Transforming HR migration

A living process

Every modern Human Resource (HR) department knows that using digital technologies can help their HR team achieve new levels of speed and efficiency. But for many, running HR systems today means managing data across different systems, on-premise and in the cloud, which can be complex and demanding.

Data migration is a fact of life. But, too often, it is under threat from disparate systems and a lack of coordination between the various teams involved across the organization. And it can suffer from poor security strategies—when handling sensitive data it is vital that it maintains integrity and accuracy, especially since failing to meet data protection regulations can result in punitive fines.

As drivers of the human capital management function, HR IT executives need to embrace migration, validation and reconciliation as fundamental to their HR data strategy—and find new ways to make those functions both easy and automated. And when it comes to the core process of payroll, they need to be sure that the data is stable and reliable and any discrepancies or errors are swiftly identified.

Migration challenges

From the HR department to the IT team, the test team to project management or governing bodies, effective migration requires collaboration and clarity around communications and processes.

Tackling these challenges requires HR IT executives to ask themselves: What data fields and structures need to be transported into the new system? How will they be mapped? How many years of historical data, if any, should be migrated? What data needs to be archived for reporting and regulatory purposes?

Efficiency gains

Many HR teams don’t pay enough attention to the initial data extraction and conversion steps in any migration. HR teams must be proactive to de-risk conversion.

A typical migration involves standard processes: gathering the data from various systems, identifying the data, migrating the data, validating the data quality and testing in parallel (for payroll).

Add-on solutions can help to make these processes easier through automation, avoiding errors and improving outcomes. By managing data more flexibly from systems outside of the SAP system, HR teams can be clear on what is happening in the system and when. They can also gain clear reporting that provides up-to-the-minute insights to feed shareholders’ interests.

Three scenarios of migration

Here are three examples of migration scenarios and how they can be tackled:

Using our SAP SuccessFactors migration suite of products, it’s possible to not only anonymize data securely and flexibly between different SAP systems to optimize quality, protection and efficiency, but also copy existing customization—and data—related to payroll to the new system. Once the copy and configuration is complete, data validation can be undertaken using Accenture Data Comparison Manager to be sure that the data has migrated properly. Further, we recommend parallel payroll tests to compare what the old system calculated on payroll and what is on the new system, to check they are aligned.

Although it is important that data migration does not leave vital information behind, it is also critical not to copy errors or out-of-date data into the new system. Taking a closer look before the migration using the analyzer functionality of Accenture Data Comparison Manager, our SAP SuccessFactors extension, can support security and traceability in migration. Focusing on cleansing data in the system before migration begins can save time post-migration. We also recommend Accenture HR Audit and Compliance as-a-service which automatically checks data quality. Based on the results, Accenture Data Comparison Manager can then be used to handle the conversion rules, field mapping, and transformation required to load the right data. For instance, SAP was able to simplify data migration and reconciliation, reducing the time needed to run full validation cycles from more than three weeks to as quickly as one week.

Gaining insight and control over data is eased by maintaining data at a consistently high quality in the system. We recommend adopting a strategic approach to de-risk operations and strengthen data integration. By applying regular quality checks and taking advantage of data integration capabilities, HR teams can ease replication and be sure that their payroll is working effectively and efficiently.

Right data, right place, right time

HR leaders need to make sure they can move data from one system to another successfully—and trust that the data is both valid and fully reconciled.

If you are planning, just starting or currently undergoing a migration to SAP SuccessFactors, ask yourself:

• Do we have an up-to-date validation strategy behind our migration efforts?
• Post go-live, do we have an “insurance policy” for our interfaces?
• Did we undertake appropriate data cleansing work before and/or during data conversion?
• Have we allowed ourselves enough time between migration cycles to consolidate, review and correct the outcome of the previous cycle?
• Have we included milestone checkpoints in our migration approach?
• Have we considered the need for payroll regression testing as part of the migration?

When tech meets human ingenuity

Backed by deep skills in talent and organization management, Accenture helps businesses to unlock the power of human potential and access a new level of workforce transformation. What is more, Accenture Software for Human Capital Management serves more than 2,000 customers globally and has been building tools in the migration space for the last 20 years.

Our longstanding experience tailoring solutions specifically for SAP Human Capital Management legacy systems and SAP SuccessFactors has produced extensions that enhance and augment functionality. Our mature products, showcased by a decade of success stories, are available as certified apps in the SAP Store and can help diverse customers, large and small, with the validation of raw extracted data, loaded data and conversion data, data integrity checks and post-mock data cleansing, scrambling and test data creation.