Synthetic Data in SAP Testing and Model Training

SAP projects rely heavily on data for testing and training purposes. Historically, consultants have had two main options: copying production data or manually creating test datasets.

Both approaches come with drawbacks. Production data often includes sensitive information, making its use complicated from a compliance standpoint. Even with data masking, the risk of exposing personal or business-critical details remains. Manually generating test data, on the other hand, is time-consuming and rarely captures the full complexity of real-world transactions.

Now synthetic data is providing alternatives. Synthetic data is generated by algorithms that mimic the patterns and relationships found in real data. The goal is to produce datasets that behave like authentic business information without containing any confidential details.

For SAP consultants, this presents a major shift in how testing environments are populated and how AI-driven models are trained. Instead of waiting for production data to be cleansed, extracted, or masked, teams can generate data on demand, structuring it in ways that meet specific project requirements.

This article from IgniteSAP explores the scenarios where it is most useful, how it compares to traditional data handling methods, and how can consultants build expertise in this area.

SAP System Testing and Implementation

One of the biggest challenges in SAP implementations is making sure that new configurations, custom developments, and integrations function correctly before they go live. This requires careful testing with data that reflects real-world business processes.

But the old approach is now increasingly difficult to justify. Regulations such as GDPR and industry-specific compliance requirements make handling real data for testing a legal and logistical headache. Also, manually creating test cases, while useful for controlled scenarios, often fails to cover the complexity of actual business operations.

Synthetic data offers a way forward by enabling teams to generate test data that closely resembles real-world transactions without relying on actual customer, supplier, or employee information.

This has several practical applications:

First, in functional and regression testing, synthetic data can be designed to include both standard and edge-case scenarios. In an SAP S/4HANA implementation, for example, consultants might need to validate how financial postings behave under various tax codes, currency conversions, or document flows. A synthetic dataset can be created to simulate a wide variety of transaction types, including cases that haven't occurred in real business history but could emerge in the future.

Second, in integration testing. A single business transaction, like a purchase order, can trigger updates across multiple modules, from materials management (MM) to finance (FI) and controlling (CO). Ensuring that these integrations work properly requires test data that maintains referential integrity across systems. Generating consistent datasets manually is difficult, but synthetic data tools can automate the process, producing related records that follow SAP’s data structures and dependencies.

Another key application is performance and load testing. SAP systems handle vast amounts of transactional data, and it’s crucial to test how they behave under peak loads. Using synthetic data allows performance testers to create massive datasets without burdening production systems or violating privacy rules. Synthetic data can be generated in whatever volume is needed and tailored to test specific stress points.

Synthetic data is also useful in data migration projects. When moving from legacy SAP ECC systems to SAP S/4HANA, businesses often perform multiple trial migrations to fine-tune their data transformation and validation processes. Since migration tools like SAP Integration Suite require well-structured input, synthetic data can be used to simulate different types of source records, helping identify potential migration issues before real business data is involved.

Use of synthetic data offers the flexibility to generate exactly the data needed, and also removes the risks associated with using production data, eliminating concerns around compliance and confidentiality.

One of the strongest arguments for continuing to use production data is realism. There is no doubt that real data, by definition, reflects how the business actually operates. But synthetic data generation has advanced significantly in recent years, with AI-driven tools capable of replicating statistical distributions and business rules with impressive accuracy. Many synthetic data platforms can generate records that not only look realistic but also follow the same dependencies and relationships as real SAP transactions.

For most SAP projects, the best practice is to combine real and synthetic data.

A hybrid model, where a small subset of anonymized production data is supplemented with synthetic records to fill in gaps, balances realism with flexibility, allowing teams to test a broader range of scenarios while still benefiting from the efficiency and safety of synthetic data.

Synthetic Data in AI Model Training for SAP

Machine learning is becoming an essential part of modern SAP environments, with organizations relying on AI-driven insights to improve financial forecasting, workforce planning, fraud detection, and supply chain management.

Training AI models to perform these tasks effectively requires large, high-quality datasets. In many cases, the data available is either too limited, outdated, or too sensitive to use freely.

In AI development, a model is only as good as the data it learns from. If training data is incomplete or biased, the resulting predictions will be unreliable. But finding sufficient real-world data to cover all relevant scenarios can be challenging. For example, an SAP-based fraud detection system might need to identify rare patterns of suspicious transactions. If an organization has only a handful of fraud cases in its historical data, the AI model may not be exposed to enough examples to learn how to detect them reliably. Synthetic data can help bridge this gap by generating thousands of additional, realistic examples of fraudulent transactions, helping the AI recognize subtle patterns it might otherwise miss.

SAP consultants working on predictive maintenance solutions in industries such as manufacturing or utilities often struggle to find enough failure-event data to train their models. Most equipment operates without issues for long periods, so actual breakdowns may be too rare to provide a statistically meaningful dataset. Instead of waiting years to collect enough examples, engineers can generate synthetic failure events based on historical trends, creating a dataset that allows the AI to anticipate problems before they occur.

Another major advantage of synthetic data in AI training is that it allows for scenario modeling.?

Businesses increasingly want AI models that can anticipate future conditions, not just react to past events. Suppose an organization is rolling out a new pricing strategy in SAP Sales Cloud and wants to predict its impact on customer behavior. Because the new pricing structure has never been used before, there is no historical data to train a model. Instead of making assumptions based only on past transactions, consultants can generate synthetic datasets that reflect different pricing scenarios, allowing the AI to explore possible outcomes.

