According to a 2017 Harvard Business Reviewstudy, only 3% of companies’ data meets basic quality standards. Based on a 2020 YDatastudy, the biggest problem faced by data scientists was the unavailability of high-quality data.
Despite understanding thatdata is the new oiland 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 frameworksevolve rapidly, it’s safe to say the scarcest resource in AI is high-quality data at scale.
The Synthetic Data Communityaims 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,claimssynthetic 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 portfoliocan benefitfrom 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.
State-of-the-art Synthesizers and Open Source
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:
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.
Got any questions? Join our dedicatedSynthetic Data Community Discordserver 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 theSynthetic Data Community— join us on this exciting journey.
1. What is the ydata-synthetic and what does it do? ydata-synthetic is an open-source Python package developed by YData’s team that allows users to experiment with several generative models for synthetic data generation. The main goal of...
Identity Disclosure Risk in a Fully Synthetic Dataset
In today's digital age, data has become an integral part of every organization's operations. Companies gather and analyze vast amounts of data to make informed decisions and gain insights into their customers' behavior and preferences....
Synthetic Data: the future standard for Data Science development
In today’s world where data science is ruling every industry, the most valuable resource for a company are not the machine learning algorithms, but the data itself. Since the rise of Big Data, a theoretical understanding that data is...