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In today’s digital landscape where “data is the new oil,” synthetic data generated via Artificial Intelligence (AI) tools is advancing the way we build data pipelines. Here we discuss the process of creating synthetic data with AI, its benefits, and how it can be leveraged to boost development in and around your data infrastructure.
What is Synthetic Data?
Before delving into how to generate synthetic data, it’s crucial to understand what it is. Synthetic data is artificial data generated programmatically. It mimics the statistical properties of original data but does not contain any real-world incidents or sensitive information, hence it can’t breach privacy or security regulations.
How to Create Synthetic Data with AI
Step 1: Understand the Original Data
The first step in creating synthetic data involves understanding the original or ‘real’ data that the synthetic data will emulate. This includes understanding its features, patterns, trends, outliers, and any correlations that may exist.
Step 2: Choose the Right Model
Different synthetic data generation methods exist, both statistical and AI-based, depending upon your data and use-case. Two commonly used AI methods are Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs).
Step 3: Train the AI Model
After choosing the appropriate model, train it using the original data. The model learns to generate synthetic data following the same distributions and patterns found in the original dataset.
Step 4: Generate and Refine Synthetic Data
Once the model is adequately trained, it generates synthetic data. Refinement involves iterative adjustments until the synthetic data is a nearly identical statistical representation of the original dataset.
Step 5: Validate Synthetic Data
The final step is validating synthetic data using statistical tests to ensure its accuracy and quality. If the generated data passes the validation metrics, it is ready for use.
Benefits of Synthetic Data
No Privacy Concerns
As synthetic data doesn’t include real events or information, it assuages concerns around data privacy, anonymity, and compliance. It’s a boon for industries that deal with personal data and are under continuous scrutiny for privacy breaches, like healthcare, finance, and marketing.
Unlimited and Diverse Data
AI can generate an unlimited amount of synthetic data and allows for the introduction of different scenarios, which might not be feasible with real data.
Cost-effective and Time-saving
Generating synthetic data for pipeline development is often cheaper, faster, and less resource-intensive than collecting and preparing real-world data.
Improved Model Performance
As AI can fine-tune synthetic data, it can be made more robust than the original data. Hence it can lead to enhanced pipeline performance.
In conclusion, the creation of synthetic data using AI is a practical and effective approach to counter the challenges that arise with real data. It helps organizations across industries preserve privacy while facilitating innovation and development. As AI continues to evolve, synthetic data will undoubtedly play a pivotal role in shaping the future of data science.
