Intelligent Test Data Management



Digital Transformation requires Intelligent Test Data Management

Banks and Financial institutions need to deliver better quality software to the business faster and at less cost. No bank can afford the time, the cost or the risk of employing an army of manual testers. They need to be able to respond to changing requirements by provisioning fit for purpose test data to the right place at the right time to accelerate and improve test cycles.

The importance of Test Data Management (TDM), or having the right data delivered to the right place, at the right time, for testing purposes is overlooked. Up to now, testers usually spent up to 50% of their time looking for data, and as much as 20% of the total software development lifecycle is spent waiting for it. This leads to testing bottlenecks which make true agility and Continuous Delivery impossible. Imagine if you need to engage in three week “sprints”, but have to spend four weeks preparing the data for the sprint.

Production data cannot provide the coverage needed to fully test a system. It only provides as much as 20% functional coverage, focusing testing only on the ‘happy paths’, which means that defects will invariably make it into production, leading to rework, critical delays, increased costs, and potential project failure. In addition, masking or subsetting production data or creating data manually from scratch can be a time-consuming task, also affected by the inconsistent storage of data within different versions of spreadsheets. Test Data Management can no longer be no longer a back-office function. It is a critical business enabler for enterprise agility, security, and efficiency and Continuous Testing success.


A requirements-driven approach to Test Data Management

Instead of thinking of data on a test case by test case basis, organizations should think of data in terms of design decisions – in terms of the requirements themselves, designing test cases with the test data linked directly to them. Aligning test data to requirements ensures that it is ‘fit for purpose’, while the ability to provision it quickly to test teams means that they can quickly respond to the changing demands of the business.

Testers need access to ‘fit for purpose’ data that can cover 100% of test cases, delivered to the right place, at the right time.
Generate ‘fit for purpose’ quality data for Intelligent Continuous Testing


Validata offers a complete, business-model, end-to-end test data management solution driven by requirements, allowing banks to shift left testing, eliminate the data wait, reduce data-related defects and fix the ‘Data Gap’ in QA and DevOps.

Through a powerful synthetic data generation engine, it provides testers with data before testing starts; realistic data tailored to their specific testing and development needs.

It can rapidly generate large sets of synthetic test data to eliminate the risk of data breach by creating production-like data but without the sensitive content.
These test data sets can be shared with outsourced data testers or uploaded for application testing in the cloud, as safely and easily as when used on-premise.

It also enhances existing subsets of production data with rich, sophisticated sets of synthetic data, reducing infrastructure by covering all combinations in the optimal minimum set of test data.

With equivalence class testing, you can derive the minimum number of test cases to expose the most noticeable defects.
AI and Machine Learning
at the core

Leveraging AI and machine learning techniques, it enhances its data self-service ability by intelligently recommending data sets that might be of interest and by suggesting next-best-actions.
Deliver projects with secure, compliant and quality data that can be versioned on-demand!

Testing using synthetic data avoids exposing personally identifiable information (PII) to non-production environments. However, many organisations choose a combination of existing data and synthetically generated data for their testing activities.

Validata helps you address data privacy and compliance issues, providing the ability to identify and irreversibly mask sensitive data and confidential information with realistic values while preserving referential integrity.
Key Features


Self-service data provisioning
Testers and Developers are now self-sufficient to provision their own test data sets across on-premise, cloud and hybrid environments.
Synthetic data
Create rich synthetic data from scratch, reducing the need to use production data.
Test Data Virtualisation
Create virtual copies of test data instantly, and with no storage overhead.
Data masking
Ensure data privacy and security and referential integrity for your testing.
Built for reusability
The model-based test data generation approach enables reuse of testing assets; test data is stored in the platform’s central repository fully versioned.
Benefits

Enables “shift left” practices and improves quality
Reduces the time and resources required to provide data ‘fit for purpose’ by 60%
Increases Developers’ & QA’s productivity by providing the test data early
Improves test coverage and reduces the costs of testing
Accelerates data agility and software delivery
Maintain compliance with all new regulations

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