Why Determinism & Realism Are So Critical in Enterprise Synthetic Data

Generating synthetic data is one thing. Trusting it is another.

As enterprises adopt synthetic data for development and testing, important questions quickly emerge: Can it be reproduced consistently? Does it accurately reflect real-world business scenarios?

In fact, our survey of enterprise leaders found that “consistent, high-quality test data to reduce defects” was their #1 priority in test data automation at 43%.

Mayank Ahluwalia, Senior Product Manager at Perforce Delphix, frequently discusses these challenges with enterprise development and testing leaders. For this second blog in our “Non-Negotiables in an Enterprise Synthetic Solution” series, he and I discussed two capabilities that often separate enterprise-ready synthetic data solutions from the rest: determinism and realism. 

While our previous post in this series on the importance of referential integrity in enterprise synthetic data explored data relationships, this conversation focuses on what makes synthetic data reliable, repeatable, and useful at scale.

Q: In our last conversation, we discussed referential integrity as a non-negotiable for enterprise synthetic data. Why are determinism and realism the next capabilities organizations should evaluate?

Mayank:

Determinism is what breaks first, usually the moment someone tries to put synthetic data generation into a CI/CD pipeline or a regulated audit trail. Realism is what breaks second, once teams realize their tests are passing against data that doesn't resemble anything their production systems actually produce. 

Both are invisible on day one. That's exactly why they need to be evaluated deliberately rather than discovered the hard way.

Q: What does determinism mean in the context of synthetic data, and why does it matter?

Mayank:

Determinism means the same schema, the same rules, and the same seed produce the same dataset every time you run it. Not just similar, the same. That sounds like a basic ask, but a lot of synthetic data tools today lean on an LLM at generation time to infer values. The problem is that LLM output isn't guaranteed to be identical across runs even with the same prompt. 

Determinism matters because synthetic data isn't just an output, it's an engineering artifact that other things depend on: a test suite, a bug report, a compliance record. If you can't regenerate it exactly, you can't treat it as ground truth for anything downstream.

This challenge is driving interest in newer approaches to synthetic data generation. For example, Delphix Synthetic Data combines AI-assisted discovery and configuration with governed generation, helping your teams accelerate setup while maintaining the control and reproducibility required for enterprise testing and development.

Q: What problems do development and testing teams run into when synthetic data generation isn't deterministic?

Mayank:

• The most common one is a flaky test that nobody can reproduce. QA reports a failure, the engineer reruns the same test, and the underlying data is subtly different, so the bug doesn't show up. Now you're debugging a moving target. 
• The second is version drift. If you can't regenerate dataset v12 exactly, you can't diff it against v13 to understand what changed when your schema evolved. 
• The third is audit exposure, which matters a lot in banking and healthcare. If a regulator asks what data a given test run used and you can't reproduce it on demand, that's a gap you do not want to explain. 

None of these are edge cases. They're what happens every week once synthetic data generation is actually load-bearing in your pipeline instead of a one-off convenience.

Q: Is determinism important as organizations adopt AI-assisted and agentic development workflows?

Mayank:

Yes, and I'd say it's underrated right now. 

When a person generates test data once and reviews it, non-determinism is annoying but survivable, you just look at what you got and adjust. 

When an agent is running the generation step as part of a longer autonomous loop, writing code, running tests, generating more data, and iterating... non-determinism compounds. The agent can't reliably tell whether a test failure came from its code change or from the data changing under it between runs. You end up debugging noise instead of signal, and the agent loop either stalls or produces unreliable conclusions. 

Agentic workflows need a stable, reproducible substrate to reason against at every step. Determinism stops being a nice property and becomes a prerequisite for the whole workflow to function.

“Determinism stops being a nice property and becomes a prerequisite for the whole workflow to function.”

Q: Let's talk about realism. When you say synthetic data should be realistic, what does that mean in practice?

Mayank:

Realism in the context of dev, QA, and test data management means the data respects the actual shape of your domain. Some examples are:

• A UK sort code that passes modulus checking. 
• An account balance history that never goes negative if your application logic forbids it. 
• A telecom EDR file that resolves cleanly against a subscriber table with the right plan and cell site. 

