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.
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).
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.
“High-end security for your sensitive information..."
“Innovative, fast and also scalable...”
“Excellent dynamic and static data masking...”
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 services, healthcare, telco and media, and more.
Generate synthetic data in minutes, based on user-defined parameters.
Maintain the referential integrity of parent/child/sibling relationships across domains and applications.
Manage access based on roles and privileges through a multi-layer security portal.
Enable automated DevOps and integrate with any automation framework or CI/CD pipeline.
Roll back synthetic data subsets to previous versions, on demand.
Maintain the same format and structure of the source data.
Create synthetic data based on production data and then inject it into any target database.
Map and label schema relationships with graphical display and auto-discovery features.
Integrate with test data management and data masking solutions.
Get a live demo of the K2View platform to assess its fit for your use cases.
Experience the power and flexibility of the K2View platform with a 30-day trial.
Experience the power and flexibility of the K2View platform with a 30-day trial.