Digital Decluttering

Gartner stated that by 2030, synthetic data will completely overshadow real data in AI models. But how about other applications? Synthetic data can be generated either by rules and statistics or AI. This talk will start by outlining the main challenges facing big data, collection size, bias, processing and storage etc, with examples from the fields of medicine and biology. All these problems create overwhelm for people and resources. One clear factor is the inefficiency of repetition. We should focus on valuable data, clean data, and find relevant data, focus on key data. Synthetic data can help us with data privacy, GDPR, ethics and bias. There is a potential to create clear cost and performance savings. Synthetic data could even have the potential to store data in a way that as much as you like can be generated on the fly, you only need to store and secure the model. Synthetic data has Speed – rapidly prototype without waiting for real data, Security – safeguard sensitive information and Scalability – scale effortlessly. Rust’s ecosystem can complement the development and use of synthetic data in varied applications. Let’s focus big data on what matters.

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