How to Generate Realistic SAP Test Data with DataMaker

Testing SAP applications is never simple. Between massive data volumes, complex dependencies, and strict compliance rules, creating reliable SAP test data often feels like a full-time job on its own.

Most teams either copy production data (risky) or mask it (still risky and rarely realistic).

But there’s a faster, safer, and more scalable way: synthetic SAP test data generation combined with automated and on-demand data workflows.

In this guide, we’ll show step by step how to generate high-quality, production-like SAP test data using DataMaker, the same platform featured on SAP’s official website through CNT Management Consulting AG.

Why Synthetic SAP Test Data Matters

Every SAP process, whether it’s Order to Cash (O2C), Procure to Pay (P2P), or Hire to Retire (H2R), depends on data that stays consistent across multiple modules.

If one record breaks the relationship between master and transactional data, the entire test scenario collapses.

That’s why “fake” or manually created data isn’t enough. You need data that behaves like production data, without exposing any real business or personal information.

The Challenge with Traditional Methods

Traditional Approach	Common Issue
Copying Production Data	Violates GDPR and internal compliance policies.
Masking or Scrambling	Breaks SAP relationships; leads to unusable data.
Manual Data Entry	Time-consuming, inconsistent, and prone to error.

The Synthetic Advantage

Synthetic SAP test data generated with DataMaker gives you:

• Production-like realism without exposing real PII or sensitive business info
• Repeatable datasets that can be refreshed or regenerated on demand
• Full control over structure, volume, and variability
• Zero compliance risk, since the data never originates from production

How DataMaker Generates SAP-Ready Test Data

DataMaker helps SAP QA teams generate structured, realistic synthetic data and push it into SAP systems using OData REST APIs.

Teams can create templates manually or use DataMaker’s AI-assisted workspace to describe datasets and workflows in natural language.

It doesn’t automatically interpret SAP schemas or business logic.

Instead, you define the structure (template), and DataMaker generates values that match your rules; then it sends them into SAP through the OData service you configure.

Here’s what DataMaker provides:

1. OData-based SAP connectivity

DataMaker connects to SAP systems using standard OData REST endpoints. This allows the platform to send synthetic records directly into SAP environments using common API methods such as POST and GET. Because the integration relies on standard OData services, no custom SAP connector is required.

2. Flexible data modeling with templates

Testers define the SAP fields, formats, and relationships required by the target OData service. These templates act as blueprints for the synthetic data that will be generated. DataMaker then produces values that match the rules defined in the template.

Teams can also speed up template creation by uploading sample payloads, such as JSON files, through DataMaker’s AI chat workspace. The platform can use these samples to help model the data structure more quickly.

3. Synthetic data generation based on defined rules

Once the template is ready, DataMaker generates synthetic master and transactional data that follows the structure defined by the template. This ensures datasets remain consistent while still allowing teams to control volumes, patterns, and variations.

4. Direct data delivery into SAP environments

After generation, DataMaker sends the records directly into SAP QA or sandbox systems using OData POST requests. SAP then applies its own validations and business rules when the records are inserted.

5. Privacy-by-design synthetic datasets

All generated records are synthetic and do not originate from production data. Because the platform does not rely on copying or masking real records, teams can test safely without exposing personal or sensitive business information.

Step-by-Step: How to Generate Synthetic SAP Test Data with DataMaker

Step 1: Connect DataMaker to SAP Using an API (POST) Connection

To send synthetic test data into SAP, DataMaker uses SAP’s OData service, which accepts standard REST API requests.

Since OData handles record creation through POST, you will create a POST-based API connection inside DataMaker.

To connect:

1. First, log in to DataMaker.
2. Now, in DataMaker, go to Connections.
3. Click New Connection.
4. Select API.
5. Name your connection (for example, “SAP S/4HANA QA OData”).
6. Choose POST (SAP requires POST for creating new records).
7. Enter your SAP OData endpoint URL.
8. Add the required authentication.
9. Save and test the connection.

DataMaker can now send synthetic SAP test data directly into your system using the configured OData POST request.

Step 2: Create a Template for the SAP Data You Want to Generate

Once your SAP API connection is ready, the next step is to build a template in DataMaker.

This template defines the fields and values that will be sent to SAP through the OData service.

