---
title: "Synthetic Data Generation: Methods, Use Cases, and Tools"
description: Explore how synthetic data generation works, its enterprise testing use cases, benefits, risks, and how to evaluate data quality and choose the right tool.
---

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 Sep 28, 2026

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 39 min

 Table of contents:

Explore how synthetic data generation works, its enterprise testing use cases, benefits, risks, and how to evaluate data quality and choose the right tool. 

# Synthetic Data Generation for Testing and Enterprise Data Projects

 Enterprise testing depends on data that is realistic enough to reproduce business conditions, but safe and accessible enough to use outside production. Obtaining that data is rarely straightforward. Production records may contain sensitive information, full system copies can be expensive, and manually assembled datasets often fail to represent the variety of conditions found in real operations. 

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Synthetic data generation offers another approach. Instead of copying existing records directly, it creates new data designed to reproduce selected characteristics of a source dataset or follow predefined rules. Organizations can use the resulting datasets for software testing, development, demonstrations, [migration](https://migravion.com/solutions/data-migration) rehearsals, [integration](https://migravion.com/solutions/data-integration) validation, and other non-production activities.

Synthetic data is not automatically realistic, private, or suitable for every test. Its value depends on the generation method, the [quality of the source data](https://migravion.com/blog/how-to-measure-data-quality), and how thoroughly the output is validated against statistical, technical, and business requirements.

## What Is Synthetic Data Generation?

Synthetic data generation is the process of creating artificial records that represent the structure, patterns, or expected behavior of real data. The generated records do not describe actual customers, employees, transactions, materials, or other real-world entities.

For structured enterprise data, synthetic generation typically starts with one of two inputs:

- **Existing source data:** A statistical or machine-learning model analyzes the dataset and generates new records with similar distributions, correlations, and relationships between fields.
- **Defined rules and constraints:** The generator creates values according to specified formats, permitted ranges, dependencies, and business conditions.

These approaches can also be combined. A dataset might reproduce the statistical distribution of production data, while applying additional rules to create specific testing conditions.

Synthetic data can take many forms, including images, text, sensor readings, and simulated environments. In enterprise data projects, however, it usually means structured tabular data, such as [customer attributes](https://migravion.com/blog/customer-master-data-management), [product records](https://migravion.com/blog/product-master-data-management), orders, financial values, or system configuration data.

A synthetic customer dataset, for example, might preserve the approximate distribution of countries, customer groups, payment terms, and order volumes found in a source dataset. The generated customer names and individual records would be new, but the overall dataset could retain patterns relevant to testing.

## How Does Synthetic Data Generation Work?

The exact process depends on the technology used, but model-based generation of structured data generally follows four stages. Together, they turn an existing dataset into new records that reproduce selected characteristics of the source, without directly copying it.

### Stage #1: Analyze the source dataset

The process begins with an analysis of the source data. The system examines its schema, field types, value ranges, category frequencies, missing-value patterns, and other structural characteristics.

It also looks for relationships between columns. For example, particular payment terms may occur more frequently in certain countries, or specific document types may commonly appear with particular classifications. These patterns provide the foundation for generating records that resemble the source dataset as a whole.

The analysis can only reflect the information contained in the source table. It does not automatically identify business rules, application configuration, or dependencies stored in other datasets.

### Stage #2: Train the synthetic data model

The system uses the results of the analysis to train a model that represents statistical characteristics of the source data. The model learns how individual fields behave and how their values relate to one another.

This does not mean that the model understands the business meaning of each field. It may learn that two values frequently occur together, without knowing why the relationship exists or whether other application rules restrict that combination. Therefore, the quality of the model depends heavily on the volume, consistency, and representativeness of the source dataset.

The different technical approaches that can be used at this stage are discussed in the next section.

### Stage #3: Generate and format new records

Once trained, the model generates the requested number of synthetic records. These records follow the expected structure and may preserve characteristics, such as value distributions, correlations, category frequencies, common combinations, and missing-value patterns.

