Services
Data and Analytics
Smart Data for AI: Building Agile, AI-ready Ecosystems
Highlights
- When compliance mandates prevent testing environments from accessing production data, incomplete testing is the inevitable result and a major bottleneck in SAP transformation.
- This isn’t merely a technical constraint. It translates directly into downstream defects, costly remediation, and delayed transformation outcomes.
- Synthetic data offers enterprises a compliant, secure, and scalable alternative that enables complete SAP workflow validation under real enterprise conditions.
In this article
In this articleinpage
The data challenge
For enterprises undergoing SAP transformation, restricted access to production data is a risk that rarely gets the attention it deserves. Data privacy, security, and compliance mandates routinely prevent testing environments from accessing the production-grade datasets they need. When the access is blocked, testing is the first casualty, and teams are forced to work with incomplete data, or none at all.
The consequences are significant. Incomplete test data leaves end-to-end business processes unvalidated before go-live, which compounds the risk of downstream defects and costly remediation.
This challenge was precisely what surfaced in TCS’s recent engagement with an aerospace industry giant, where the absence of approved test data created a critical gap in validating key SAP workflows such as procure-to-pay (P2P) and procure-to-stock (P2S).
TCS addressed this challenge by generating production-like synthetic data that eliminated dependency on sensitive production datasets. Even when faced with evolving requirements, inconsistent seed data, and a restricted deployment model, TCS-generated synthetic data consistently delivered high-grade data with full referential integrity that enabled complete end-to-end SAP workflow validation.
For enterprises where production data is off-limits, that means transformation programmes no longer have to choose between testing thoroughness and compliance. They can have both.
Synthetic data
Synthetic data gives enterprises a compliant foundation for SAP testing without exposing sensitive information.
What synthetic data delivers |
Business impact |
| Production-grade data generation | Testing proceeds without waiting for production data access |
| SAP-specific referential integrity preservation | Data relationships across interdependent tables remain valid and testable |
| Zero dependency on production or masked datasets | Compliance and security standards maintained throughout |
| End-to-end business process coverage | P2P and P2S workflows validated, with no gaps in coverage |
During the engagement with the aerospace industry major, TCS generated an average of 15,000 records per transactional table across 100 SAP tables, more than three times the initial target. The following section sets out exactly how that was achieved and what the experience revealed about deploying synthetic data in a complex SAP environment.
Case study
Client: A major aerospace enterprise.
Engagement duration: 14 weeks.
Challenge: The client lacked approved test data to validate their SAP business workflows. Production data could not be used due to data privacy and compliance constraints. Without an alternative, critical processes, including P2P and P2S could not be tested.
Resolution: Secure, realistic, and compliant synthetic data was deployed at enterprise scale.
Initially, multiple datasets were generated and delivered in a tabular format. Based on client feedback, select tables were regenerated using a client-provided single-sheet template, with multiple tables consolidated to streamline data ingestion into their systems. This standardised template approach was then applied to subsequent datasets.
Value delivered:
- 100 SAP tables completed on time, within a 14-week pilot
- 15,000 records generated on average per transactional table, three times the target of 4,000 to 5,000
- High-quality, privacy-preserving datasets generated with zero exposure to sensitive production data
- Referential integrity maintained across all datasets
- Full end-to-end validation of P2P and P2S workflows achieved
- Zero dependency on production data
- Client-provided templates were adopted for regenerated and upcoming table outputs, simplifying data upload
SAP complexity
SAP is not a forgiving environment. Its referential integrity model is fundamentally different from standard relational databases, and any synthetic data strategy that underestimates this complexity will struggle to deliver results that hold up under real testing conditions.
In this engagement, that complexity was compounded by practical realities on the ground:
- Business logic was still evolving as data generation was underway
- Seed data arrived inconsistently and changed frequently
- Validation feedback sometimes contradicted earlier requirements
- A single consultant was responsible for end-to-end execution
None of this is unusual in large enterprise programmes. What it does underscore is that synthetic data generation in SAP is not purely a technical exercise.
Three things must be in place before synthetic data is deployed:
- SAP functional experts must be involved from the outset, not brought in after problems emerge.
- Business logic and seed data must be agreed, documented, and frozen before generation begins.
- A governance model must be established to keep requirements stable and feedback consistent across the life of the engagement.
Why It matters
A 14-week pilot, one consultant, a restricted deployment model, and requirements that kept moving— the conditions were not ideal. Yet the engagement exceeded every target.
The value of this engagement is not that TCS-generated synthetic data performed well under controlled circumstances. It is that it performed well under genuinely difficult ones. SAP transformation programmes are rarely clean. Requirements evolve, resources are stretched, and environments are complex. Synthetic data that only delivers when everything is perfectly aligned is not one that enterprise programmes can rely on. Enterprises no longer need to choose between moving forward with inadequate test data and waiting indefinitely for production access that may never come.
A proven method
The case for synthetic data in SAP environments is no longer theoretical. This engagement demonstrated that it is possible to generate high-volume, referentially sound, high-quality, and privacy-preserving synthetic data without touching production data, even when conditions are far from perfect.
For chief data officers (CDOs) navigating SAP transformation, programmes that have historically stalled or accepted reduced test coverage because production data was off-limits now have a proven, compliant path forward.
The question is not whether synthetic data can work in your SAP environment. It is how much longer your programme can afford to go without it.
Synthetic data is a compliant, risk-free alternative to production data. With high quality, production-like synthetic datasets, organisations can accelerate validation of critical workflows at scale.