We at MOSTLY AI are big fans of open-source software. We are leveraging more than 90 open-source software packages for our synthetic data generator. It is safe to say that without open-source software libraries, it would have been impossible to get where we are today so quickly. We sometimes get asked by prospects why they should choose MOSTLY AI’s Synthetic Data Platform over freely available open-source solutions, like MIT’s Synthetic Data Vault, to synthesize data. This blogpost is meant to provide an answer to that.

Synthetic data quality: SDV versus MOSTLY AI

The answer is multifaceted, but the main point is the quality of the synthetic data you can generate. We pride ourselves on delivering synthetic data that is so close to the real data that it can be used as a drop-in replacement without sacrificing any meaningful quality. And, of course, all while guaranteeing full privacy.

Already two years ago, we looked at the quality of synthetic data generated with two popular open-source models: CTGAN and TVAE. Back then, we showed how MOSTLY AI’s  synthetic data had higher accuracy on multiple dimensions. This time we look more broadly at the open-source software library developed by MIT,  the Synthetic Data Vault (SDV). It was initially released in 2018 based on research work led by Kalyan Veeramachaneni. SDV is a Python library that supports three types of data: single table data, relational data, and time series data. In addition, SDV provides an evaluation and benchmarking framework, SDGym, and comes with 100+ datasets that can be used to explore the functionality.

For this benchmarking exercise, we picked five of the 19 provided single table datasets to get a good variety of data in terms of size and structure:

Synthetic data benchmarking

Currently, SDV offers five different models for synthesizing single table data: Tabular Preset (FAST_ML), GaussianCopula, CTGAN, CopulaGAN, and TVAE. To get a proper overview of the state of the art of open-source data synthesis, we spun up some virtual machines and synthesized all five datasets with all available models. And of course, we used the latest release of the MOSTLY AI Synthetic Data Platform to synthesize these datasets to compare. For the record – we used the standard configurations of all models and of our platform. We did not specifically try to tune any dataset. In total we created more than 5 million rows of synthetic data or 300 million synthetic data points.

The big picture of quality includes the functionality of the synthetic data

Since we wanted to check out SDV more broadly, we also had a look at the functionality to evaluate the quality of generated synthetic data. SDV’s Evaluation Framework takes a real and a synthetic dataset as an input and then calculates up to 29 different metrics comparing these two. It returns the average of the scores of the individual metrics, which results in an overall score from 0 to 1, with 0 being the worst and 1 being the best (= the synthetic data is really close to the real data). For our benchmark, we picked three metrics that worked without any further configuration (LogisticDetection, CSTest, and KSTest) and had SDV report the aggregate score. CSTest (Chi-Squared test) and KSTest (two-sample Kolmogorov–Smirnov test) are statistical metrics that compare the tables by running different statistical tests. LogisticDetection is part of the detection metrics, which evaluate how hard it is to distinguish the synthetic data from the real data by using a ML model (in this case a LogisticRegression classifier).

The results are summarized in the chart below:

Comparison of synthetic data generators

* Please note that no synthetic data for covtype could be created with CopulaGAN due to time-out issues, even on a VM with 224 vCPUs

In short: MOSTLY AI beat every single open-source model for every single dataset. Unsurprisingly the less compute intense FAST_ML, and GaussianCopula models cannot create highly realistic synthetic data with average scores of 0.68 and 0.63, respectively. From the more sophisticated models, TVAE performs best with an average score of 0.82, followed by CopulaGAN (0.78) and CTGAN (0.74). MOSTLY AI’s average score is 0.97.

Beyond the hard metrics: further evaluations on synthetic data generation

In practice, you will want to evaluate synthetic data on more dimensions than statistical and detection metrics. High-level metrics give you a first assessment of the quality of the created synthetic data, but the real deal is when synthetic data is actually evaluated by performing the exact same downstream tasks you would have performed using the real data. Again and again, these analyses confirm though what we already know: MOSTLY AI’s Synthetic Data Platform delivers the most accurate synthetic data consistently. But don’t take my word for it: you can find all the created synthetic datasets as a download here to perform whatever kind of analysis you wish.

The heart of our synthetic data platform is where we do not rely on open source but instead have developed our own proprietary IP. The approach and the deep learning architecture used to train a generative model. We have done so because this is what really matters when it comes to the achievable synthetic data quality.

There are other reasons to consider when choosing a synthetic data generator. In addition to unmatched synthetic data quality, some of the reasons for choosing MOSTLY AI’s Synthetic Data Platform include:

  • Ease of use: Our platform is so simple to use, practically anyone can synthesize data. You do not have to write a single line of code. You do not have to worry about choosing the generative model or about hyperparameter tuning. Just provide the data you want to synthesize, and the platform takes care of the rest – making sure to deliver the highest quality synthetic data.
  • Speed: Training sophisticated generative models is compute-intense, takes time and money. We waited several hours for some CTAN and TVAE models to train on strong VMs. With our platform on a lightweight VM, the synthesizations never took longer than an hour. In production, this can lead to significant cost savings.
  • Flexible data ingestion: Instead of being limited to ingesting CSV files, you can connect directly to various data sources, including cloud buckets (e.g., AWS S3) or relational databases like Oracle, MySQL, and PostgreSQL. This saves time and pre- and post-processing steps that are otherwise necessary.
  • Privacy guarantees: Synthetic data is not, per default, fully anonymous. For example, you need to make sure to handle outliers and extreme values properly. MOSTLY AI’s synthetic data generator takes care of all that automatically.  After the data synthesization is completed, in-built privacy tests will show you immediately if a synthetic dataset is private or not.
  • Dedicated support: We have been working in this space for more than five years and know synthetic data inside out. If you encounter any issues, we are there to provide support – for our enterprise clients with guaranteed SLAs.

If you want to experience the power of the MOSTLY AI Synthetic Data Platform yourself, you can sign up to generate synthetic data for free.