The output should contain newly generated combinations, rather than direct copies of the source rows. It must also retain compatible field names, data types, formats, and other structural properties, so that it can be passed to the intended file, database, application, or subsequent processing step.

The required number of records depends on the intended use. A demonstration may need only a compact dataset, while performance or volume testing may require many more records. Increasing the number of generated rows, however, does not necessarily improve coverage of rare scenarios that were poorly represented in the source.

### Stage #4: Evaluate the generated dataset

Before the synthetic data is used, the output should be reviewed to confirm that the expected number of records and columns was created, the field structures remain compatible, and no obvious generation errors are present.

The review should also determine whether the generated data is suitable for its intended purpose. A dataset may reproduce the statistical properties of its source, but still contain invalid business combinations, omit important edge cases, or reproduce sensitive values unintentionally. Therefore, synthetic data should be treated as candidate test data, until it has passed the necessary quality, privacy, and business checks.

## Main Synthetic Data Generation Methods

Synthetic data can be created by using approaches that range from simple random-value generation to models trained on existing datasets. These methods differ in the realism they can achieve, the control they provide over individual values and relationships, and the effort required to configure and validate them. Understanding the main options helps teams select an approach that matches the complexity of their data and the requirements of the intended test or enterprise project.

The most common synthetic data generation methods include:

- **Rule-based generation:** Values are created according to predefined formats, ranges, lists, and dependencies. This approach offers strong control and works well when requirements are explicit. But creating and maintaining extensive rules may require considerable effort.
- **Random generation:** Values are selected randomly within permitted ranges or categories. This is simple and useful for basic volume or format testing, but it usually fails to reproduce realistic distributions and relationships, unless additional logic is applied.
- **Statistical generation:** A model analyzes characteristics of an existing dataset and generates new records with similar statistical properties. This can produce more representative data than basic random generation, although the results still require checks for invalid combinations and privacy risks.
- **Machine-learning-based generation:** Generative models learn more complex relationships within source data. These methods can improve fidelity for datasets with many interdependent attributes, but they require sufficient, consistent training data and appropriate evaluation.
- **Hybrid generation:** Statistical or machine-learning generation is combined with explicit constraints, reference values, or selected real data. A hybrid method can balance realism with control, but it must be designed carefully to avoid introducing inconsistent or sensitive records.

The most appropriate method depends on the intended use. A dataset for interface testing may only need correct formats and mandatory fields. [Migration validation](https://migravion.com/solutions/data-quality/data-migration-testing) may require realistic source patterns and error conditions. Performance testing may prioritize volume and distribution, while process testing may require logically consistent relationships across several business objects.

## Synthetic Data vs. Masking, Anonymization, and Subsetting

Synthetic data generation is one of several techniques used to prepare data for testing, development, analytics, and other non-production activities. Although synthetic generation, masking, anonymization, and subsetting can all reduce reliance on unrestricted production copies, they work in fundamentally different ways and offer different levels of realism, privacy protection, and connection to the original records.

The table below compares these techniques and shows where each is typically applied.

| **Technique** | **What it does** | **Connection to original records** | **Typical use** |
| --- | --- | --- | --- |
| Synthetic data generation | Creates new records based on patterns, models, or rules | Generated records are not intended to be direct copies | Testing, development, demonstrations, simulation, and dataset expansion |
| Data masking | Replaces or obscures sensitive values in existing records | The underlying records and much of their structure remain | Using production-derived data with reduced exposure |
| Anonymization | Transforms data to prevent records from being linked to identifiable individuals | Usually retains information derived from real records | Research, analytics, data sharing, and testing |
| Subsetting | Selects a smaller portion of an existing dataset | Selected records remain production-derived, unless additionally transformed | Reducing refresh time, storage use, and test data volume |
| Manual test data creation | Produces individual records for defined scenarios | Usually has no direct connection to production records | Unit tests, demonstrations, and narrowly defined test cases |

These methods are complementary, rather than mutually exclusive. An organization might subset production data, mask sensitive fields, and add synthetic records that represent conditions missing from the source. Another project might use fully synthetic data for early development and controlled production-derived data for final integration testing.