How to do it:

1. Go to Templates in DataMaker
2. Click New Template
3. Select Create Template From Scratch or create one using a sample payload. This subtly references JSON-based template creation.
4. Choose the SAP API connection you created in Step 1
5. Add the fields you want to generate (these must match the SAP OData payload)

Inside the template, you simply choose:

• Which fields to generate
• How each field should be generated (text, number, date, list, etc.)
• How many records do you want

DataMaker doesn’t read SAP schemas automatically — you manually define only the fields required by your OData service.

Step 3: Generate Synthetic Data Inside SAP

When your templates are ready, you trigger generation from inside DataMaker. The platform creates synthetic records based on the instructions in your template and then inserts them into SAP via your OData endpoint.

Here’s what happens:

• DataMaker generates synthetic values according to your template rules.
• It sends the generated records to your SAP system using the OData interface you configured.
• SAP processes the incoming data according to its own logic and validations.

DataMaker ensures consistency with your template, and SAP ensures consistency with SAP’s internal rules when the data is inserted.

In short, DataMaker handles the synthetic data creation, and SAP handles the business rule validation when the data is received.

Step 4: Validate and Use the Data

After generation, use DataMaker’s validation module (or your SAP QA tools) to verify record consistency.

Key checks include:

• Cross-module data alignment (O2C, P2P, H2R, R2R).
• Value field validations (currencies, units, account assignments).
• Business rule compliance (credit limits, pricing conditions, etc.)

You can now:

• Run automated SAP QA tests and regression suites.
• Simulate end-to-end workflows safely.
• Share realistic data with external testing partners or demo systems.

Step 5: Maintain and Refresh Your Test Data Cycle

SAP systems evolve constantly, and so should your test data.

DataMaker lets you:

• Refresh existing datasets with new synthetic values
• Fill coverage gaps across business processes
• Schedule automated regeneration in CI/CD pipelines
• Automate recurring data preparation using reusable generation scenarios or scripts

This way, your test environment never goes stale, and every sprint has the data it needs instantly.

Integrating DataMaker into Your QA & Automation Pipeline

Synthetic data is even more powerful when automated.

• Export through REST API: Connect DataMaker to your test automation framework (e.g., Tosca, Xray, Playwright, or any REST-compatible tool).
• Trigger on demand: Generate fresh datasets automatically before every run or pipeline execution.
• Automate workflows: Reuse templates or scripted scenarios to prepare datasets for recurring regression tests.
• Monitor via dashboard: Keep visibility over data freshness, coverage, and generation success.

This lets you achieve continuous test data provisioning,  a cornerstone of modern DevOps-enabled QA.

Best Practices for Generating SAP Synthetic Data

Follow these best practices to get the most value from synthetic SAP test data:

1. Start with critical business processes:  Finance, Procurement, and HR often expose the biggest risks.
2. Use modular templates:  Keep master and transactional templates separate for easier updates.
3. Validate early and often:  Run quick checks before full-scale generation.
4. Align with test cases:  Generate data that matches your QA scripts and expected input formats.
5. Automate regeneration:  Include DataMaker in your CI/CD cycle to keep test data fresh.
6. Document versions:  Track template versions and data generation logs for audit and reproducibility.

Ready to Get Started?

Generating synthetic SAP test data does not have to be complicated. With the right approach and tools, teams can create realistic datasets, automate provisioning, and maintain compliance without relying on production data.

If you would like to understand the process in more detail or see how DataMaker works in a real SAP testing environment, our team can walk you through it.

 Please do not hesitate to book a Live Demo to See How DataMaker Works.

Final Thoughts

SAP systems rely on tightly connected data across multiple modules, which makes test data preparation one of the most challenging parts of SAP QA.

Copying production data introduces compliance risks, and manually creating datasets rarely reflects real business scenarios.

Synthetic test data offers a more reliable alternative. By generating structured datasets that follow SAP rules and relationships, teams can test complex workflows without exposing real business or personal data.

With DataMaker, QA teams can define the structure of their SAP data, generate synthetic records, and push them directly into SAP environments using standard OData services. The platform also allows teams to automate data preparation workflows and regenerate datasets when needed, making it easier to maintain consistent test environments across sprints and releases.

For SAP teams that want predictable, privacy-safe, and repeatable test data, synthetic generation provides a practical path forward.