Salesforce Synthetic Test Data
Salesforce synthetic test data is business records generated from scratch — never derived from a real customer — that satisfy your field rules, picklists, validation logic and object relationships. SyntraFlow is designed to build realistic, referentially complete records so tests get exactly the scenarios they need with zero exposure to production data.
This page focuses narrowly on generating synthetic records. For the wider discipline of preparing and provisioning test data, see the Salesforce test data management pillar.
What synthetic test data is — and when to reach for it
Synthetic test data is a set of Salesforce records — Accounts, Contacts, Opportunities, Cases, custom-object rows and their relationships — manufactured to match a target scenario rather than copied from a live org. Because no value traces back to a real person or contract, synthetic data carries no privacy risk, which makes it a natural fit for sandboxes, scratch orgs and CI pipelines where access controls are looser than production.
Synthetic generation is the right choice when the scenario you need is rare, brand-new or simply absent from production: a maximum-discount quote, a five-year renewal that has not happened yet, a case that hits every escalation branch, or the first records for an object you shipped last sprint. It also shines when you need volume — hundreds of predictable records for a performance or bulk-trigger test — without dragging real customer data into a lower environment.
The trade-off to understand is realism versus control. Synthetic data gives you complete control — you decide every value and every relationship — but it only approximates the messiness of a live org: the legacy formats, the half-filled fields, the unusual distributions that accumulate over years. For most functional and regression tests that control is exactly what you want, because a predictable, seeded dataset makes a failure easy to reproduce. Where a test depends on production's real-world variety, a masked slice is the better source. Many mature test suites use both: synthetic records for deterministic scenarios and edge cases, and a masked subset for volume and realism.
Synthetic data is deliberately different from a masked copy of real records, which preserves production's statistical shape and long-tail quirks. When that realism matters more than control, a masked slice is the better tool — see Salesforce data masking and data subsetting. Synthetic records are also records, not configuration; if you need to compare or track the objects, fields and Flows themselves, that is metadata intelligence, not test data.
How SyntraFlow is designed to generate synthetic data
These capabilities are available for demonstration and proof-of-concept validation. The goal is records that are realistic, valid on insert and complete across their relationships.
Schema-aware field values
Designed to read each object's field types, required flags, picklist values, length limits and record types so generated values are valid the first time and inserts are not rejected.
Validation-rule awareness
Designed to respect validation rules and cross-field constraints — a close date after a created date, an amount within an allowed band — so synthetic records survive the org's own logic.
Related-record generation
Designed to build the full chain a scenario needs — an Account with Contacts, an Opportunity with Contact Roles and line items — in dependency order so no required lookup is left empty.
Realistic distributions
Designed to spread values across names, regions, amounts and dates so a generated set looks plausible rather than a thousand identical rows that mask real behavior.
Edge-case scenarios
Designed to deliberately manufacture boundary and negative cases — empty pipelines, maximum discounts, expired entitlements — that are hard to find reliably in live data.
Repeatable seeds
Designed to regenerate the same logical dataset on demand, so a regression suite gets an identical starting state each run and failures point to code, not shifting data.
Custom-object support
Designed to generate against each org's own custom objects and master-detail relationships, because every implementation extends the standard model differently.
Bulk volume generation
Designed to produce large record counts for bulk-trigger, list-view and performance tests without copying anything sensitive from production.
Reusable scenario templates
Designed to save a scenario's shape as a template so the same renewal or escalation dataset can be regenerated into any sandbox on request.
Synthetic vs. copied production data
A quick decision guide. Neither approach is universally better — the right one depends on what the test is trying to prove.
| Consideration | Synthetic generation | Copied / masked production |
|---|---|---|
| Privacy exposure | None — no value traces to a real person | Requires masking to be safe |
| Edge & new scenarios | Manufactured on demand | Limited to what exists in production |
| Real-world messiness | Approximated, not guaranteed | Faithful to legacy quirks and distributions |
| Volume for scale tests | Generated to any count | Bounded by source size and sandbox limits |
| Setup for a new object | Works with no history | Needs existing records to copy |
| Repeatability | Deterministic from a seed | Depends on the source snapshot |
Synthetic data that spans more than Salesforce
A synthetic customer often needs a matching counterpart in the ERP, billing or portal system an end-to-end test touches. SyntraFlow is designed to generate coordinated synthetic records across Salesforce and connected systems such as Oracle ERP, SAP, Workday and NetSuite, so a manufactured account carries consistent identifiers on every side. Explore the counterpart discipline in the Oracle ERP testing tool.
What teams aim for with synthetic data
Zero production exposure
No real customer, employee or commercial data enters a sandbox, so a lower environment can never leak what it never held.
Scenarios on demand
Rare and edge-case situations are manufactured when a test needs them instead of waiting for them to appear in production.
Valid on first insert
Schema- and rule-aware generation means fewer failed loads and less time spent fixing data that the org rejected.
Repeatable baselines
Seeded generation gives every regression run the same starting data, so failures reflect defects rather than shifting inputs.
Scale without risk
Bulk volumes for performance and trigger tests are generated freely, unconstrained by source size or privacy concerns.
Ready for new features
A brand-new object or feature can be tested from day one, before any real records exist to copy.
Synthetic test data FAQs
What is Salesforce synthetic test data?
It is business records — Accounts, Contacts, Opportunities, Cases and custom-object rows — generated from scratch to satisfy field types, picklists, validation rules and relationships, without deriving from any real record. Because nothing traces to a real person or contract, it carries no privacy risk.
When is synthetic data better than copying production?
When the scenario is rare, brand-new or absent from production, when you need bulk volume for scale tests, or when you want zero exposure to real data. When you instead need production's exact statistical shape and legacy quirks, a masked subset is the better fit.
How does synthetic data stay valid against Salesforce rules?
SyntraFlow is designed to read each object's schema — field types, required flags, picklists, length limits and record types — and to respect validation rules and cross-field constraints, so generated records pass the org's own logic on insert instead of being rejected.
Can synthetic records include related objects?
Yes. Generation is designed to build the full chain a scenario needs in dependency order — an Account with its Contacts, an Opportunity with Contact Roles and line items, plus any custom-object rows — so no required lookup is left empty and the data behaves like a coherent scenario.
Is synthetic data repeatable across test runs?
Yes. Seeded generation is designed to reproduce the same logical dataset on demand, giving a regression suite an identical starting state each run so a failure points to a code change rather than to data that drifted between executions.
Does synthetic data work for custom objects?
Yes. Because every org extends the standard model differently, generation is designed to inspect and target each org's own custom objects and master-detail relationships rather than assuming a fixed schema, so custom automation still fires against the generated records.
Generate the records your tests actually need
See how SyntraFlow is designed to generate realistic, referentially complete synthetic Salesforce data with no exposure to production.