Synthetic Generated Data — Will it surpass real-world data in the future?

Finding the right dataset for any data science project is a challenging task. A machine learning model is dependent on the quality and quantity of the dataset. And, training a robust AI model, requires a vast quantity of data.

What is Synthetic Dataset?

As the name suggests, the synthetic dataset is similar to a real-world dataset but is generated programmatically. Unlike real-world datasets it is not collected by any real-life means, surveys or experiments. A synthetic is generated as per the requirement of the data science project and is used for a wide range of activities including training robust AI models, testing and validation models.

Why Synthetic Dataset is Important?

There are several reasons why synthetic datasets overdue real-world datasets:

• Specific Data Requirements: The synthetic dataset can be generated to meet the specific demand of the dataset for any data science project that can’t be fulfilled using a real-world dataset.
• Control of vast data with tech giants: A large amount of data is generated every time, but the control of the dataset is with some of the tech giants like Google, Microsoft, Amazon, Facebook, etc. Small companies or startups don’t have access to a vast and accurate dataset, so they have to be dependent on artificially generated datasets or synthetic data.
• Data Privacy: Data privacy is an important aspect to consider. Data can be misused by hackers, as they can access the data by making structured queries to the model to get personal data. White Box and Black Box attacks can be performed on a model infer personal data and the model outputs can be changed.
• Generating Data to handle edge cases: The model needs to be trained and tested for every situation. For some conditions, the real-world dataset may not be available for training or testing the model. For a self-driving car project, to handle the case when someone comes suddenly in front of the vehicle, such real-world might be risky to record, hence synthetic data generation is feasible.
• Expensive real-world dataset: In some cases recording real-world datasets can be very expensive, in that case generating a synthetic dataset to meet the requirements of the project can be the most economical option. To record real-world datasets for self-driving cars can be expensive, instead, computer-generated simulations can be a feasible alternative.

Synthetic Data can be a game-changer for data science startups or small companies. Mostly.AI is an AI-powered synthetic data generation platform that claims that 99% of the information in the real-world dataset can be reclaimed in the artificially generated dataset. That makes synthetic data fully anonymous and as good-as-real.

There are various techniques to generate synthetic datasets. Before determining the method to generate synthetic data, one must figure out the type of synthetic data they aim to. There are broadly two broad categories:

• Fully Synthetic Data Generation
• Partial Synthetic Data Generation

Different techniques to generate artificial dataset are:

• SMOTE
• ADASYN
• Data Augmentation
• Variational Auto Encoders
• Generative Adversarial Networks (GAN)

Read the below article, to know more about how to create an artificial image dataset using open-sourced python library.

The requirement for synthetic datasets is increasing rapidly in machine learning, as machine learning models are trained using a vast amount of well-prepared data as per requirement, and obtaining such a real-world dataset is very difficult.

There are several additional benefits of synthetic datasets such as accuracy in labeling, replacement of sensitive information, ease in data production, and many more.

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