Despite its benefits, synthetic data does require careful implementation in AI projects. Poorly designed synthetic datasets can introduce biases or unrealistic distributions, leading to inaccurate models. To minimize these risks, SAP consultants need extensive expertise, combined with strong data validation processes, to ensure that synthetic data remains a useful tool rather than a source of misleading patterns.

How Synthetic Data is Generated for SAP Projects

So how is synthetic data actually created?

One common method is rule-based generation, where datasets are created by applying predefined business logic. For example, a consultant working on SAP S/4HANA Finance might define rules to generate general ledger postings that follow specific accounting principles. This method is useful when the required data follows well-understood patterns, such as sales orders with typical discount structures or purchase requisitions that match procurement policies.

A more advanced approach involves AI-driven data synthesis, where dedicated machine learning models analyze existing data and generate new records that statistically resemble the original dataset.

This is useful for creating complex datasets with realistic distributions and dependencies, such as customer purchase histories or supplier payment behaviors.

AI-generated synthetic data is often produced using Generative Adversarial Networks (GANs) or similar techniques, which learn from real data samples and then generate new data points that follow the same statistical patterns.?

Some enterprise AI platforms, such as Hazy or Mostly AI, specialize in producing synthetic data for business applications, including SAP environments.

Supporting Tools

SAP AI Core supports the use of synthetic data by allowing the integration of external data generation tools and datasets into its workflows. This enables developers to incorporate synthetic data for training and testing purposes. Developers can also utilize external synthetic data generation tools in conjunction with SAP AI Foundation to enhance their AI models' training and testing processes.

SAP Test Data Migration Server (TDMS) is designed for creating and managing test data during migrations. SAP TDMS enables the extraction of relevant data subsets from production systems to create non-production environments, facilitating effective testing and training without exposing sensitive information.

SAP TDMS is not a synthetic data generator but can provide a secure foundation for testing environments. If using AI/ML models, TDMS can provide anonymized real data samples for training, but truly synthetic datasets need to be generated externally. Combining SAP AI Core and TDMS is a potential strategy for companies integrating SAP solutions with ML/AI initiatives.

Data integration tools play a key role in integrating synthetic data into SAP landscapes. Many synthetic data platforms offer connectors that allow data to be fed directly into SAP databases, whether via API, OData services, or traditional batch imports. SAP Datasphere integrates data from SAP and non-SAP sources, making it easier to combine real and synthetic data for testing or training AI models.

This means synthetic data can be created outside the SAP system and then loaded into test environments without requiring manual entry. Some organizations even automate the process, generating fresh synthetic datasets at regular intervals to support continuous testing or model retraining.

Developing Expertise in Synthetic Data for SAP

Given the increasing relevance of synthetic data in SAP projects, consultants who develop expertise in this area will be well-positioned to take on more strategic roles. So, what does it take to build skills in synthetic data generation and application?

A strong foundation in SAP data structures is essential. Consultants who already work with SAP tables, transactions, and business processes will find it easier to define the rules for generating meaningful synthetic data. For those less familiar with SAP data models, training in SAP database management can provide useful background knowledge.

Many of the techniques used for managing production test data, such as subsetting, anonymization, and data validation, are also relevant to synthetic data. Learning how to design datasets that accurately reflect business scenarios will help consultants create synthetic data that is both realistic and useful for testing and training purposes.

Data science skills can be a valuable addition. While not all SAP consultants will need to become machine learning experts, familiarity with statistical modeling and data generation techniques will allow for more effective collaboration with data science teams. Online courses in Python-based data synthesis can provide a useful introduction.

Understanding the theory is essential but hands-on experience is also necessary.?

Consultants can start by experimenting with open-source synthetic data libraries, such as the Synthetic Data Vault (SDV) in Python, to generate small-scale datasets for SAP-related projects. Many test data management tools also offer trial versions, allowing consultants to explore rule-based generation in a sandbox environment.

Keeping up with industry discussions can provide valuable insights. SAP Community forums, LinkedIn discussions, and professional networks often feature real-world examples of how synthetic data is being used in SAP testing and AI projects. Engaging with these discussions can help consultants stay ahead of emerging trends and tools.

Low Risk Testing and Modeling?

Synthetic data offers a flexible, scalable alternative to production data. It addresses long-standing challenges around data privacy, accessibility, and completeness. Whether used to simulate complex business scenarios in testing environments or to generate training data for AI-driven applications, synthetic data allows consultants to work with high-quality datasets with far less risk and without the limitations associated with real records.

For those working in SAP implementations, testing, or AI development, understanding how to generate and apply synthetic data is becoming an increasingly valuable skill. While it requires a mix of SAP expertise, test data management knowledge, and, in some cases, familiarity with AI-driven techniques, it is a field with growing relevance.

As more organizations move toward AI-driven decision-making and stricter data privacy regulations, the demand for synthetic data solutions is likely to increase. SAP consultants who can work effectively with synthetic data will not only improve testing efficiency but also regularly contribute to the development of more reliable and innovative SAP-driven solutions.

If you are an SAP professional looking for a new role in the SAP ecosystem our team of dedicated recruitment consultants can match you with your ideal employer and negotiate a competitive compensation package for your extremely valuable skills, so join our exclusive community at IgniteSAP .