loading of data while ensuring data quality, traceability, and compliance requirements are met.
SAP provides Info Porter Tools to migrate Foundation, Org and Employee data from the below systems to SF EC. This tool works really great for data migration loads.
- SAP S/4HANA HCM
- SAP ERP HCM
3) Data Migration Team – SI Skillset and Customer Resources availability: Data Migration requires strong commitment and dedicated participation from both the Customer and the System Integrator (SI). Data migration activities, such as data mapping, data validation, data cleansing, reconciliation, and testing, are highly business-driven and require deep knowledge of the Customer’s existing systems, data, and processes.
At the same time, it is equally important to ensure the SI provides experienced Data Migration consultants who can offer the right guidance, proven methodologies, and strong delivery ownership.
4) Data Migration Requirements and Mapping workbook ownership: Capturing Data Migration requirements is one of the first and most critical activities during the Realization phase. A well-defined migration requirement and mapping framework establishes the foundation for successful data transformation, validation, and loading activities throughout the project lifecycle.
Defining migration requirements requires close coordination and collaboration among multiple stakeholders, including:
- SuccessFactors (SF) Functional Resources
- Data Migration (DM) Team
Since migration requirements evolve throughout the project, it is important to clearly define ownership, governance, and update processes for the Data Migration Mapping Workbook from the beginning.
5) Data Migration Build and Unit Testing: Identifying the right team for the Data Migration workstream is critical to the success of the overall implementation. The Data Migration team must have a strong understanding of the SuccessFactors (SF) Data Model, object relationships, data dependencies, and migration sequencing requirements.
At a minimum, key resources assigned to the project should have prior experience in SuccessFactors Data Migration and hands-on knowledge of:
- SuccessFactors Employee Central data structures
- Foundation Objects and MDF Objects
- Effective-dated data handling
- Load sequencing and dependency management
- SAP migration tools and templates
- Data transformation and validation techniques
6) Mock Cycles Planning: Because Data Migration is one of the most complex and risk-prone workstreams, ne of the key success factors for migration readiness is defining the appropriate number of mock migration cycles before the Production load. More mocks generally provide higher-quality data, but they also increase project timelines. The required number depends on customer complexity, global footprint, and the project deployment approach. I strongly recommend 3 mock cycles with a full load prior to the production load
The number of mock cycles should be determined based on several factors, including:
- Customer complexity
- Global footprint and localization requirements
- Number of countries/business units in scope
- Volume of historical data
- Data quality maturity of legacy systems
- Complexity of integrations and downstream systems
- Deployment and rollout approach
- Compliance and reporting requirements
7) Instance Strategy: A dedicated SuccessFactors (SF) instance is required to migrate data, and the SF instance cannot be used for any other purpose during data loads. At the same time, ensuring that the source system (e.g., SAP S/4HANA) is available and that production data has been copied is critical. This must be properly planned as part of the instance strategy for each mock cycle, including which systems will be used. If necessary, an additional SF instance should be provided.
8)Entry and exit criteria for each mock: For each mock load, we must clearly define the entry criteria required to initiate the exercise, as well as the exit criteria that determine successful completion. As part of the data migration workstream, each activity should also be assigned defined entry criteria and timelines. Without these clearly established controls, there is a high risk of delays and misalignment across dependent activities.
9)Data Scrambling: Protecting sensitive data and coming up with the right data scrambling approach is key. We typically do not migrate real email addresses, national IDs, compensation details, or payment details during Mock loads. Depending on the usage of mock load data, we may include or exclude sensitive data.
If sensitive data is not required during mock loads, we scramble data in the source system or in SuccessFactors before granting user’s access. Partner tools are available to support data scrambling, and some customers also have built-in tools to do so.
10) Data Validation Tools and Approach: Data validation requires significant effort. Identifying the right tools—both internal and external—that enable business teams to continuously validate data can greatly accelerate processes and reduce risk. In addition, establishing a dedicated data validation team during mock load or testing phases can significantly improve data quality and further minimize risks before production deployment.
11) Data Cleansing Approach: Data cleansing effort during migration depends primarily on two factors: the quality of the source system data and the number of years of history included in the migration scope. In top-of-stack migrations, effort is typically lower. However, when defining the migration scope, it is critical to recognize that expanding historical data coverage significantly increases cleansing complexity due to accumulated inconsistencies, missing standards, and legacy data issues.
12) Data Archival Strategy: Once you are live with SAP SuccessFactors HCM, we do not bring over all historical data. However, we still need historical data to support different requirements such as reporting, regulatory requirements, rehire processes, and tenure calculations..etc. The right data archival strategy must be defined based on the legacy HR system’s role after SuccessFactors goes live. Some partner tools provide seamless access to historical data from the SuccessFactors system.
13) System freeze strategy during Prod Loads: A proper strategy should be defined during production data loads, including how source system transactions will be governed during production loads. Depending on production load, timelines, and business impact, we carefully plan soft and hard freeze periods. We also define a delta migration approach to minimize disruption.
The InfoPorter tool supports a delta approach when migrating data from SAP S/4HANA HCM or SAP ERP HCM. If you are migrating data from other HCM systems, you should define the appropriate strategy based on system capabilities and project requirements
14) Production Load : Define production load timelines. We usually plan around three weeks to complete production load activities, including data load, validation, and sign-off. However, the timeline may vary depending on complexity. The goal is to achieve a 99.99% validation success rate, which serves as the exit criterion for production load completion.
Below is the High-Level SAP SuccessFactors Data Migration Framework cover all key areas in one picture :
