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According to a 2017 Harvard Business Review study, only 3% of companies’ data meets basic quality standards. Based on a 2020 YData study, the biggest problem faced by data scientists was the unavailability of high-quality data.
Despite understanding that data is the new oil and the most valuable resource, not every company, researcher, and student have access to the most valuable data like some tech giants do. As machine learning algorithms, coding frameworks evolve rapidly, it’s safe to say the scarcest resource in AI is high-quality data at scale.
The Synthetic Data Community aims to break the barriers for data science teams, researchers, beginner learners to unlock the power of synthetic data. We believe having quality data is truly a game-changer.
What if we can create high-quality data that resembles the real-world data that was initially inaccessible? What endless possibilities would that unlock?
Before getting ahead of ourselves, let us understand basic building blocks and why they are essential in our data science toolkit.
Synthetic data is artificially generated data that is not collected from real-world events. It replicates the statistical components of actual data without containing any identifiable information, ensuring individuals’ privacy. When used right, it can give access to high-quality data at scale for everyone.
Danny Lange, senior VP of AI and ML at Unity, claims synthetic data could solve the most significant problems organizations face: collecting the right data, structuring it, cleaning it, and ensuring that it’s bias-free and privacy-law compliant. We can’t agree more.
It benefits not only organizations but individual data scientists and researchers too. Self-driven data scientists who are building a portfolio can benefit from the availability of high-quality data at scale.
Synthetic data is better than the real thing because we have control of what we create: data that is balanced, bias-free and privacy compliant with unlimited augmentations.
At Synthetic Data Community, we don’t only preach the importance of synthetic data; we take action to break all the barriers of entry for the essential emerging technology.
Generative Adversarial Network (GAN) is a generative model based on deep neural networks, a powerful tool for generating artificial datasets indistinguishable from real ones. The most common data science problems require tabular and time-series data, and hence we use GANs to create synthetic tabular and time-series data.
Instead of re-inventing the wheel, we looked at leading state-of-the-art research papers published in the space of synthetic data, implemented the synthesizers and presented them in one package for ease of use. Some of the research we referenced are:
WGAN (Wassertain GAN) and WGAN-GP (WGAN with Gradient Penalty)
DRAGAN (On Convergence and stability of GANs)
Cramer GAN (The Cramer Distance as a Solution to Biased Wasserstein Gradients)
And did we say open-source? Yes, you heard it right. All of the work we did is open-source, and as we work on adding more features to the library, we invite you to contribute and make the library even better.
Not interested in the research stuff — just tell me how do I get started?
Sure, fire your terminal and type in the following:
pip install ydata-synthetic
That’s it. You have all the synthesizers installed in a single command. Now to walk you through various library usages, we have included multiple examples presented as jupyter notebooks and python scripts.
We recommend starting with this example, Google Colab notebook, which synthesizes the minority class of the credit card fraud dataset.
Got any questions? Join our dedicated Synthetic Data Community Discord server and ask away everything. We’re a friendly bunch of people looking to learn from each other and grow in the process.
You understand synthetic data and its importance. You have state-of-the-art synthesizers at your disposal with a single line of command. You’ve got a bunch of examples to walk you through. You’ve got a vibrant, enthusiastic community to learn and grow together.
Do you know what all this means? Endless Possibilities.
When all the barriers to high-quality data are broken, the things we can accomplish are endless. Ladies and Gentlemen, introducing the Synthetic Data Community — join us on this exciting journey.
Fabiana Clemente, CDO at YData.
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