5 costly SAP test data mistakes (and how to avoid them)

In complex SAP projects, it’s not always the big architectural decisions that derail delivery. Sometimes, it’s the small, overlooked details – especially when it comes to test data.

Test data is the backbone of quality assurance. When it’s inaccurate, incomplete, or poorly managed, even the best project plan can grind to a halt. And the cost isn’t just in wasted testing hours – it’s in missed go-lives, compliance risks, and the erosion of trust across the business.

Here are five of the most common (and costly) mistakes we see in SAP test environments – and how you can avoid them.

1. Using full-system copies by default

Why it’s costly:

Defaulting to full-system copies inflates infrastructure costs, extends refresh timelines, and brings unnecessary (and often sensitive) data into non-production environments. You end up with more data than your tests actually require – and more risk than you need to carry.

How to avoid it:

Use subset-based provisioning aligned to your test scope. Preserve referential integrity so that related data remains intact, and only move what’s needed for the scenarios you’re testing.

2. Manual or inconsistent scrambling

Why it’s costly:

When scrambling is done manually or inconsistently, sensitive data can slip through unmasked – or business logic can break in ways that cause false test failures. Compliance becomes reactive, adding unnecessary stress to already tight timelines.

How to avoid it:

Automate scrambling at the point of extraction, with rules-based logic that’s applied consistently across every environment. Build masking into the provisioning process, not as a bolt-on at the end.

3. Broken referential integrity

Why it’s costly:

When relationships between data objects are lost – for example, between modules or across clients – test automation scripts can fail for the wrong reasons. Testers spend hours chasing issues that aren’t real defects, wasting both time and budget.

How to avoid it:

Use processes and tooling that preserve relationships during subsetting and refreshes. This ensures your test data behaves like Production data, without exposing actual Production records.

4. Stale or outdated data

Why it’s costly:

Testing against outdated organisational structures, processes, or configurations is like training for a race on a different course. You risk validating scenarios that no longer exist – and missing defects that will appear in Production.

How to avoid it:

Refresh data regularly, based on project milestones or release cycles. Focus on scenario-driven refreshes that ensure data is relevant to current business processes.

5. No business input in data validation

Why it’s costly:

When business subject matter experts (SMEs) aren’t involved in data validation, test cases can lack real-world context. It’s easy to pass tests in a ‘perfect’ technical environment that would fail immediately in Production.

How to avoid it:

Involve SMEs early in the planning and validation stages. Their input helps ensure data realism and usability, increasing the accuracy of your test results.

The hidden cost of these mistakes

Each of these issues can delay testing and inflate costs on its own; but together, they can quietly erode confidence in your SAP program and slow transformation projects to a crawl.

Want the full list?

These are just five of the mistakes we see most often. We’ve compiled a full checklist of the 10 most common SAP test data mistakes – plus the proven fixes that improve speed, security, and stability in every test cycle.

Download the full checklist here and start building a test data strategy that accelerates, not delays, transformation.