In an increasingly data-centric world, enterprises must focus on gathering both valuable physical information and generating the information that they need but can’t easily capture. Data access, regulation, and compliance are an increasing source of friction for innovation in analytics and artificial intelligence (AI).
For highly regulated sectors such as Financial Services, Healthcare, Life Sciences, Automotive, Robotics, and Manufacturing, the problem is even greater. It causes barriers to system design, data sharing (Internal and external), monetization, analytics, and machine learning (ML).
Synthetic data is a tool that addresses many data challenges, particularly AI and analytics issues like privacy protection, regulatory compliance, accessibility, data scarcity, and bias. This also includes data sharing and time to data (and therefore time to market).
Synthetic data is algorithmically generated. It mirrors statistical properties and patterns from the source data. But importantly it contains no sensitive, private, or personal data points.
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