The decision should be based on the test objective, data sensitivity, required realism, and acceptable residual risk.

## Common Enterprise Use Cases

Synthetic data can support enterprise activities whenever suitable production data is unavailable, restricted, insufficient, or impractical to use. However, different scenarios require different levels of statistical similarity, business validity, data volume, and coverage of unusual conditions.

The following use cases illustrate where synthetic data generation can provide the greatest value and what teams should consider when applying it:

- **Software testing:** Development and QA teams can use synthetic data to test application functions, without waiting for production extracts. The data must include the formats, value combinations, and conditions required by the test cases.
- **Integration testing:** Synthetic records can be passed through interfaces, APIs, [transformations](https://migravion.com/solutions/data-maintenance/data-transformation), and target systems to verify [mappings](https://migravion.com/blog/data-mapping-automation) and processing logic. This is particularly valuable when access to one of the connected production systems is limited.
- [**Migration testing**](https://migravion.com/blog/data-migration-testing-guide)**:** Teams can generate records that resemble source data and use them to test [extraction](https://migravion.com/solutions/data-maintenance/data-extraction), [mapping](https://migravion.com/solutions/data-maintenance/visual-data-mapping), transformation, [validation](https://migravion.com/solutions/data-quality/data-validation), [loading](https://migravion.com/solutions/data-maintenance/mass-data-upload-sap), and [reconciliation logic](https://migravion.com/blog/enterprise-data-reconciliation-automation). Synthetic data can help identify technical problems early in the process, although production-derived samples are still important for confirming real migration behavior.
- **Data quality rule testing:** Generated datasets can be used to determine whether validation rules accept compliant records and reject invalid ones. Model-based generation may need to be supplemented with negative cases that are deliberately created, because statistically generated data does not necessarily contain every required error condition.
- **Development environments:** Synthetic datasets give developers data with which to build and troubleshoot features, without routinely exposing production records. Smaller datasets may be sufficient for functional work, while realistic volumes may be needed to identify performance issues.
- **Product demonstrations and training:** Demonstration environments can be populated with credible data that does not disclose actual customer, supplier, employee, or financial information. The records should still be reviewed to avoid accidental reproduction of sensitive or inappropriate values.
- **Performance and volume testing:** Synthetic generation can increase the number of records that are available to test system capacity, batch processing, and query performance. Simply multiplying records is not always sufficient: distributions and relationships may affect how the system behaves under load.
- **Edge-case testing:** Synthetic records can represent rare values, unusual combinations, boundary conditions, and exceptions that are difficult to find in production. Explicit rules or targeted generation may be required, because a model trained on historical data tends to reproduce existing patterns, rather than invent underrepresented scenarios.

## Benefits of Synthetic Data Generation

Synthetic data generation gives organizations greater control over the availability, volume, and composition of datasets used outside production. Its value extends beyond privacy: it can help teams begin work earlier, create data at the scale required by different tests, and reduce dependence on time-consuming production extracts.

The principal benefits include:

- **Reduced dependence on production copies:** Teams can prepare development or test data, without repeatedly duplicating complete production datasets. This can shorten preparation cycles and reduce competition for access to production-derived data.
- **Lower exposure of real business records:** Newly generated records can reduce the need to distribute actual personal or commercially sensitive values. Nevertheless, synthetic generation requires privacy assessment, because a model can occasionally reproduce or closely resemble its source data.
- **Flexible dataset sizes:** Organizations can create a smaller dataset for targeted functional testing or a larger one for volume and performance tests. The generated volume should reflect the test objective, rather than an arbitrary record count.
- **More accessible test data:** Synthetic datasets can be made available when production data is restricted, incomplete, or not yet available. This supports earlier development and testing, especially in new implementations.
- **Representative patterns:** Model-based generation can retain distributions and cross-field relationships that basic random data would miss. These patterns can make test conditions more comparable to real system behavior.
- **Support for uncommon scenarios:** Targeted generation can add boundary conditions and exceptions that are scarce in historical data. This benefit usually requires deliberate control, rather than relying only on patterns learned from the source.

