Synthetic data involves fabrications of data created by different algorithms to protect privacy, test systems, or generate training data for machine learning (ML). The importance of synthetic data lies in its ability to replicate the characteristics of real data sans revealing any identifiable information about individuals.
Producing synthetic data is crucial as it influences the quality of the simulated data. For instance, if the synthetic data can be reprogrammed to recognize real data, it lacks adequate utility for privacy protection.
One of the critical uses of synthetic data is meeting unique demands or conditions, not fulfilled by the existing data. Their use becomes especially beneficial under circumstances such as privacy restrictions hindering data availability, the need for testing a product in its developmental phase without real data, or training ML algorithms which require an expensive production of real-life data.
The advancements in processing and memory space in the 2010s have amplified the utilization of synthetic data, initially introduced in the 1990s.
Machine Learning and Synthetic Data
The machine learning sector is showing increased interest in synthetic data creation. Considering that ML algorithms require extensive data for learning purposes, the collection of the necessary amount of labeled training data could be economically challenging. Companies and researchers can address the issue by creating synthetic data to form datasets for pre-training ML models through transfer learning.
Research initiatives are currently underway to promote synthetic data generation in machine learning.
Synthetic Data Applications
The utility of synthetic data expands across two key sectors: financial services and healthcare. The methodology helps create synthetic data from real data, thereby enabling data professionals to utilize and share data more freely.
In healthcare, synthetic data can help make record-level data accessible to the public while preserving patient confidentiality. For the financial industry, synthetic datasets, like credit card transactions, help identify fraudulent behavior. Data scientists can use this fabricated data to examine or develop fraud detection systems.
DevOps teams also use synthetic data for software testing where fake data can be incorporated into the process without eliminating real-world data.
Generating Synthetic Data
Organizations can leverage techniques like deep learning algorithms, decision trees, and iterative proportional fitting to execute the data synthesis process based on their needs to create synthetic data for machine learning. Evaluating the worth of synthetic data post its synthesis by comparing it against real data is a recommended practice.
There are various open-source tools available to generate test synthetic data for use in running test cases.
Key Points to Remember
The success in synthetic data development largely depends on working with clean data. Evaluating if the synthetic data parallels real data for its intended purpose is essential. Also, it's crucial to assess the synthetic data capabilities of your organization and outsource based on the limitations in those skills. Data synthesis and data preparation are two critical phases, and suppliers can automate both these stages.