Evaluate Enterprise Synthetic Data: A Decision Maker’s Guide

Referential integrity matters in synthetic data because enterprise applications span multiple databases. Test data has to reflect the connections between those systems. 

• Data that looks right in each system can quietly break across systems. 
• It may load without errors, then cause test failures that have nothing to do with your code. 
• Integration and end-to-end tests lose their meaning. 
• Teams building new applications may not uncover integration issues until late in the development cycle. 

Look for a solution that keeps data connected across all your systems, not just within one database. Ask whether it can: 

• Preserve relationships across database boundaries, not just within a single schema. 
• Generate in dependency order, parents before children, in a single pass across all connected systems. 
• Work with tables that already contain data, not only clean, empty environments. 
• Handle schema changes without manual reconfiguration.  
• Keep masked production data and synthetic data consistent when they coexist in the same environment. 

Delphix maintains the referential integrity of synthetic and masked data across connected systems, not only within an individual database. It builds on the referential integrity Delphix has refined over years of masking data in complex enterprise environments.  

1. Generation is synchronized across and within heterogeneous databases, such as Postgres and Oracle together. 
2. Platform-generated keys are handled automatically. 
3. Data is inserted into databases that already contain data, with awareness of existing keys. 
4. Schema changes are detected, and generation rules adjust without manual rework. 
5. Masked and synthetic data share one understanding of relationships, so they combine into one consistent dataset. 

Determinism and realism matter in synthetic data because you can only trust data that you can reproduce and that behaves like production. Determinism means the same schema, rules, and seed produce the same dataset every time. 

• Without determinism, QA reports failures that engineers can’t reproduce. 
• Datasets drift between versions, and you can’t regenerate what an auditor asks about. 
• AI agents can’t tell if a failure came from their code or from changing data. 
• Without realism, tests go green on data that ignores real business logic, such as an invalid sort code. 

Look for a solution that generates data consistently on demand and realism that comes from your own domain. Ask whether it can: 

• Use schema, relationships, formats, and business constraints to create realistic data. 
• Regenerate an identical dataset from the same schema and configuration, not just a similar one.  
• Version that configuration the way you version code. 
• Let you inspect and edit business rules, not hide them in a black box. 
• Produce realistic data without relying on an LLM call at generation time. 

Delphix uses AI to speed up setup, then generates data from governed rules. That keeps output repeatable and realistic. 

1. AI-assisted discovery and configuration reduce manual setup. 
2. AI helps configure generation but doesn’t generate the values. 
3. Business rules are Python-based, so you can inspect and edit them. 
4. The same rules produce the same output on every run. 
5. Rules can be versioned and audited like code. 
6. Realism comes from your schema, cross-table references, formats, and business constraints. 

Business rules matter in synthetic data because your software enforces logic that random data ignores. Generated data has to follow the same rules your application does. 

• Rules govern valid formats, allowed values, and how fields depend on each other. 
• Data that breaks these rules gets rejected, or it tests the wrong things. 
• New products, fields, and rules have no production data to copy from. 
• Unit and scenario tests need data shaped to specific rules. 

Look for a solution that learns your rules automatically and lets you add your own. Ask whether it can: 

• Infer rules from your schema and metadata where possible, instead of requiring every rule to be documented and hard-coded. 
• Capture rules between columns and between tables, not just within a single field. 
• Extend generation for complex, derived values using a standard language, not a proprietary framework. 
• Keep working when the schema changes. 

Delphix uses AI to configure business rules for you, then lets you extend them with Python where needed. Most fields are handled automatically, and special cases are easy to add. 

1. AI analyzes your metadata and assigns generation logic to each column. 
2. That analysis captures rules between columns and tables that manual setup often misses. 
3. Custom Python generators handle complex, interdependent values, such as a valid UK driving license number built from a person’s name and date of birth. 
4. Any developer who knows Python can write rules, with no specialist framework needed. 
5. When the schema changes, generation rules adjust without manual rework. 

Language- and location-specific data matters because global enterprises run on regional formats. Test data has to look like the data your applications handle in each market. 