These advantages are strongest when synthetic generation is part of a broader [test data strategy](https://migravion.com/solutions/master-data-management/sap-test-data-management). It does not eliminate the need for masking, subsetting, manually defined test cases, or controlled testing with representative production-derived data.

## Limitations and Risks

Synthetic data can closely resemble real data, but still be unsuitable for a particular test or enterprise process. Its reliability depends on different factors, such as the quality and representativeness of the source dataset, the capabilities of the generation method, and the validation applied to the output.

Before using synthetic data in development, testing, or demonstrations, teams should account for the following limitations and risks:

- **Poor source data produces poor models:** Inconsistent categories, outdated records, missing values, and source-system errors can be learned and reproduced. Therefore, source data assessment should precede generation.
- **Small samples may not be representative:** A limited dataset may not contain enough examples for the model to learn meaningful distributions or dependencies. Rare categories and uncommon relationships are especially likely to be lost.
- **Statistical fidelity is not business validity:** A model may generate values that match overall patterns, but violate process, configuration, or application rules. Domain-specific validation remains necessary.
- **Rare cases can disappear:** Generation models usually reproduce prominent patterns more reliably than exceptional ones. Therefore, the dataset may perform well statistically, but provide inadequate coverage for negative and boundary testing.
- **Sensitive values may be reproduced:** Synthetic generation reduces direct reliance on production records, but by itself does not prove that the output is anonymous. Teams should check for exact matches, unusually close records, and other indicators of possible disclosure.
- **Relationships may be limited:** A model that processes a single table can learn relationships between its columns, but cannot automatically preserve dependencies across customers, orders, products, and other separate tables.
- **Generated data can create false confidence:** Passing tests against synthetic records does not prove that a solution will behave correctly with production data. Important source system irregularities may not be represented.

These limitations do not make synthetic data unsuitable. They show why it should be treated as one component of a controlled testing approach, rather than a universal replacement for real data.

## How to Evaluate Synthetic Data Quality

Synthetic data should be evaluated against its intended purpose, rather than judged only by how realistic it appears. A dataset suitable for a demonstration may not provide the accuracy, coverage, or business validity needed for migration or integration testing.

A practical evaluation should consider several complementary dimensions:

- **Structural validity:** Confirm that the generated dataset contains the expected columns, data types, formats, field lengths, null behavior, and identifier patterns. These checks identify basic incompatibilities that could prevent the data from being processed by a target file, interface, database, or enterprise application. Structural validity is a minimum requirement, but it does not prove that the values themselves are meaningful.
- **Statistical similarity:** Compare the generated and source datasets across relevant measures, such as category frequencies, numeric ranges, distributions, averages, percentiles, missing-value rates, and correlations. The goal is not necessarily to reproduce every source statistic exactly. Instead, the generated data should preserve the characteristics that could influence the intended test — for example, transaction-value distributions in a performance scenario or category proportions in a validation workflow.
- **Cross-field consistency:** Examine whether related values form plausible combinations. Individual fields may look realistic, while their combinations do not: a document date may occur after a completion date, a country may be paired with an inappropriate regional code, or a record category may conflict with another attribute. Where several tables are involved, relationships between identifiers and dependent records must be assessed separately, especially if the generation method processes only one table at a time.
- **Business-rule validity:** Validate generated records against applicable reference data, configuration, permitted-value lists, mandatory-field rules, calculations, and process constraints. A generation model can learn recurring patterns from source data, without understanding why those patterns exist. This distinction is particularly important in SAP environments, where statistically credible values may still conflict with organizational structures, object-specific rules, or target system configuration.
- **Privacy protection:** Check whether the output contains exact copies or unusually close reconstructions of source records, particularly for rare categories, outliers, and sensitive fields. The review should consider both individual values and combinations that could identify a person or organization. Synthetic generation can reduce reliance on production data, but privacy protection should be demonstrated through appropriate checks, rather than assumed from the word “synthetic.”
- **Scenario and edge-case coverage:** Determine whether the dataset represents the conditions that the planned tests need to exercise. These may include common transactions, boundary values, missing mandatory fields, invalid formats, rare categories, rejection scenarios, and unusually large or small values. Because model-based generation tends to reproduce dominant source patterns, important exceptions may need to be introduced deliberately.
- **Downstream utility:** Process the generated data through the actual mappings, transformations, validations, interfaces, or application functions it is intended to support. This reveals whether the dataset is useful in practice, rather than merely similar to the source on paper. Useful measures might include acceptance and rejection rates, transformation outcomes, processing times, rule coverage, and the number of defects exposed.
- **Repeatability and variation:** Generate and evaluate more than one dataset where the data will support recurring or high-impact testing. Different runs should provide sufficient variation, without producing unstable quality or substantially different business behavior. Repeatability is especially important when synthetic data is incorporated into automated test or development workflows.