It also means cross-table and cross-schema consistency, an order referencing a customer that actually exists, a claim referencing a policy that's still active. 

Realism is the difference between data that satisfies a schema and data that would survive contact with your actual business logic.

(As a side note, for clarity: Realism in this context is not the same thing as statistical fidelity to a production distribution. That's an ML training concern.)

Q: What impact does unrealistic synthetic data have on software quality and testing outcomes?

Mayank:

The dangerous outcome isn't that tests fail, it's that they pass when they shouldn't. 

Unrealistic data tends to avoid the edge cases and constraint violations that real production data eventually throws at your system. That means your test suite goes green while the code underneath has gaps nobody caught. 

It creates a false sense of coverage, and it's worse than no synthetic data at all because it actively erodes trust once a bug reaches production that testing should have caught. 

I've also seen the second-order effect: teams get burned by this once and decide synthetic data "doesn't work,” then miss out on the quality and efficiency gains they would have had with a synthetic data solution that does ensure data realism.

“[Unrealistic synthetic data] creates a false sense of coverage, and it's worse than no synthetic data at all because it actively erodes trust once a bug reaches production that testing should have caught.”

Data Quality Is the Top Test Data Challenge

Poor-quality test data is one of the biggest obstacles organizations face today. In fact, enterprise leaders identified data quality as both their #1 test data challenge and the top priority for test data automation.

Explore more findings from 500+ enterprise leaders in The 2026 Test Data Management Report for AI-Ready Enterprises.

Get My Insights [2026 Report]

Q: Why is it so difficult for synthetic data solutions to achieve both determinism and realism at enterprise scale? 

Mayank:

There's a real tension in how most tools are architected. 

LLM-driven generators get you closer to realism because the model can infer intent from a column name or a business context, but that inference is exactly what makes the output non-deterministic and hard to fully control. 

On the other end, purely rule-based or faker-style tools are deterministic. But the rules are usually generic, first name, last name, random integer, and don't reflect the actual constraints of your domain unless someone hand-writes every rule. Most users and providers don't invest in that. Very few approaches are built to give you domain-aware realism without giving up reproducibility.

This is the balance enterprise teams are increasingly looking for from synthetic data solutions. We designed Delphix Synthetic Data to help organizations like yours generate realistic, scenario-specific test data while preserving the consistency, governance, and repeatability required by modern development pipelines and AI-assisted workflows.

Q: If an organization is evaluating synthetic data solutions, what capabilities should they look for to ensure both determinism and realism?

Mayank:

A few concrete things I'd check:

1. First, can the tool regenerate an identical dataset from the same schema and configuration — not "similar" but identical, this is important — and can you version that configuration the same way you'd version code?
2. Second, are the business rules something you can actually inspect and edit, or are they a black box you have to trust blindly?
3. Third, does realism come from understanding your schema and domain, cross-table references, format validation, and business constraints, rather than depending on an LLM call at generation time that you can't fully pin down? 

The organizations that get this right treat synthetic data generation as governed infrastructure, not a black box generator. That governed path ends up being the fast path, because it's the only one that still works when you scale it across a hundred pipelines instead of one.

Final Thoughts: Determinism and Realism Are Essential for Enterprise Synthetic Data

As synthetic data becomes a critical part of software delivery, organizations need more than data that simply looks valid. They need data they can reproduce consistently, trust in testing, and scale across increasingly complex development environments.

As Mayank explained, determinism and realism are closely connected. Without determinism, teams struggle to troubleshoot, automate, and govern synthetic data at scale. Without realism, tests may pass while critical defects remain hidden until production.

Together, these capabilities help ensure synthetic data delivers on its promise: faster development, better testing outcomes, and greater confidence in every release.

If you haven’t read it yet, check out my first post in this Q&A series with Mayank: #1, Referential Integrity. 

In the next post, we will explore another non-negotiable capability for enterprise synthetic data: business rules, and why understanding real-world business logic is critical to generating test data teams can trust.