In order to provide a flawless software or system, it is important to test the developed project with data. However, the new data protection and privacy regulations require system developers not to use real data as it might fall into malicious hands, make real users’ information available to undesired elements, and put user’s privacy and safety at risk. Therefore, synthetic data is generated for data testing. This article discusses ways of generating synthetic data and few challenges associated with it.
Before generating synthetic data it is important to first identify the type of synthetic data you need for testing. Broadly, synthetic data generation can be categorized into two labels. Both categories come with their share of pros and cons.
Synthetic Data Types
This type of data is completely generated using tool or software, and does not contain any real data. This means that this data does not belong to any real people, and it cannot be associated to anyone. The data entries, though, are completely according to the desired format, and all entries are fully available.
In this case, some of the data entries are authentic, whereas some entries are generated synthetically. While opting for this method, only those data entries are replaced with synthetic entries which are sensitive to privacy or safety. Partially synthetic data requires a high level dependency on the imputation model which provides a lesser model dependence. Picking this method means that some of the entries do not conform with regulations because some data fields belong to real dataset.
Synthetic Data Generation Strategies
1. Distribution Generated Numbers:
Using this strategy synthetic data is produced by observing real statistical distributions. Generative models can also be produced using this strategy.
2. Agent-based modeling
In this method, an exemplary data set is created according to the observed or desired behavior. On the basis of manually entered data, random data is generated as per the same model. The focus of this method is on interactions between agents, and their impact on the system.
3. Deep learning models
With the help of models like variationally autoencoder and generative adversarial network (GAN), synthetic data can be generated. These techniques improve data utility as they contribute more data to the models.
Challenges of Synthetic Data
Although synthetic data is popular way of data testing because it does not compromise data security of real people, however, there are certain limitations and baggage of using this method for data generation.
1. Missing Outliers
Synthetic data is only an imitation of real data. It is not the exact copy of real data, therefore, it is quite possible that the generated data might not comply with the required fields, and miss on some outliers that original data has.
2. Quality of generated data depends on the data source
The quality of synthetically generated data relies on the quality of the source data to a great deal. It is also dependent on data generation model. Poor quality input data results in poor quality synthetic data generation.
3. User don’t find it reliable
Synthetic data is an emerging trend which has yet to gain popularity because majority people find it as unreliable data. As the data is generated using some tool or strategy, a normal perception is that this data lacks attributes of real data, hence it is not viewed as valid or authentic data.
4. Requires more resources
Since the data repository is to be created from scratch, synthetic data generation requires more time, resources and energies. This puts more strain on available resources.
5. Output control is necessary
If the test data is generated using synthetic tool or strategy, it is important to compare the output data with the real dataset to ensure validity, reliability and quality of the produced data. A comparison with real data will help remove inconsistencies in synthetic data, and ensure data very similar to original data.
At GenRocket, our qualified and competent team can help you in picking the right test data management, strategies and tools for testing your software as well as complicated system. We have the expertise and resources to perform full-fledge testing for your systems and software. To know more about our services, get in touch with us today.