Making the Most of Synthetic Data

Project managers, data scientists, data engineers, developers, and testers can all attest to the importance of data in the AI/ML modeling and software development life cycle. At each stage of development, data helps to ensure that the models and software are functioning as expected and meet the needs of the business. However, for a variety of reasons, most notably privacy protection, it is often necessary to generate synthetic data that mimics production data. This blog discusses some of the ways in which synthetic data can be generated and used in conjunction with production SQL data.

Synthetic data for privacy. Synthetic data can help to protect a population's privacy by providing a realistic representation of the data that is not easily guessed or guessed with a high degree of accuracy. Traditional data masking is often insufficient to protect privacy because it does not actually change the data, but simply hides select personally identifiable information (PII), often leaving indirect identifiers intact. Synthetic data can provide a more effective way to protect the privacy of individuals while still allowing for the development and testing of machine learning models and software.

Higher data quality than other anonymization techniques. In addition to protecting the privacy of individuals, synthetic data also provides higher quality data sets than other techniques to anonymize the data. Permutation, randomization, and generalization are all methods of anonymizing datasets, but they all have the potential to "destroy" the data, rendering it unusable. By utilizing synthetic data instead of altering the production data, data scientists, developers, and testers can maximize their data's usefulness while still protecting user privacy.

Beyond row and column generation. Traditionally, synthetic data was generated using row and column generators. Row generators create synthetic data that mimics the distribution of values in the source data. Column generators create synthetic data that mimics the distribution of values in the source data for a specific column.

One limitation of traditional row and column data generation is that it is difficult to generate data that spans multiple tables in a relational database. In order to generate data for multiple tables, synthetic data generation must be configured to preserve the relationships between the tables. Another limitation of traditional synthetic data generation is that it is difficult to generate data that is representative of the entire population. Synthetic data can only be generated for a subset of the population, and it is often necessary to very carefully sample the source data in order to generate a representative synthetic dataset.

Basing on production data. In order to create the best learning models and the most accurate testing scenarios, it is important to base synthetic data on actual production data sets. When based on production data sets, synthetic data can help to improve the accuracy of models by providing a more realistic representation of the data. In addition, synthetic data based on production data sets can help to improve the accuracy of testing scenarios by providing a more realistic representation of the data that is being tested.

The field of synthetic data generation is gaining popularity and there are still opportunities for improvement. By basing synthetic data on production data sets, it is possible to improve the accuracy of the models and testing scenarios that use this data. Additionally, by developing proprietary methods for subsetting very large data sets, synthetic data can be generated in a more cost effective manner. This will ensure that synthetic data accurately reflects production data, while also providing a practical solution for development and testing teams.