The migration process to SAP S/4HANA presents a significant data management challenge, necessitating careful consideration of data selection, migration methods, and system reconfiguration.
Undertaking an S/4HANA migration can be an intimidating endeavor for customers, particularly when it comes to transferring data from the previous system to the new platform. The successful execution of this data transfer is of utmost importance, as migrating outdated, inaccurate, or redundant data into the S/4HANA system can pose substantial risks to the overall implementation.
The transition from legacy systems to S/4HANA hinges on the establishment of what is referred to as a “clean core” – a streamlined and simplified system that minimizes the need for extensive customizations. This approach lays the foundation for the integration of S/4HANA’s promised innovations, such as enhanced capabilities for artificial intelligence and analytics. Given that SAP environments typically encompass a vast array of data in various formats, customers are likely to encounter challenges during the data migration process.
The data challenges associated with migrating to S/4HANA is adaptation of data to fit the new SAP’s clean core. SAP is advocating for a shift towards standardized practices, which involves adopting a clean core digital ERP system – S/4HANA – and leveraging the SAP Business Technology Platform (BTP) to interact with additional cloud services. This shift necessitates adjustments in data storage, utilization, and management.
Defining the Concept of a Clean Core
SAP contends that many customers are impeded by an extensive portfolio of customized code they have developed over time. Many companies have decades of old legacy systems with substantial customizations leading to unmanageability. To achieve SAP’s long-term objective of transitioning users to the public cloud, eliminating such custom code becomes imperative. Customers are encouraged to adopt a simpler S/4HANA system by discarding custom code. This involves either reverting to standard processes or replacing customized processes with external solutions that exist beyond the SAP environment. The eventual aim is to simplify operations, making the transition to the public cloud more seamless. Once on the public cloud, customers can access new capabilities on a regular basis.
Complex Factors in Simplifying the System
The adoption of a simplified system not only results in reduced data within the SAP environment but also demands the management of data across different contexts. Currently, a significant portion of a customer’s processes may run on one system. However, simplifying the core architecture introduces multiple operational centers. The central hub interfaces with various peripheral applications, accessed either through the BTP or via cloud connections. This shift necessitates a departure from the current data organization and management strategy, presenting a general challenge in data management as customers embrace a more standardized and streamlined core in conjunction with the BTP
The Complexity of a Typical SAP Data Environment
For customers exclusively utilizing SAP financials, their data landscape is relatively well-defined, encompassing elements such as customers, vendors, payables, receivables, balances, and financial metrics. In contrast, large global manufacturing enterprises with numerous subsidiaries present a more intricate scenario. These entities may have undergone sales or closures, resulting in a complex data landscape. Migrating such data to S/4HANA requires careful decisions regarding what data to retain, discard, activate, or deactivate.
In scenarios where multiple legacy systems are consolidated into one, a data quality challenge arises when comparing master data across systems. Duplication of data, such as a single material being recorded across multiple systems, necessitates meticulous selection and transformation processes. Addressing such complexities represents a substantial Data Harmonization endeavor.
In conclusion, the migration to SAP S/4HANA involves multifaceted data challenges. Successfully navigating these challenges requires a holistic approach, encompassing careful data selection, meticulous migration strategies, and the alignment of data with the clean core model.