There are several challenges when creating synthetic data. Below are some general limitations and challenges you will likely experience with synthetic data.
Quality control
Data quality is vital in statistics and analytics. Before you incorporate synthetic data into learning models, you must check that it is accurate and has a minimum level of data quality. However, ensuring that no-one can trace synthetic data points back to real information may require a reduction in accuracy. A trade-off in privacy and accuracy could impact quality.
You can perform manual checks of synthetic data before you use it, which can help to overcome this issue. However, manually checking can become time-consuming if you need to generate lots of synthetic data.
Technical challenges
Creating synthetic data is difficult—you must understand techniques, rules, and current methods to ensure its accuracy and utility. You need high expertise in this field before you’ll be generating any useful synthetic data.
No matter how much expertise you have on your side, it is challenging to generate synthetic data as a perfect imitation of its real-world counterpart. For instance, real-world data often includes outliers and anomalies that synthetic data generation algorithms can rarely recreate.
Stakeholder confusion
Although synthetic data is a useful supplementary tool, not all stakeholders may understand its importance. As a more recent technology, some business users may not accept synthetic data analytics as having real-world relevance. On the flip side, others may over-emphasise the results due to the controlled aspect of generation. Communicate the limits of this technology and its outcomes to stakeholders, making sure they understand both benefits and shortfalls.