No single metric can establish synthetic [data quality](https://migravion.com/solutions/data-quality). A reliable assessment combines structural, statistical, business, privacy, and task-specific checks, with acceptance criteria defined according to the intended use. The stricter the operational or compliance consequences of an invalid record, the more comprehensive that evaluation should be.

## Synthetic Data in SAP and Enterprise Data Projects

SAP and other enterprise platforms present particular challenges for synthetic data generation. Their records are governed by configuration, reference data, organizational structures, dependencies, and application-level logic that may not be visible in a standalone table.

Synthetic data can still provide considerable value in these environments. It can help teams test:

- Field mappings between source and target structures
- Transformation and enrichment logic
- Mandatory-field and format validations
- Value conversions and lookup rules
- Rejection and exception-handling processes
- File and interface processing
- Data-volume behavior
- Reconciliation and reporting logic

For example, a migration team could generate source-like records and process them through its mappings before representative production data becomes available. The results could reveal truncated fields, failed conversions, incorrect default values, or validation gaps.

However, a synthetic row that resembles material, customer, supplier, or [transactional data](https://migravion.com/blog/sap-master-data-and-transactional-data) does not automatically constitute a valid SAP record. Its values may need to correspond to company codes, plants, sales organizations, units of measure, account groups, and other configured elements. Relationships with records held in other tables may also be essential.

Therefore, synthetic data is particularly useful for testing individual datasets and data-processing logic. End-to-end SAP process testing may require additional business rule validation, reference data, and relationships across multiple objects.

## What to Look for in a Synthetic Data Generation Tool

A synthetic data generation tool should be evaluated by how realistic its output appears, as well as by how well it fits the organization’s data landscape, testing processes, privacy requirements, and technical operating model. A tool that performs well with a clean standalone table may be less effective when data spans multiple systems, depends on complex business rules, or must be delivered through a controlled enterprise workflow.

Therefore, the evaluation should cover the complete path from source data access and model configuration to validation, delivery, and ongoing use:

