Synthetic Data Generation Tools.

Create fake, realistic data, at scale, to ensure privacy compliance.

SDG features bar
header mobile-1
Book a Demo

Trusted by the world’s largest enterprises

american-express
vodafone
global-payments-1
sky
farm credit
verizon
DirecTV-Logo-2
Rogers
BNG bank
sunlife

Generate synthetic data
for any scenario

01

When there’s not enough real data

Create realistic synthetic data to complement production data.

SDG1-1

Build fake data that looks and behaves like real data by preserving formats. "Perfect customers" can be cloned into thousands of lookalikes with predetermined characteristics. Cloned entities replace sensitive data, such as names, contact information, and payment details. Hybrid datasets serve operational and analytical use cases by combining masked production data with synthetic data. And synthetic data can be used to fill gaps in production datasets, such as replacing missing values, or simulating future data.

SDG1-1
02

When real data is off limits

Use fake data when you can't use the real thing.

SDG2-1

When internal data governance policies prevent access to production data, you can synthesize 100% of your data based on predefined rules. By applying rules for each table/column, such as date ranges and gender balances, you could build a fake dataset that eerily resembles production data but is entirely artificial.

For example, you could specify that the synthetic data needs to contain all the same regions as the production dataset while ensuring that the data points from each region are in the same ratio as the production dataset. You could also correct gender bias, by giving the fake dataset a 50-50 gender balance (instead of the actual 70-30 ratio).

SDG2-1
03

When there's no real data

Develop dummy data for upcoming product releases and upgrades.

SDG3

Sometimes you need test data to validate new features or applications for which no production data exists. Synthetic test data can be tailored to your needs. For instance, you could generate realistic customer datasets for a loyalty program, including millions of simulated customer profiles, complete with purchase histories, demographics, and more.

Or you could test applications with updated database schemas without having to manually fabricate new datasets in a testing environment – enabling you to quickly create and run test scenarios, saving time and resources.

SDG3
Data masking tools - Gartner Peer Insights

K2view gets top scores in Gartner Peer Insights

Data masking tools - Gartner Peer Insights

“High-end security for your sensitive information..."
“Innovative, fast and also scalable...”
“Excellent dynamic and static data masking...”

How we do it

SDGproducts
SDGproducts

K2view developed the entity-based data masking technology that integrates fragmented data from disparate systems and organizes it by business entities (e.g., customers, orders, devices, etc.)

Our intelligent synthetic data generator uses SQL and business rules to create realistic artificial data, and to ascertain the relationships between elements – for example, primary and secondary keys between tables in complex models. 

This unique approach, in conjunction with data masking tools, and/or data tokenization tools, enhances your test data management tools, for a wide range of operational and analytical use cases in financial serviceshealthcare, telco and media, and more.

Use cases

icons web__Golden Record-353

Testing

Collaboration-1

Simulation

business continuity-2

Analytics

Entity-based synthetic data
generation tools and features

Data integration for data masking

Self-service portal

Generate synthetic data in minutes, based on user-defined parameters.

Scalable data masking architecture

Relational consistency

Maintain the referential integrity of parent/child/sibling relationships across domains and applications.

blue icons-web__respond quickly-2

Access control

Manage access based on roles and privileges through a multi-layer security portal.

Data Pipelining-4-Feb-16-2023-01-23-55-8812-PM

Web service APIs

Enable automated DevOps and integrate with any automation framework or CI/CD pipeline.

C360 pillar pg-1

Time machine

Roll back synthetic data subsets to previous versions, on demand.

File exchange

Format preservation

Maintain the same format and structure of the source data.

icons web_Fabric-AccessibleData-1

Multi-source connectivity

Create synthetic data based on production data and then inject it into any target database.

blue_TDM-blue-Integrity-4

Data catalog

Map and label schema relationships with graphical display and auto-discovery features.

icons web_DataPlatform_fuelDecission

Suite of tools

Integrate with test data management and data masking solutions.

See our synthetic data generation tools in action.

Book a Demo