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, generative adversarial networks, computer vision, and transformers. Qualitative data comprises text, images, and video, while quantitative synthetic data is tabular. Business leaders and data scientists across industries prioritize creative data synthesis to address data gaps, protect sensitive data, and speed to market. Finding and exploring synthetic data use cases like:
- Edge cases and sample size increase with synthetic tabular data. When paired with real datasets, this data improves AI model training and prediction.
- Synthetic test data speeds application and feature testing, optimization, and validation.
- Synthetic data from agent-based simulations for “what-if” or new business events.
- Protecting sensitive machine learning data with created data.
- Sharing and selling a high-quality, privacy-protected synthetic copy to internal stakeholders or partners.
Synthesising data provides improved data value and guards against data privacy and anonymization strategies like masking. Business leaders lack trust. To build confidence and adoption, synthetic data creation tool manufacturers must answer two questions corporate leaders ask: Does synthetic data enhance my company’s data privacy risks? How well does synthetic data match mine?
Best practices help organizations answer these challenges and build trust in synthetic data to compete in today’s shifting marketplaces. Check it out.
Keeping bogus data private
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. If a company prioritizes precision in synthetic data, the output may include too many personally identifiable traits, accidentally increasing privacy risk. Companies and vendors must work hard to minimize inadvertent linkages that could reveal a person’s identity and expose them to third-party assaults as data science modeling tools like deep learning and predictive and generative models evolve.
Companies interested in synthetic data reduce privacy risk:
Data should stay
Many companies are moving their software programs to the cloud for cost savings, performance, and scalability, but privacy and security require on-premises deployments. This includes fake data. Low-risk public cloud deployment of synthetic data without private or PII or model training data. When synthetic data requires sensitive data, organizations should implement on-premises. Your privacy team may prohibit sending and storing sensitive PII client data in third-party cloud providers, notwithstanding their outstanding security and privacy measures.
Be in charge and protected
Some synthetic data uses require privacy. Executives in security, compliance, and risk should govern their intended privacy risk during synthetic data generation. “Differential privacy” enables data scientists and risk teams choose their privacy level (1–10, with 1 being the most private). This method hides each person’s participation, making it impossible to tell if their information was used.
It automatically finds sensitive data and hides it with “noise”. The “cost” of differential privacy is reduced output accuracy, although introducing noise does not reduce usefulness or data quality compared to data masking. So, a differentially private synthetic dataset resembles your real dataset statistically. Data transparency, effective data security against privacy threats, and verifiable privacy guarantees regarding cumulative risk from subsequent data releases are also provided by differential privacy strategies.
Understand privacy metrics
If differentiated privacy isn’t achievable, business users should monitor privacy metrics to determine their privacy exposure. Although incomplete, these two metrics give a good foundation:
Leakage score: Percentage of false dataset rows that match original. A synthetic dataset may be accurate, but too much original data may compromise privacy. When original data contains target information but is inaccessible to the AI model for prediction or analysis, data leaks.
Distance between original and generated data generates closeness score. Short distances make synthetic tabular data rows easier to extract, increasing privacy risk.
Synthetic data quality assessment
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. Lower-fidelity synthetic data is needed for realistic commercial demos, internal training assets, and AI model training situations than healthcare patient data. A healthcare company may use synthetic output to identify new patient insights that inform downstream decision-making, thus business leaders must ensure that the data matches their business realities.
Considering fidelity and other quality metrics:
Fidelity
A critical metric is “fidelity”. It assesses synthetic data based on its data model and real data similarity. Companies should know column distributions and univariate and multivariate column linkages. Understanding complex and huge data tables is crucial (most are). The latest neural networks and generative AI models capture these intricate relationships in database tables and time-series data. Bar graphs and correlation tables show lengthy but informative fidelity measurements. Open-source Python modules like SD metrics can help you learn fidelity analytics.
Utility
AI model training requires real datasets, which takes time. Machine learning model training is faster with synthetic data. Understanding synthetic data’s application in AI model training is essential before sharing it with the proper teams. The expected accuracy of a machine learning model trained on real data is compared to synthetic data.
Fairness
Enterprise datasets may be biased, making “fairness” important. A biased dataset will skew synthetic data. Understanding prejudice’s scope helps businesses address it. Identification of bias can help businesses make informed judgments, but it’s less common in synthetic data solutions and less significant than privacy, fidelity, and utility.
Watsonx.ai synthetic data usage
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. This statistics-based method creates edge situations and larger sample sets to improve AI training model forecasts. With this data, client demos and employee training may be more realistic.
Foundation models power Watsonx.ai, an enterprise-ready machine learning and generative AI studio. Watsonx.ai lets data scientists, application developers, and business analysts train, validate, adapt, and deploy classical and generative AI. Watsonx.ai aids hybrid cloud AI application development collaboration and scaling.