- **Generation method:** Teams should understand whether the tool uses random values, predefined rules, statistical models, machine learning, or a combination of approaches. The method affects what the output can preserve and how much configuration it requires. Rule-based generation offers direct control over known constraints, while model-based generation can reproduce patterns that would be difficult to define manually. A tool should explain what its model learns and avoid treating all forms of generation as equivalent.
- **Supported data structures:** The tool should support the field types, formats, and dataset structures relevant to the intended use. In addition to standard numeric, categorical, date, text, and identifier fields, teams may need support for missing values, high-cardinality attributes, nested structures, time-series data, or composite keys. It is also important to establish whether the tool generates one table at a time or can model relationships across an entire relational dataset.
- **Source and target connectivity:** Direct access to required databases, files, enterprise applications, and cloud platforms can reduce manual exports and prevent synthetic generation from becoming an isolated activity. The tool should also be able to deliver generated data in a form that downstream systems can consume. In SAP-centric environments, connectivity must be considered alongside the interfaces and loading methods approved for the relevant business objects and systems.
- **Dataset-size control and scalability:** Users should be able to specify the number of records required for each scenario, from compact demonstration datasets to high-volume performance tests. The tool should maintain acceptable output quality as volume increases and provide predictable processing times for large sources. Teams should also determine whether increasing the number of records produces meaningful data variation or simply repeats a limited set of learned patterns.
- **Relationship handling:** A capable tool should preserve important relationships between fields, such as correlations, conditional dependencies, and common value combinations. Where testing involves multiple tables, it may also need to maintain primary and foreign keys, parent-child structures, and consistent references between business objects. Teams should verify whether such relationships are learned automatically, configured manually, or not supported.
- **Generation controls:** Different projects require varying degrees of control over the output. Useful capabilities may include selecting fields for generation, excluding sensitive or irrelevant columns, defining permitted ranges, fixing selected values, specifying category proportions, or deliberately increasing rare scenarios. Without these controls, teams may receive statistically plausible data that does not cover the conditions their tests need to exercise.
- **Validation capabilities:** The tool should make it possible to assess structural compatibility, statistical similarity, cross-field consistency, and source-record duplication. Automated profiles, comparison reports, and configurable acceptance thresholds can make quality checks more repeatable. Where such features are absent, the generation workflow should allow external validation steps to be applied before the data reaches its target.
- **Privacy controls:** Synthetic generation should not be treated as proof that data is anonymous. The tool should help identify exact matches, near-duplicates, rare-value disclosure, and other signs that generated records may reveal information from the source. Organizations handling sensitive data may also need configurable privacy techniques, access controls, or evidence that supports their internal risk and compliance reviews.
- **Workflow integration:** Synthetic data becomes more useful when generation can be combined with source extraction, field mapping, transformation, validation, loading, scheduling, and exception handling. Integration with the wider data workflow reduces manual handoffs and makes it easier to reproduce the process when systems, schemas, or testing requirements change. API, command-line, or orchestration support may also be important for automated testing and DevOps scenarios.
- **Monitoring and traceability:** Teams should be able to identify which source dataset, configuration, model version, and execution produced a particular output. Execution logs, status reporting, error details, and retained configuration improve troubleshooting and support controlled reuse. Traceability becomes especially important when synthetic data is used across several environments or generated regularly as part of automated testing.
- **Transparency and extensibility:** Users should understand which settings are configurable, which behaviors are predefined, and where the tool’s limitations begin. Support for custom logic, scripts, validation rules, or external libraries can be valuable, when standard generation settings do not address a specialized requirement. At the same time, extensibility should not make routine generation dependent on undocumented custom code that only a small number of specialists can maintain.
- **Deployment and security model:** The tool’s deployment options should align with the requirements that govern where sensitive source data can be processed. Organizations may need on-premises, private-cloud, or hybrid deployment, along with encryption, role-based access, and separation between development and production environments. The assessment should consider where generated data is stored, as well as where the source is analyzed and where the model itself resides.

No tool can determine data fitness without a clearly defined purpose. Selection should begin with the datasets, relationships, privacy conditions, and test scenarios the organization needs to support, followed by a practical evaluation using representative source data. This makes it possible to assess whether the tool can produce, validate, and deliver dependable synthetic data within existing enterprise processes.

## How Migravion Supports Synthetic Data Generation

Migravion incorporates synthetic data generation into configurable enterprise data workflows. Its Synthetic Data Generation plugin uses the Synthetic Data Vault Python library to analyze an existing source dataset and produce new tabular records with similar characteristics.

The plugin can identify patterns, such as value distributions, category frequencies, missing-value behavior, and relationships between fields. During execution, it generates the specified number of new records, rather than directly copying the source rows. Depending on the source dataset, the output may preserve relevant statistical properties and common value combinations.

A typical workflow contains three components:

1. A source plugin supplies the original dataset.
2. The Synthetic Data Generation plugin analyzes the data and creates new records.
3. A target plugin stores or processes the generated dataset.

