A shortage of high-quality data is a significant barrier to AI adoption. A recent Deloitte study found that 55 percent of organizations were delaying or avoiding some AI use cases due to data-related issues. Many organizations lack enough relevant, diverse and unbiased data to support their AI initiatives, particularly when data is siloed in various systems and applications.
Synthetic data can help fill the gap. As the name suggests, synthetic data is artificially generated data that can serve as a stand-in for real-world data when training AI models. It’s not enough to simply produce random data points. Synthetic data must mimic real-world data and be relevant to a specific AI use case.
In the past, data synthesis required the expertise of data scientists. After all, it’s one thing to generate a dataset with the names and ages of cancer patients. It’s quite another to create complete patient records with symptoms, medical histories and demographics. Now, however, AI-powered tools allow users to quickly and easily generate data that’s precisely customized to their needs.
Benefits of Synthetic Data
Generative models create synthetic data by analyzing the patterns in real-world training data and using that knowledge to generate similar data. AI makes it faster and less expensive to generate synthetic data than to collect real-world data. Users maintain full control over every aspect of the synthetic dataset, which can be tailored according to specific conditions.
Generative models also eliminate the need to manually label and annotate data — a time-consuming and error-prone process. They can automatically apply labels and a variety of annotations to real-world data or synthetic data that they create.
When organizations use synthetic data, they don’t have to be as concerned about exposing the sensitive information of real individuals. Synthetic data can also be used to mitigate biases in real-world data, although it can perpetuate those biases or even introduce new ones if not handled carefully.
Synthetic Data Types and Use Cases
Like real-world data, synthetic data can be structured and unstructured. Synthetic structured data is quantitative data that can be arranged in rows and columns, as in a relational database or spreadsheet. It is primarily used in education, finance, healthcare and other industries with a need to analyze complete database records of a customer, patient or student. It is especially useful in sectors with legal or regulatory mandates to maintain privacy and confidentiality.
Unstructured synthetic data can include text, images, sounds, 3-D models and other multimedia data. It is often used to train models for image classification, object detection, text translation and more. It is also used for medical diagnostics, training self-driving vehicles and other use cases that require analysis of images and 3-D spaces.
Synthetic time series data mimics the patterns and characteristics of real-world time-ordered data, such as sensor readings, stock market prices and web traffic. It is used for rigorous testing of models under various conditions and simulating future trends and behaviors.
How Organizations Can Capitalize on Synthetic Data
At the most basic level, synthetic data provides organizations with the volume of data they need to train their AI models. Beyond that, it provides higher-quality data without the gaps and inconsistencies that often plague real-world data. This allows them to go beyond off-the-shelf AI models and build models that precisely meet their business needs.
Synthetic data also allows organizations to perform analyses when no historical data is available. It’s useful for exploring new markets, accelerating research and development, and analyzing scenarios that rarely occur.
A recent survey conducted by Coleman Parkes found that 80 percent of decision-makers have a strong interest in using synthetic data. The Technologent team has the knowledge and experience to help you utilize synthetic data to address data challenges and support your AI initiatives. Contact us to schedule a no-obligation consultation.
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