Data Alchemy: Trustworthy Synthetic Data Generation
Many firms use structured and unstructured synthetic data to tackle their largest data challenges thanks to breakthroughs in machine learning models and artificial intelligence including generative AI
Synthesising data provides improved data value and guards against data privacy and anonymization strategies like masking
Artificial data, which is computer-generated rather than real occurrences like customer transactions, internet logins, or patient diagnoses, can reveal PII when used as AI model training data
Many companies are moving their software programs to the cloud for cost savings, performance, and scalability, but privacy and security require on-premises deployments
Some synthetic data uses require privacy. Executives in security, compliance, and risk should govern their intended privacy risk during synthetic data generation
Data scientists and business leaders must trust synthetic data output to use it enterprise-wide. In particular, they must immediately assess how well synthetic data matches their data model’s statistical properties
IBM watsonx.ai allows AI builders and data scientists input data from a database, upload a file, or construct a custom data schema to create synthetic tabular data