Key aspects to consider include data masking, data subsets, data generation, automation capabilities, compliance features, scalability, integration capabilities, and reporting.
Data masking
Data subsets
Data generation
Automation capabilities
Compliance features
Scalability
Integration capabilities
Reporting
Data masking ensures sensitive information is protected by masking or anonymizing it. Data subsets allow efficient use of storage by creating smaller, relevant datasets. Data generation helps in creating synthetic data for testing environments. Automation capabilities enhance efficiency by automating data refresh cycles and data provisioning. Compliance features are essential for meeting regulatory requirements such as GDPR and HIPAA.
Scalability is crucial to accommodate growing data needs. Integration with other tools and systems is important for seamless operation across platforms. Detailed reporting provides insights into the health and status of test data, enabling better decision-making. Each of these features plays a significant role in optimizing the Test Data Management process and ensuring high-quality, efficient testing practices.
Search for a product comparison in Test Data Management
Presales at a tech services company with 501-1,000 employees
MSP
Aug 6, 2019
Data privacy tools to protect the sensitive data, test data "golden copy" administration and monitoring module, natively supported DB, bookmarks feature for easily rollback to a point in time.
Presales Consultant for CA Southern Africa at Hyperion Holding Pty Ltd
Real User
Dec 3, 2021
1. Capability to discover data from data source and categorize them automatically + having the capability to add or define your own categorization
2. Capability for classifying the data in your data source in Prod and allowing the extraction of the subset of data meeting your testing requirement at the same time sanitizing the data to ensure Personal Identifiable Information is not compromised
3. What is my requirement for the data for bug fixing or getting missing data or getting data for new apps
E.g. Getting a valid Social Security number that is usable but has no actual entity to be identified to, Generating a Visa card dummy number that will pass visa validation but is not a valid card
4. Have the capability to deal with relational data and nonrelational data or flat-file based data - Generate or Obfuscate
5. Can my data mask relatable data across database or DB tables, e.g if 2 databases hold user records have a key to identify an entry that is common in both databases then use that key? Can I maintain referential integrity across all dB e.g If a user table has Jim as a name and he is obfuscated to Jack then all the fields that hold the name be obfuscated to Jack is a true test of Referential integrity?
6. Ease of use and how easy it can be scripted via Automation tools or call to API because most of the requirement for Test data is for Testing hence it would be nice to call API to meet certain rules for data requirement and provide that concise information to a tester.
Test Data Management is crucial for ensuring data accuracy and security in development environments. It helps manage data lifecycle and maintain high-quality data across all testing stages. With Test Data Management, organizations enhance their ability to efficiently manage test data while protecting sensitive information. This practice supports development and testing teams by offering secure, repeatable, and reliable data masks and subsets, enabling faster deployment cycles. Key tools in...
Key aspects to consider include data masking, data subsets, data generation, automation capabilities, compliance features, scalability, integration capabilities, and reporting.
Data masking ensures sensitive information is protected by masking or anonymizing it. Data subsets allow efficient use of storage by creating smaller, relevant datasets. Data generation helps in creating synthetic data for testing environments. Automation capabilities enhance efficiency by automating data refresh cycles and data provisioning. Compliance features are essential for meeting regulatory requirements such as GDPR and HIPAA.
Scalability is crucial to accommodate growing data needs. Integration with other tools and systems is important for seamless operation across platforms. Detailed reporting provides insights into the health and status of test data, enabling better decision-making. Each of these features plays a significant role in optimizing the Test Data Management process and ensuring high-quality, efficient testing practices.
Data privacy tools to protect the sensitive data, test data "golden copy" administration and monitoring module, natively supported DB, bookmarks feature for easily rollback to a point in time.
1. Capability to discover data from data source and categorize them automatically + having the capability to add or define your own categorization
2. Capability for classifying the data in your data source in Prod and allowing the extraction of the subset of data meeting your testing requirement at the same time sanitizing the data to ensure Personal Identifiable Information is not compromised
3. What is my requirement for the data for bug fixing or getting missing data or getting data for new apps
E.g. Getting a valid Social Security number that is usable but has no actual entity to be identified to, Generating a Visa card dummy number that will pass visa validation but is not a valid card
4. Have the capability to deal with relational data and nonrelational data or flat-file based data - Generate or Obfuscate
5. Can my data mask relatable data across database or DB tables, e.g if 2 databases hold user records have a key to identify an entry that is common in both databases then use that key? Can I maintain referential integrity across all dB e.g If a user table has Jim as a name and he is obfuscated to Jack then all the fields that hold the name be obfuscated to Jack is a true test of Referential integrity?
6. Ease of use and how easy it can be scripted via Automation tools or call to API because most of the requirement for Test data is for Testing hence it would be nice to call API to meet certain rules for data requirement and provide that concise information to a tester.
Covering all data sources including DH, streaming, and message based.
Data Integrity and Data privacy and Data Sync up across all multiple systems
Reduce data for copying (Virtual)
Test data selection and swift availability for all dev/test purposes.
More data Source and tools for managemet data
To improve their products, software development teams leverage many innovations: Agile, DevOps, big data, cloud, and mobile. Testing is part of this, and testing tool vendors. you may want to check out a Forrester report found here: www.ca.com/us/collateral/industry-analyst-report/agile-test-data-management-the-new-must-have.register.html
Copying speed and automation
Data security