Any information created artificially and accurately reflecting events or things in the real world is considered synthetic data. Algorithms generate synthetic data that is utilised in ML models, Relational Databases, and Unstructured/No SQL databases for various testing purposes.
In some cases, we are bound to duplicate and subset the production data values, which is further utilised by the programme under test to get test data for functional testing. This strategy is predicated on the notion that testing may use production data that is safe, accessible, and realistic — but not always recommended due to Data Privacy concerns.
What are the Advantages of Using Synthetic Data?
Due to the requirement to collect data from actual occurrences, it is now feasible to create Synthetic Data and build datasets considerably faster than a dataset that depends on actual events.
1. But unfortunately, real-world data collection might be hazardous.
Real-time production data is not allowed because of Data privacy and regulatory Concerns. However, synthetic data in non-production can replace radioactive production data.
For example, AI-generated synthetic data is mainly used to train ML algorithms. This is common in software testing and development. Simultaneously, the approach reduces Test Data Preparation time and Time-to-Market.
2. Privacy:
Privacy concerns have been addressed because they must be considered when sensitive data has to be processed or supplied to other parties for use. Producing synthetic data eliminates any traces of the original data’s identity, resulting in a new legitimate data collection that does not compromise privacy.
Privacy becomes a real-time issue because many countries do not allow customers’ data to go out. They imply laws and regulations like GDPR – General Data Protection Regulation or HIPAA – Health Insurance Portability and Accountability Act to protect the Customers’ data. That’s when these types of Data enable easy testing without the use of Production Data.
3. Lesser Overheads:
Furthermore, synthetic data removes the extra admin overheads involved with acquiring access to sensitive data. Even for internal purposes, businesses may require a lot of time to demonstrate the necessity for access to a given dataset. As a result, companies may acquire insights much faster using synthetic data.
Data extraction from any production system is a challenge and not easy. However, synthetic data allows you to generate millions of Data in just a few seconds.
4. Maximum Coverage across testing:
Negative testing, edge case testing, complete combinatorial testing, and testing for new applications for which no historical data is available to produce synthetic data and maximise the coverage across various testing areas.
5. Users can fully control Data:
The testers can control everything if the synthetic data is simulated. The users can control Data generation Frequency, Data Distribution, and Data Volume completely.
Conclusion:
After getting approvals, a specialist test data support team only provides production data for testers on-demand. Furthermore, this data must be appropriately disguised to adhere to data protection requirements. This data bridges all these issues and makes testing easy without dependency on the Production Data.
Authored by:
Abhigna Arcot
Senior Content Writer