For example, data can be [imported from Excel](https://migravion.com/blog/excel-to-sap-automation), processed by the synthetic data plugin, and exported to a new Excel file. SQL-based sources and targets can also be incorporated through compatible Migravion plugins. Field mappings connect the source schema, generation step, and target structure within the project flow.

This approach enables teams to use synthetic data alongside other extraction, transformation, validation, and loading activities. Relevant scenarios include:

- Preparing data for development and demonstrations
- Testing migration mappings and transformations
- Exercising validation and exception-handling logic
- Increasing or reducing dataset volume
- Avoiding direct use of sensitive source records
- Supplying structured data to compatible downstream systems

The current plugin processes one source table at a time. Separate plugin instances or project flows are needed for additional tables, and the plugin does not automatically preserve relationships across them. It also does not guarantee that generated combinations comply with every business or SAP rule. Therefore, the output should be checked for structural validity, statistical suitability, source-record matches, and fitness for the intended test.

By incorporating model-based synthetic generation into broader data workflows, Migravion helps teams move from isolated dataset creation to a controlled process for preparing, processing, and [delivering enterprise test data](https://migravion.com/blog/sap-test-data-management-tools-guide).

## Conclusion

Synthetic data generation can make realistic data more accessible for software testing, development, demonstrations, migration validation, and other enterprise data projects. Unlike masking or subsetting, it creates new records, potentially preserving useful characteristics of the source, without directly copying its contents.

Yet, its effectiveness depends on more than realistic-looking values. Source quality, statistical fidelity, business validity, privacy risk, cross-record relationships, and test coverage all influence whether a generated dataset is fit for purpose. Synthetic data works best as part of a broader strategy that combines appropriate generation methods with validation, masking, subsetting, and carefully controlled production-derived data.

Migravion brings synthetic data generation into visual data workflows that connect source data, model-based generation, mappings, transformations, and compatible targets. To explore how it can support your testing or enterprise data project, request a Migravion demo.

## FAQ

- ### What is synthetic data generation?
  
  Synthetic data generation is the creation of artificial records that reproduce selected structures, patterns, or relationships found in real data. The resulting records are newly generated, rather than intended to represent actual people, organizations, products, or transactions.
- ### How is synthetic data generated?
  
  Synthetic data can be produced using predefined rules, random-value generators, statistical techniques, machine learning models, or combinations of these methods. Model-based tools analyze source data to learn distributions and relationships before generating new records with similar characteristics.
- ### Is synthetic data the same as masked data?
  
  No. Masking modifies sensitive values in existing records, while retaining much of the original dataset. Synthetic generation creates new records based on learned patterns or defined rules. Both methods can reduce exposure of sensitive data, but each has different privacy, realism, and validation requirements.
- ### Is synthetic data safe to use for software testing?
  
  Synthetic data can reduce the need to expose production records, but it should not be considered automatically private or safe. Generated data should be checked for matches with source records, disclosure of rare values, structural errors, invalid combinations, and suitability for the environment in which it will be used.
- ### Can synthetic data be used in SAP migration and integration testing?
  
  Yes. Synthetic data can help test mappings, transformations, field validations, interfaces, exception handling, and processing volumes. However, statistically representative values may not comply with SAP configuration or business rules, and single-table generation may not preserve dependencies across related SAP objects. Therefore, additional validation and representative production-derived testing may be required.

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```json
{
  "@context" : "https://schema.org",
  "@type" : "WebSite",
  "name" : "Migravion",
  "url" : "https://migravion.com/"
}
```

```json
{
  "@context" : "https://schema.org",
  "@type" : "LocalBusiness",
  "address" : {
    "@type" : "PostalAddress",
    "addressCountry" : "US",
    "addressLocality" : "Miami",
    "addressRegion" : "Florida",
    "postalCode" : "33131",
    "streetAddress" : "801 Brickell Avenue, Suite 1970"
  },
  "image" : "https://migravion.com/hubfs/Migravion-logo.svg",
  "name" : "Migravion",
  "telephone" : "+17864645772"
}
```