Providing insights: from healthcare to fashion
Data generated through simulation environments allow users to conduct “what if” analyses and design new test scenarios. This is particularly useful when no real data is available. During the COVID-19 pandemic, many of the AI models that healthcare professionals and researchers used required advanced computation. Researchers used large quantities of synthetic data that was based on actual patient data but not directly derived from individual records. Synthetic data were also used to study the spread and impact of the pandemic over time across densely tested geographic areas.
Use cases are emerging in various sectors, like financial services, software testing, pharmaceuticals, manufacturing and distribution, retail, fashion and others. For instance, banks and financial services companies can use synthetic data to evaluate potential market behaviors, design algorithms for more equitable loan distribution, combat financial fraud and make new products and services.
In the pharmaceutical industry, synthetic data is useful when handling large but sensitive samples, where regulatory restrictions and data privacy is a challenge. It enables faster and better trials as well as cross-border research.
In agriculture, digitally generated data can be helpful in developing computer vision applications for crop yield prediction, crop disease detection, identifying fruits and predicting plant growth models.
Natural language processing is an area where synthetic data is used widely, especially while training systems of virtual voice assistants. In manufacturing, synthetic data is used to train AIML for industrial robots to enable factory automation and for robots to perform complex tasks in the production line. Artificially generated data sets can train AI in autonomous check-out systems, study customer demographics, or run cashier-less retail stores. Apart from these, advanced ML models trained on synthetic data help e-commerce companies in improving warehousing and inventory management.
Synthetic data has multiple use cases and solves many of the problems associated with real-world data. It is, however, not a one-stop solution. There are significant risks and limitations, since the quality of data generated largely depends on the quality of the model that created it. This means that biases can still exist, and it can get obsolete quickly. However, advances in synthetic data generation will boost the accuracy of ML models and accelerate AI. Used with due caution, it has the potential to make the software more trustworthy as well as transform the economics of data.