v1.0.0
Dataskope Suite · Module

Test Data Management

Masked & Synthetic Test Data

Realistic test data,
without the real risk.

Test Data Management gives development and QA teams the data they need: masked copies moved between environments, or realistic synthetic data generated on demand. Everything runs as reusable scenarios and schedules.

test-data-management / scenario: release-qa
● running
Scenarios
24
6 scheduled tonight
Environments
9
source · target
Last run
OK
no rollback needed
MapColumns mapped to generatorsdone
MaskCUSTOMER, PAYMENT maskeddone
Generate50,000 synthetic rowsrunning
LoadTarget: QA PostgreSQLqueued
Capabilities

Masked or synthetic.
Always on schedule.

Stop copying production data into test environments. Define how test data should be produced once, and run it whenever teams need it.

01

Scenarios and schedules

Group test cases into suites and run them on a schedule, so test data is ready when teams need it.

Test casesSuitesSchedules
02

Environments

Define source and target environments and group them to manage data across teams.

SourceTargetGroups
03

Masked data movement

Move masked data between environments with orchestrated data flows.

Data flowsMasking
04

Synthetic data generation

Generate dates, financial values, locations, numbers, personal data, text, and web data with rule-based, regex-based, and AI-assisted generators.

Rule-basedRegexAI
05

Column mapping

Use discovery results to map which column is produced by which generator.

DiscoveryMapping
06

Safe loading with rollback

Load generated data into Oracle, MSSQL, PostgreSQL, MySQL, DB2, SAP HANA, Databricks, and MongoDB. If a load fails, it is rolled back. Connection credentials are managed centrally.

RollbackCentral credentials
How it works

From definition to ready-to-use data.

Scenarios describe what data is needed. Orchestrated flows produce it and load it into the target environment.

1

Map

Map columns to masking rules or generators using discovery results.

DiscoveryMapping
2

Produce

Mask data from a source or generate synthetic data.

MaskingSynthetic
3

Load

Load into the target database, with rollback on failure.

TargetRollback
4

Report

Review run reports for every scenario.

Run reports
Use case · Development and QA

Give teams data that behaves like production.

Developers and testers need realistic data, but production copies carry personal data into places it should not be. Masked and synthetic data keep tests realistic and personal data out.

  • Masked copies between environments
  • Synthetic data for new or empty systems
  • Scheduled runs before each test cycle
  • Run reports for every scenario
test-data-management / scenario: release-qa
● running
Scenarios
24
6 scheduled tonight
Environments
9
source · target
Last run
OK
no rollback needed
MapColumns mapped to generatorsdone
MaskCUSTOMER, PAYMENT maskeddone
Generate50,000 synthetic rowsrunning
LoadTarget: QA PostgreSQLqueued
synthetic data preview
5 of 50,000
full_nameibancity
Deniz AksoyTR33 0006 1005 …İzmir
Elif KayaTR76 0001 0017 …Ankara
Can DemirTR19 0004 6007 …Bursa
Zeynep ŞahinTR58 0006 4000 …Antalya
Use case · Synthetic data

Generate the data you do not have yet.

New features often need data that does not exist in production. Synthetic data generators create realistic values and load them straight into the target database.

  • Generators for personal, financial, location, and web data
  • Regex and AI-assisted generation
  • Eight target database types
  • Automatic rollback when a load fails
FAQ

Questions & answers.

Which databases can receive synthetic data?
Oracle, MSSQL, PostgreSQL, MySQL, DB2, SAP HANA, Databricks, and MongoDB.
What happens if loading fails halfway?
The load is rolled back, so the target database is not left in a half-loaded state.
How are database credentials handled?
Connections are managed by a central connection service. Credentials are not hardcoded in scenarios.

See Test Data Management in action.

Book a demo and see masked and synthetic test data produced from a single scenario.

Book a demo

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