In real-world applications, where data is subjected to a multitude of data quality issues, the implementation of Data-Centric AI best practices becomes severely compromised, which impacts the development of robust AI solutions and consequently business outcomes.
Challenges such as data scarcity, privacy concerns, and data bias have however paved the way for an innovative solution: Synthetic Data. This artificially generated data is proving to be a game-changer across all industries, with Gartner predicting the use of about 60% of synthetic data as soon as 2024.
In this article, we delve into the top five advantages of synthetic data in shaping the future of AI and enhancing the development of machine learning systems across various applications.
In applications where data privacy is paramount, synthetic data emerges as a powerful ally. By generating data that faithfully replicates the statistical properties of real data, but without any personally identifiable information (PII), synthetic data facilitates collaboration among researchers and stakeholders, enabling organizations to analyze important information and share valuable insights without divulging sensitive information.
In domains where data is hard to obtain, synthetic data eases the burden of collecting additional real-world data so machine learning models can be trained effectively. Synthetic data arises as a suitable solution for data augmentation, creating realistic training sets that allow machine learning classifiers to improve their generation and performance. This not only reduces additional costs in obtaining more data to train, but it effectively accelerates the process of AI development.
Since missing data often hinders the application of certain AI techniques (or heavily jeopardizes the accuracy of their predictions), synthetic data can be used as a data imputation technique. By generating synthetic instances that align with the distribution of real data, synthetic data can be used to impute missing values with plausible replacement values that mimic the overall properties of data. Additionally, synthetic data also be used to map and incorporate the underlying missing mechanism in data, replicating the original data behavior, including the missingness process.
AI systems are only as unbiased as the data they are trained on. Beyond being used for general data augmentation (i.e., increase training sets), synthetic data can increase data diversity by augmenting tailored subgroups of data that are underrepresented, thus helping to mitigate bias concerns. When trained with more diverse datasets, AI models can learn to make fair and balanced decisions, leading to more equitable and responsible applications across domains such as finance and heathcare, for instance.
Synthetic data accelerates AI development by expediting data generation, where data teams can create custom-tailored datasets on demand: data scientists can swiftly create large volumes of synthetic data with specific attributes and characteristics, enabling models to learn faster and iterate more quickly. This flexibility is especially valuable when dealing with niche or rapidly evolving domains where real-world data might be scarce or outdated, or where edge and rare cases are necessary to improve the generalization ability of AI models. This agility ensures that AI development remains agile, responsive, and primed to tackle the ever-changing demands of the AI landscape, translating to reduced time-to-insight, faster deployment of solutions, and faster return on investment (ROI).
With is potential to ensure privacy, augment datasets, imputing missing data, reduce bias, and expedite development, synthetic data is revolutionizing the AI landscape.
In the upcoming years, as AI continues to empower industries and shape societies, synthetic data will stand as one of the pivotal tools for fostering innovation and delivering fast and successful solutions.
Join the revolution of synthetic data and start leveraging its benefits for your organization’s use cases with YData Fabric. Unlock the potential of your data with our community version, and start generating synthetic data that aligns with your business goals in a seamless and effortless manner.
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