Taking Plunge With Synthetic Data
Synthetic data is less a data science experiment than a management decision about where to accept approximation. For CIOs and data leaders, the first question is not whether the technique works in principle, but which business problems justify the governance overhead, validation effort and residual model risk. The strongest use cases are usually narrow: privacy-constrained testing, rare-event simulation and controlled AI training where real data is incomplete or too sensitive to use directly.
That makes portfolio discipline essential. Synthetic data should compete for funding against other ways to improve model quality, such as better instrumentation, data quality remediation, master data work or process redesign. Leaders should ask whether the constraint is truly data scarcity, or whether the real issue is weak data management. If the latter, synthetic data can become a workaround that masks structural problems instead of fixing them.
Governance needs to be explicit before adoption scales. Teams should define who approves synthetic datasets, how generation rules are documented, what validation thresholds are required, and how often synthetic outputs are compared with real-world distributions. Without those controls, organisations risk training models on elegant but misleading substitutes. This is especially important where decisions affect customers, patients, citizens or regulated workflows.
The practical next questions are organisational, not technical:
- Which use cases have enough business value to justify a synthetic-data operating model?
- Who owns approval, lineage, and risk acceptance across data, analytics and compliance?
- What metrics will prove the dataset improves model performance without distorting reality?
For executive teams, the real test is whether synthetic data increases delivery speed while preserving trust. If it does not, it should remain a targeted tool rather than a default strategy.
Instead of being created by real-world activities, like conventional data, synthetic data is totally artificial. Constructed algorithmically, synthetic data is frequently used as a substitute in test datasets, as well as to validate mathematical models and train AI and ML models.
Synthetic data is relatively inexpensive to create, easily accessible, and allows testing without any human impact concerns, says Viveca Pavon-Harr, chief data officer at Accenture Federal Services in an email interview. Synthetic data can also facilitate faster model testing and evaluations and, depending on the type of work an organization does, can allow quicker data acquisition and data documentation.
Synthetic data is prized for its ability to create balanced and unbiased datasets, a significant challenge in machine learning, observes Woody Zhu, an assistant professor of data analytics at Carnegie Mellon Universityโs Heinz College of Information Systems and Public Policy via email. โBy simulating data, we can address issues of bias and fairness, particularly in high-stakes fields like healthcare, power systems, finance, and education,โ he explains. โThis leads to the development of more trustworthy and inclusive machine learning models.โ
Numerous Benefits
Related:How Synthetic Data Can Help Train AI and Maintain Privacy
Itโs frequently difficult to gain a high degree of accuracy when thereโs only a limited availability of data, says Olga Kupriyanova, principal consultant with global technology research and advisory firm ISG via email. โOrganizations can leverage synthetic data to train models that would otherwise not reach the necessary levels of performance,โ she explains.
Perhaps the most typical synthetic data use case is fraud detection. โFraudulent events are rare, yet models need to be trained to detect them,โ Kupriyanova says. โThe best way to do this is to generate synthetic events data to expand training opportunities.โ
Synthetic data shines when real data is scarce, sensitive, or too risky to use. โIn scenarios where gathering ample and diverse data is impossible, challenging, or unethical, synthetic data steps in as a reliable alternative,โ Zhu says. โIt allows organizations to model complex situations without compromising privacy or safety.โ
Synthetic data becomes easily attainable and inexpensive to create when generative AI is used. โThe data is not only easily generated, but it can also have embedded annotations already included,โ Pavon-Harr notes. โThis is a huge benefit for organizations, given that it reduces the labor-intensive task of going through data and identifying features and metadata.โ
Related:Data Strategy: Synthetic Data and Other Tech for AI’s Next Phase
Yet another benefit is that data can be generated in a way that removes or limits biases and vulnerabilities. This attribute can help reduce the creation of unintentional information or information that may not be truly representative of a particular group. โIf we think about the medical space, for example, using patient information could violate privacy concerns,โ Pavon-Harr observes. By using synthetic data, private information about individuals can be completely removed. โThis provides great opportunities for research and scenario building without exposing negative events or consequences.โ
Any model-generated content, whether a prediction or a set of synthetic variables or outputs, can be subject to bias or inaccurate content. โThis is especially a risk for synthetic data, which by its nature is tied to the rules set to it by the model creator,โ Kupriyanova says. โItโs important to remember that synthetic data effectively generates data via generative AI capabilities, which means it can hallucinate when given the direction to create something it doesnโt have enough context for.โ In other words, all of the risks associated with generative AI also exist for synthetic data.
Related:Building Strong Data Pipelines Crucial to AI Training
Getting Started
Synthetic data initiatives should be driven by need. โIf you have a business use case that requires an AI solution, but you canโt get enough data to generate the right kind of behavior, then itโs time to consider ways the model can be improved,โ Kupriyanova says. โOne of your options will be synthetic data.โ
On the downside, if the synthetic data isnโt correctly developed, the resulting models wonโt perform as expected. โIf the data created isnโt a true representation of whatโs being evaluated, the models will not converge,โ Pavon-Harr says.
Initiating work with synthetic data requires a foundation in high-quality real data or substantial domain knowledge, Zhu warns.
An Opportunity
Synthetic data offers an opportunity to study new methodologies and infuse creativity into various approaches to AI without putting humans or sensitive data at risk. Synthetic data should be used to exemplify human populations, expedite research opportunities, and remove bias whenever possible. โAll generalized assumptions should be vetted to ensure as much truth as possible is included in the data, not just what was conveniently gathered,โ Pavon-Harr states.
While synthetic data is immensely useful, itโs important to be wary of over-reliance. โThereโs always a risk of missing out on subtle real-world nuances,โ Zhu explains. โEnsuring accuracy in simulation and being mindful of ethical considerations in data representation and usage are key.โ
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