Data Migration into C/4HANA

Publish date:

Data is the heart of having a successful C/4HANA implementation and the right data is critical.

Migrating data could be either moving data from an existing system to C/4HANA, upgrading your solution or just trying to synchronise your environments.  Regardless of what type of these scenario, you need to understand the requirements for your client data and what needs to be imported/exported from your C/4HANA solution.

Data requirements:

Upgrade from existing system

From a requirements perspective, data migration for upgrades is complicated because you are working with an existing system which has evolved over the time and is quite often not well documented. It produces the following challenges.

Data cleanliness:

The data from the legacy system may not be “clean”. This could include incorrect or incompatible data. Your requirements should define where the data is coming from, when it should be imported and where it will be cleaned (source or in-transit).

A strategy should be defined up front so that it can be planned for and considered in the effort and timeline. Also, the strategies should include an effort to clean the data before the data transfer.

Incomplete or inaccurate data:

It can be good to set a target for completeness of the data transfer when dealing with large amounts of records. For example, when transferring millions of transaction records, whether you need to transfer all completed/closed/rejected records or only the in-flight/in-process records.

Master records:

Before deciding what data to migrate, you should first identify which data you will need in your C/4HANA solution. To get an idea of the data, you can work with the architect to review the data model of your solution.

Once you’ve established the data model and data objects, you should determine where the master record (also known as “source of truth”) of the data is. Will your C/4HANA contain the master record that all other integrations will read from or is the master record contained elsewhere and your C/4HANA instance will have to call for the data or replicate it? If another system contains the master record, typically a data migration for that data is not required.

Once you’ve established where the master record for your data is, you’ll need to define migration requirements of what data record needs to be migrated. 

Data migration steps:

  • The extraction of the data from the legacy system
  • The transformation of the extracted data to the data format of the new system
  • Loading the transformed data into the target system

Data migration options:

Manual migration:

  • Low data volumes
  • Legacy data is unstructured
  • Migration template for a specific object is not available in C/4HANA

Tool supported or initial load (via. integration) migration:

  • High data volumes
  • Time available to load the data is restricted. For example, only weekend is available to load the data
  • Performing multiple data loads in different systems. For example:
    • Test loads in the test system and final load in the production system
    • Replicating employee / customer from SAP ECC to C/4HANA systems.

Migration process:

Schedule: create a precise project plan for all tasks regarding the data load.

  • Select the objects relevant for data load
  • Define milestones and sequence for the load of each data object into a test tenant
  • Define milestones and sequence for the load of each data object into the production

Cleanse: cleanse the source data that will be loaded into C/4HANA.

  • Deleting duplicate records
  • Removing obsolete records and any associated transactions
  • Check data quality and correct if required
  • Validate address & contact data and enrich if required

Extract source data: export the data from the source system(s).

  • Review the format required for entering the data into the C/4HANA migration templates
  • Extract the data using a report or database query into a .csv, or another flat file format

Populate migration template: copy the data into the migration templates.

  • Download required migration templates directly from C/4HANA system
  • Copy extracted data into the templates

Test Load: perform test loads of source data, a successful test load ensures a safe and smooth cutover.

  • Load data into C/4HANA test tenant
  • Start with small sets of data and verify the replicated data

Verify: verify the loaded data in C/4HANA test tenant.

  • Confirm the number of loaded records is correct
  • Perform spot checks of loaded records
  • Run end-to-end business scenarios within C/4HANA test tenant using the loaded records

Cutover:  perform final load of source data into the productive C/4HANA.

  • Load data into the C/4HANA production tenant
  • Start with small sets of data and verify the replicated data
  • Gradually increase the set size (3, 10, 50, 100, etc.) – up to max. 50000 records
  • Resolve errors (if any) before loading next set of data
  • Verify that all data was imported successfully

That’s a brief overview of the data migration fundamentals in C/4HANA.  In my next blog, I’ll detail how the data will be migrated into the C/4HANA and the best practices when performing a data migration.



Andrew Alexander

Andrew is a Senior Application consultant in the SAP Customer Engagement Team, UK. Andrew has over 8 years of consultancy and extensive SAP implementation experience from blueprinting to go live and support. His current engagement as a lead migration consultant, implementing solution using the C/4HANA Service Suite.


Related Posts


SAP Collaborative Enterprise Planning in a Nutshell

Date icon January 17, 2022

In this blog, Luis Lopez explains what SAP’s Collaborative Enterprise is and how SAP...

Artificial Intelligence (AI)

Predictions 2022: Intelligent industry

Date icon January 14, 2022

Our forecasts of what we believe will shape the intelligent industry sector in 2022

Artificial Intelligence (AI)

Effective data management: Data tiering, archiving and purging with SAP BW

Date icon January 11, 2022

In this blog you’ll learn about effective data management; how techniques such as data...