• Names, addresses, currencies, dates, and identifiers vary by region and language. 
• Data in the wrong format fails validation or hides defects that appear in one market. 
• Teams expanding into new geographies have no production data to copy. 
• Schemas in some regions use non-English column names, which slows manual setup. 

Look for a solution that generates region-correct data without hand-built rules for every locale. Ask whether it can: 

• Generate names, addresses, currencies, and identifiers in the formats each region uses. 
• Support the specific locales and languages you operate in. 
• Understand schemas with non-English column names without manual mapping. 
• Handle region-specific identifier rules, not just generic formats. 

Delphix uses locale-aware AI to generate data that fits each region. That cuts manual setup for global applications. 

• AI-assisted discovery is locale-aware. 
• It identifies business meaning in non-English column names without manual input. 
• It assigns generation logic to each column automatically. 
• Custom Python rules can cover region-specific identifier logic. 
• Teams can generate data for a new geography before any production data exists there. 

Data shape and distribution matter because applications behave differently on realistic data than on evenly random data. Shape is how values cluster, skew, and repeat across your records. 

• Real data has proportions, such as the mix of states, event frequencies, and transaction types. 
• Evenly spread random data can hide query and performance problems. 
• It can also leave out the rare events your application must handle. 
• Tests on unrealistic shapes can pass while real-world behavior goes untested. 

Look for a solution that builds realistic proportions into generation from the start. Ask whether it can: 

• Produce realistic proportions, frequencies, and skew, not just valid values. 
• Base the shape of generated data on statistical analysis of your data, not uniform randomness. 
• Learn the shape of your data without exposing actual data or PII to an AI or LLM. 
• Let you adjust distributions for a specific scenario, such as more failed transactions. 
• Show you a preview so you can check the shape before generating at scale. 

Delphix uses statistical analysis to learn the shape of your data. It then configures generation to match, so the result has a representative shape and distribution.  

1. AI scans only metadata and schemas to discover data structures and relationships. 
2. Statistical analysis captures the shape and distribution of your data.(No actual data or PII is exposed to AI or LLMs in this process.) 
3. Generation logic is configured automatically from that analysis. 
4. Generated values follow realistic proportions and frequencies, not uniform randomness. 
5. You can fine-tune the data with natural-language requests. 
6. A sample preview lets you check the shape before generating the full dataset. 

Ease of use matters in synthetic data because the people who need data are often not data engineers. If only specialists can generate data, everyone else waits in a queue. 

• Testers and developers need data for new features without writing scripts. 
• Every new rule or schema change becomes an engineering ticket. 
• Business users need realistic data for training and user acceptance testing. 
• AI agents need to request data themselves, without a person in the loop. 

Look for a solution that lets non-specialists get usable data without deep database knowledge. Ask whether it can: 

• Discover your schema and configure generation automatically, instead of requiring regex and manual rules. 
• Accept plain-language requests from testers and developers. 
• Show a preview of the data before you generate it at scale. 
• Offer APIs so pipelines and agents can request data on their own. 

Delphix offers an easy-to-use interface with an AI assistant. Teams describe what they need and get realistic data in minutes. 

1. AI discovers your schema and configures generation automatically. 
2. The in-product AI assistant turns a plain-language request into the relevant data. Example: “Create 200 customer records, each with multiple valid orders.” 
3. A sample preview lets you fine-tune the data before the full run. 
4. Open APIs and MCP access let pipelines and AI agents request data without manual jobs. 
5. Developers, testers, and agents can generate and deliver data on-demand, without waiting for a specialist. 

Correlations and patterns matter because realistic scenarios depend on fields that move together. Data with unrelated fields can pass validation but still not look like real behavior. 

• A customer’s location, product, and price should fit together. 
• A name and date of birth should match the identifier built from them. 
• Production data often lacks the invalid, out-of-bounds, and rare-but-valid cases that edge-case and negative testing need. 
• Without credible patterns, tests miss how your application behaves in the real world. 

Look for a solution that preserves relationships between data elements and lets you create edge cases on purpose. Ask whether it can: 

• Keep related fields consistent within a record. 
• Keep patterns consistent across records and tables. 
• Generate rare, invalid, or out-of-bounds data on demand. 
• Tailor data to a specific test scenario, not just a generic dataset. 

