Synthetic data is gaining traction in AI and analytics, but definitions vary widely. At its core, it spans from filling gaps in existing datasets (data imputation) to generating entirely new datasets. The article outlines four key quadrants of synthetic data: data imputation focuses on enhancing incomplete datasets using advanced machine learning techniques; user creation involves generating users for simulations; insights modeling interprets outcomes; and manufactured outcomes create entirely new data constructs. Understanding these distinctions is crucial for effective use in data-driven fields.
At its core, synthetic data operates along two key dimensions, allowing for various use cases that make it essential in AI, analytics, and data science.
Imputation techniques have evolved, utilizing machine learning and generative AI models to create plausible values that enhance existing datasets that have gaps.
Synthetic data can be categorized into four quadrants: data imputation, user creation, insights modeling, and manufactured outcomes, each serving different functions.
Understanding the distinctions between types of synthetic data, including the difference between filling gaps in data versus generating entirely new datasets, is critical.
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