Synthetic data gets a lot of attention, especially in regulated environments. It’s clean. It’s safe. It avoids compliance hurdles. And for many QA teams, it seems like the simplest way to scale test coverage without touching production. 

But in SAP environments, simplicity comes at a cost. 

Synthetic Data Isn’t the Problem. Disconnected Data Is. 

Synthetic data isn’t inherently bad. It’s useful in the right context like when applied to low-risk scenarios, early-stage automation, or simple validations. But it breaks down fast when used as the default strategy especially in SAP testing, where data isn’t just a placeholder. It’s a critical part of the process logic. 

SAP isn’t a transactional system. It is governed by tightly coupled business rules, cross-functional dependencies, and deeply embedded business logic. Every transaction depends not just on inputs, but on relationships between master data, config tables, and real-world business context. In that environment, synthetic data often fails, not because it’s incorrect, but because it’s incomplete. 

Where Synthetic Strategies Break in SAP 

We’ve seen tests pass when they shouldn’t, break for no clear reason, or produce inconsistent results that erode trust in the automation itself. In many cases, these failures aren’t due to the automation logic. They’re symptoms of test data that doesn’t align with how SAP works. 

That’s why a synthetic-only strategy creates risk. Not because the data is artificial, but because it’s disconnected from reality. 

Automation programs stall when they rely too heavily on synthetic records. The data doesn’t trigger real business rules. It lacks the depth needed for regression testing. And it can’t replicate the full complexity of a live environment, especially when testing cross-module flows like order-to-cash, procure-to-pay, or hire-to-retire. 

The Smarter Path: Context-Aware Test Data Strategies 
 
In SAP testing, synthetic data can be helpful, but only in limited, low-risk scenarios. Unlike in other enterprise systems, SAP’s tightly coupled configuration, master data rules, and cross-functional dependencies make synthetic test data difficult to generate accurately – and risky to rely on at scale. 

But once testing moves into cross-system flows, regression validation, or high-risk areas like financial postings or pricing logic, synthetic data often falls short. It lacks the depth and system context required to reflect real-world behavior. 

That’s where Worksoft and EPI-USE Labs offer a smarter alternative. 

Worksoft provides intelligent, codeless automation designed for complex, end-to-end SAP processes. Paired with Worksoft Data Connect powered by EPI-USE Labs, teams gain secure, masked, production-derived data delivered on demand and aligned to the test process. 

Together, this enables hybrid test data strategies that are: 

• Context-aware: Tailored to the process under test 
• Compliant: Masked to meet regulatory standards 
• Reliable: Provisioned with the structure SAP actually expects 

A Strategic Approach to SAP Test Data 

In our whitepaper, Smarter SAP Testing Starts With Smarter Data, we outline how successful testing teams choose the right data strategy based on: 

• Risk level 
• Process complexity 
• Compliance sensitivity 

The takeaway? There’s no universal approach. But in SAP, realistic, production-aligned test data must be the foundation.Designing test data with intention, just like test automation, is the key to faster, safer, more trusted validation at every phase of your SAP journey. 

Ready to take the next step? 

Choose how you’d like to move forward:  

• Speak to a consultant about improving test reliability with data-aware automation  
• Download the eBook: Smarter SAP Testing Starts With Smarter Data  
• Visit our website to explore how Worksoft helps QA teams deliver automation that works