Delphix uses AI to understand your data, then configures generation to preserve how it fits together. Teams can then shape the data for each scenario. 

1. AI-assisted discovery infers what data means, how it relates, and its industry context. 
2. Generation is configured automatically to preserve those relationships and patterns. 
3. Teams generate data on-demand for a specific testing scenario. (Important note: Unlike other tools, Delphix does not use an LLM to generate data, which would result in non-deterministic data and excessive hidden costs.) 
4. Edge cases and negative scenarios can be created deliberately, not left to chance. 
5. Natural-language requests and a sample preview help fine-tune the result. 

A unified platform matters because enterprise testing needs more than synthetic data. Teams also need masked data, fast delivery, and a single control plane. 

• Masked data covers established workflows. Synthetic data covers new features, edge cases, and negative tests. 
• Separate tools leave teams to reconcile keys and relationships by hand. 
• Generated data only helps if it reaches every dev, test, and agent environment quickly. 
• Policies and visibility fragment across separate tools. 

Look for a solution that treats synthetic data as part of a complete test data strategy. Ask whether it can: 

• Combine masked and synthetic data into one dataset with relationships intact. 
• Deliver data quickly to many environments, not just generate it. 
• Support blended or synthetic-only data, depending on the test. 
• Give you one central place to see your data environments and apply consistent policies. 

Delphix Synthetic Data is part of the Delphix DevOps Data Platform. The platform also includes sensitive data discovery, data masking, data virtualization and delivery, and centralized data control. 

1. Sensitive data discovery: Automatically identify sensitive data across enterprise environments to improve visibility, compliance, and protection. 
2. Masking: Masked and synthetic data share one understanding of relationships, so they behave like one system. 
3. Virtualization and delivery: Space-efficient virtual copies and automated delivery get data into dev, test, and agent environments fast. 
4. Centralized control: One place to see your data environments and apply consistent policies across masked and synthetic data.   

Self-service data environments matter because generating data is only part of the lifecycle. Teams also need to deliver, refresh, share, and reproduce it. 

• Large teams and AI agents multiply the number of environments needed. 
• Waiting on tickets for each refresh slows development. 
• Debugging means getting back to the exact data state where a bug appeared. 
• Shared datasets drift unless they are versioned and controlled. 

Look for a solution that manages data across the development lifecycle, not just at generation. Ask whether it can: 

• Rewind, bookmark, branch, and reset datasets — not just generate static copies. 
• Give each developer or agent an isolated environment. 
• Provision environments quickly without excessive storage. 
• Offer APIs so teams and agents can self-serve without tickets. 

Delphix lets teams and agents get, reset, and reproduce data on their own. Synthetic data can be delivered instantly to individual sandboxes. 

• Generated data is delivered to one-to-one sandboxes for large teams and agents. 
• Self-service APIs support refresh, rewind, bookmark, branch, and reset. 
• Space-efficient virtual copies keep many environments affordable. 
• Rewind and bookmarks return teams to the exact data state where a bug appeared. 
• The MCP server lets agents run data operations and see which environments and snapshots are available. 

Governance matters because synthetic data still has to follow enterprise rules. Self-service and AI agents make clear controls more important, not less. 

• Auditors ask what data a test used and who generated it. 
• Separate tools mean separate policies and fragmented audit trails. 
• Self-service and agent requests need guardrails. 
• Synthetic-only mandates need to be enforced consistently. 

Look for a solution that governs masked and synthetic data in one place. Ask whether it can: 

• Apply central policy across both masked and synthetic data. 
• Keep audit trails of what was generated, by whom, and with which configuration. 
• Control access and set guardrails on self-service and agent requests. 
• Regenerate a past dataset on demand for an audit. 

Delphix applies one governance framework to masked and synthetic data. Security and compliance teams get a single place for policy and audit. 

• Policy-based governance spans masked and synthetic data. 
• Audit trails record data activity. 
• Generation rules are deterministic, so past runs can be regenerated. 
• The MCP server gives AI workflows compliance context. 
• Controls help keep sensitive data out of non-production environments.