Understanding SAP S/4HANA Implementation Risks with RSM – Part Five: Data Determines Everything
Key Takeaways
⇨ High data quality is critical for a successful migration to SAP S/4HANA; poor quality can lead to errors and inefficiencies that undermine the new platform's value.
⇨ A well-planned data classification and cleansing process can mitigate common data issues, ensuring data is accurate, consistent, and complete before migration.
⇨ Investing in data governance prior to migration reduces risks and enhances performance, enabling organizations to achieve reliable insights and streamlined business processes.
As organizations embark on their journey to SAP S/4HANA, they will often look to external factors like implementation partners and technology solutions and believe that they have everything they need to be successful. Yet one of the most crucial factors for a successful digital migration is stored within – data.
Ensuring high data quality is essential for a successful migration to SAP S/4HANA. Poor data quality can lead to system errors, inaccurate reporting, and operational inefficiencies, undermining the value of the new platform. Since SAP S/4HANA relies on a simplified data model and real-time processing, clean and structured data is necessary to maximize its benefits.
Ensuring Data Quality
For organizations in the process of moving to SAP S/4HANA, the digital transformation professionals at RSM recommend a well-planned data classification process along with appropriate cleansing, mapping, and migration processes.
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This can help reduce the risk of some of the most common data issues:
- Improper classification of sensitive data
- A lack of accurate data owner identification
- Improperly migrated data
- Unvalidated data
- Uncleansed data
- Inaccurate data migration tests
Data issues can be one of the top stumbling blocks that companies face in migrations to SAP S/4HANA.
“Most often, the delays in implementation we see are due to data. We see multiple approaches, sometimes the SI would take a smaller portion of data through their automated tools do 100% validation. In some cases, they might just say we’ll do a manual sample-based exercise because it’s going from a legacy application. That’s where it kind of leads to a disconnect between preload and post load,” said Shreyas Devaraju Reddy, a Director for GRC and Controls at RSM.
Migrating data from legacy systems to SAP S/4HANA requires data to be accurate, consistent, and complete. Poor data quality—such as duplicate records, missing values, and incorrect formatting—can result in migration failures or data corruption. Organizations should cleanse and validate data before migration. That way, they can ensure that the new system reflects a true and complete state of business operations.
Benefits of Cleansed Data
SAP S/4HANA processes data in real-time using an in-memory database, providing faster insights and analytics. If the underlying data is inconsistent or inaccurate, the insights generated will be unreliable, leading to poor business decisions. Clean data improves the accuracy of financial reports, inventory levels, and customer information, supporting better decision-making.
SAP S/4HANA’s streamlined business processes rely on consistent and standardized data. Poor data quality can cause process failures, bottlenecks, and increased manual corrections. High-quality data ensures seamless execution of automated workflows and minimizes exceptions.
“One of the frustrations amongst a lot of program teams is how much time gets, how much time they have to incur trying to sift through defects that are not really defects. But it turns out, after investigation that they’re really driven by bad data. The solution’s working exactly the way they it’s supposed to work, but they’re not seeing the test step outcomes that they expect to see because of bad data,” said Joe Corro, Director at RSM.
What This Means for SAPinsiders
Data is the bedrock. Clean, consistent, and accurate data is the foundation for a successful SAP S/4HANA implementation. Investing in data cleansing, governance, and validation before migration reduces risks, enhances system performance, and ensures the platform delivers accurate insights and efficient business processes.
Understanding which shortcomings are data-related can save significant time and cost. Rectifying data issues can help companies understand which processes are not working in an implementation. Some workflows are failing due only to the low-quality data they are using. Companies can avoid these issues by working with partners like RSM to ensure that they have usable data going into a digital transformation.
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