Why should you trust Synthetic Data for AI Models?

CIO.com reported that “With thoughtful synthetic data, companies can find the balance between data privacy and utility. Organizations can take advantage of generated data that lacks personally identifiable information (PII) — enabling them to gain its full value while being able to indefinitely store, analyze, and train models with it.”  The January 20, 2025 article entitled “Synthetic data: Generate safer and better data for AI models” (https://tinyurl.com/59avace5) included these comments:

Every company is a data company, but not every company can use its data fully. International Data Corp. (IDC) estimates that, on average, approximately 270GB of healthcare and life science data was created for every person in the world in 2020.1 Yet, 97% of healthcare data isn’t used, according to a MedCity News article.

No matter the industry, the business of using data for value becomes complicated — not only by regulations such as HIPAA, GDPR, and CCPA but also by customers’ expectations that their data privacy should be a priority, especially in a world of big data breaches.

Different organizations have different barriers to cross. Using unbalanced data sets, for example, runs the risk of bias, which presents many challenges. Upon feeding it into their generative artificial intelligence (genAI) models, they fall into the IT adage “GIGO” — or “garbage in, garbage out.” Other businesses stop before they can even start, halted because gathering real-world data is time-consuming and costly.

These issues with using real-world data to train AI models make it difficult to scale, hindering the success of an organization’s AI initiatives. Synthetic data generation, however, enables companies to see those barriers removed, enabling fast experimentation and innovation with synthetic data sets that have the same characteristics as real data but without privacy risks.

Interesting, what do you think about Synthetic Data in AI Models?