Determine When You Should Use Synthetic Data

Data is the indispensable foundation of decision-making, but what can organizations do when usable data is lacking? With the aid of AI, synthetic data is fast becoming a more viable alternative. Our comprehensive primer on synthetic data helps you determine if it would be useful for your organization and how to communicate its value to decision-makers.

Synthetic data has a number of use cases, such as training AI models or testing software, where it can be a genuinely valuable, rapid, and inexpensive stand-in for real data. However, it should be relied on only when real-world data is unavailable, inadequate, or cannot be used due to confidentiality or privacy concerns. Organizations must have a clear understanding of the problem it is meant to solve and be able to directly relate it to the wider business strategy, in order to unlock its potential.

1. "Fake" data has real value.

Though created artificially rather than from real-world experience, synthetic data can provide decision-makers with actionable insights that benefit the organization as a whole. It is especially useful in cases where obtaining particular kinds of real-world data would be impractical or unethical, such as in the financial or healthcare sectors.

2. Plan to mitigate synthetic data’s risks.

Synthetic data carries its own inherent risks and sometimes falls short of representing the real world. Organizations must develop a plan to mitigate those risks and be aware of synthetic data's limits.

3. Make the business benefits obvious.

Like real data, synthetic data is not used for its own sake, but to achieve specific organizational outcomes. IT leaders must articulate the benefits of using synthetic data for particular use cases and link those benefits to overall organizational strategy to convince stakeholders of its value.

Use this step-by-step research to determine if synthetic data is right for your organization

Our research includes four-step guidance and a comprehensive template to help you decide whether synthetic data is right for you, and features highlights of an interview with NVIDIA Vice President of AI Research Sanja Fidler detailing the AI heavyweight's Cosmos platform, which creates synthetic data for robotics and self-driving cars. Use our comprehensive framework to clarify synthetic data’s specific value to your organization while outlining the business case to decision-makers.

• Articulate the business use case by engaging stakeholders, linking the use case to strategic objectives, and setting out the problem synthetic data is meant to solve.
• Identify the data gap to address by examining your data challenges, current data set, aims, and use case readiness.
• Assess your ability to execute by determining who should be involved and how, reviewing your data governance policies, and documenting your data generation plan.
• Make the case for synthetic data use, including monitored KPIs for expected benefits, and a risk monitoring plan.