We discover the benefits of synthetic data, from protecting sensitive information to training cutting-edge technologies.
Imagine a world where you can train cutting-edge technologies without sacrificing data privacy and security.
This world is no longer a distant reality as synthetic datasets revolutionize the way we handle and use data.
Data is the fuel that drives technology forward, but what happens when the dataset is too sensitive to use, too expensive to obtain, or simply not available?
This is where synthetic data comes in - data generation is made algorithmically to approximate the original data.
Understanding Synthetic Data
Let us first define it is. Synthetic data is data generated by algorithms to approximate the actual data and can be utilized for the same purpose.
Companies use it for a variety of reasons, including a lack of original dataset, the need to protect sensitive information, or the need to comply with data protection rules such as the General Data Protection Regulation (GDPR).
Types of Synthetic Data: Text, Media, and Tabular
So, what kinds of synthetic data are there?
Text, media (video, image, and sound), and tabular synthetic data are the three basic categories.
It can come in the form of text that has been generated artificially. You create and train a text generation model.
It has always been difficult to achive synthetic data generation for text, but the introduction of new machine learning models, like as OpenAI's GPT-3, has resulted in the development of performant natural language production systems.
GPT-3 is a language model that was trained on massive amounts of text, such as Wikipedia and digital books.
Synthetic photos and videos are artificially generated media with qualities similar to real-world data. Because of this resemblance, synthetic media can be utilized as a drop-in replacement for genuine data.
The Generative Adversarial Network, StyleGAN2, for example, can generate realistic images of human faces. These images are used for a variety of reasons, including the creation of virtual settings for video games and the training of facial recognition systems.
Data created for a certain data format, such as a table or spreadsheet, is referred to as tabular synthetic dataset. This data is used to train machine learning algorithms or to test databases.
Real-Life Applications of Synthetic Data
Now that we've covered the various sorts of synthetic datasets, let's look at four real-world applications:
Amazon is training Alexa's language system using synthetic data. Amazon can train Alexa's language system without using sensitive consumer data by generating a synthetic one. This helps to secure Alexa users' privacy while also boosting system speed.
Waymo, a subsidiary of Google, trains its self-driving cars using synthetic data. Waymo can test its autonomous vehicles in simulated real-world scenarios using synthetic data, without the risk of causing accidents on the road.
Anthem, a health-care provider, collaborates with Google Cloud to do synthetic data generation. Anthem trains machine learning algorithms for predictive analytics using synthetic datasets, which does not expose sensitive patient information.
AMEX & J.P. Morgan:
American Express and J.P. Morgan are improving fraud detection by leveraging fake financial data. These organizations may train their fraud detection algorithms without exposing sensitive consumer information by generating fake data.
These are only a handful of the numerous real-world applications of synthetic data. It has the potential to transform the way we use data in technology, from training language systems to testing autonomous vehicles.
What are the best programming languages for synthetic data?
It is safe to say that Python and R are widely considered to be the best programming languages for synthetic data.
Python, Are We Even Surprised?
Not at all, because of course, Python is important in the domain of synthetic data. There are various libraries and tools in the language, that make data generation easier. Python is a powerful tool for producing synthetic data and developing novel solutions, whether you're a data scientist, software developer, or IT expert.
To emphasize, Python is a versatile programming language with a large library of data science and machine learning tools, such as NumPy, Pandas, Faker, Scikit-learn and Scipy that make it easier to create synthetic data.
What About R?
R, on the other hand, is a statistical programming language commonly used for statistical computing and graphics. It is weaponized with various tools that make data generation easy, such as:
Synthpop, Sampler, Faker, DataCombine, RSample.
Let us not forget though, in the end, the appropriate programming language for synthetic datasets would be determined by the unique requirements as well as the individual's knowledge and preferences.
Oh, in case you need a Python developer...
The Future of Synthetic Data
With the usage of synthetic data rapidly increasing, the future of data privacy and security may depend on our capacity to develop and use synthetic data properly. Will we be able to generate fully realistic and useful data, or will privacy issues and technological constraints prevent us from attaining our full potential? The only way to know is to wait and see.