Synthetic Data: From Hype to AI Game-Changer

In the race to build smarter AI and advanced systems, organizations and technology developers are focusing on optimizing models. They are fine-tuning AI architectures, scaling capabilities, and pushing benchmark scores higher.

Yet amidst this chaos, one must not forget that behind every impressive model lies a more fundamental force: data. Not just any data, but data that is high-quality, diverse and available in sufficient quantity.

But as we reach the limits of what real-world data can offer, whether that is due to privacy concerns, cost or simple scarcity, a quiet revolution is gaining pace.  

Synthetic data is emerging not just as a workaround but as a cornerstone of the next generation of AI.  Whether for automation, large language models or AI applications in tightly regulated sectors, synthetic data is solving problems that traditional data simply cannot.

Increasingly, synthetic data is the invisible thread weaving through AI systems – that’s powering its creation, evolution, and accountability. As AI becomes more powerful and apparent both in business and daily life, the importance of synthetic data that fuels it will only grow.

For CTOs, synthetic data isn’t just a technical curiosity—it’s a strategic lever. It can unlock AI opportunities, enable safer experimentation, and future-proof organizations against data scarcity and compliance risks.

This article will explore the benefits and risks of synthetic data and what it could look like in the future AI space.  It also highlights practical best practices for CTOs to implement a winning synthetic data strategy.

Understanding synthetic data

Synthetic data is artificially generated to match the statistical properties of real-world data. It is created using algorithms, simulations, or generative AI models to replicate the statistical relationships, diversity, and structure of real datasets.

For example, instead of using actual customer records, a bank might generate a synthetic dataset that has the same format and statistical characteristics as real customer data, but with entirely fictional individuals.

The synthetic data feels real (accounts, balances, transactions, etc. in similar proportions) without exposing any actual personal information.

Benefits of synthetic data

Tech giants are betting big on synthetic data. Meta has its Self-Taught Evaluator. Google has described its approach to generating private synthetic training data. NVIDIA has released a family of open models that users can leverage to create synthetic data for training LLMs.  

The reason behind this growing demand is as follows:

Unlimited data generation and cost-effectiveness

Acquiring and labeling real-world data is expensive and time-consuming. Whereas, synthetic data can be generated faster in limitless quantities, based on specifications rather than needing to wait to collect data once it occurs in reality.

Moreover, unlike real data, synthetic datasets come pre-labeled, saving time and reducing the cost of manual annotation.

Addresses privacy and ethical issues

Regulations and proprietary restrictions can severely limit the use of real-world data. Concerns over privacy, ownership, and ethical use further complicate access, slowing innovation in regulated industries.

Hence, instead of personal data, tech professionals can use synthetic data to serve the same purpose as these private datasets. They can create similar data with statistically relevant information without exposing private or sensitive data.

Fairness and diversity

One of the most pressing challenges in AI today is ensuring models are not only accurate, but also fair, explainable, and robust. Achieving this requires data that reflects a wide spectrum of demographics, scenarios, and environments.

The problem is that real-world datasets—often drawn from historical records or observational data—rarely provide that level of diversity. They may overrepresent common cases while overlooking minority groups or rare but critical events.

Synthetic data can be engineered to plug these gaps. By generating data that covers underrepresented groups, it enables AI systems to perform more reliably and fairer in the real world.

For example, in early 2024, Google’s Gemini model made headlines for generating historically inaccurate images, a byproduct of fine-tuning efforts that failed to balance diversity with contextual accuracy.

It was a sharp reminder that data quality and diversity are not trade-offs but essential components of responsible AI development.

Risk assessment and rare event simulation 

Synthetic data is also a powerful tool for risk assessment and rare-event simulation. Organizations can generate scenarios that are difficult—or even impossible—to capture in real life, such as financial crises, cybersecurity breaches, or extreme customer behaviors. By exposing systems to these synthetic “what-if” situations, businesses can test resilience, identify vulnerabilities, and ensure their models perform reliably under stress.

Challenges with synthetic data

Synthetic data is not a universal fix. It still has some limitations.

Lack of realism and accuracy

The most significant limitation of synthetic data is its inability to fully replicate the realism and accuracy of real-world datasets. While it can capture broad patterns and correlations, generating data that reflects the subtle nuances of real-world remains a challenge—especially when the model is poorly calibrated or fails to mirror the true underlying distribution of the source data.

Also, synthetic datasets can potentially omit important details or relationships needed for accurate predictions, reducing their effectiveness in high-stakes use cases.

For example, a healthcare organization might create synthetic patient records to train an AI model for predicting disease progression. However, if the data lacks sufficient realism, the model may struggle to deliver accurate predictions in real-world scenarios.

Dependency on real data

Synthetic data generation remains highly dependent on the quality of the underlying real-world data. If the source data is incomplete, biased, or inaccurate, the synthetic output will inevitably reflect those flaws. Moreover, as real-world datasets evolve over time, synthetic data must be continuously validated and updated to maintain accuracy, relevance, and reliability.

Generation of frictional data and its social implications 

When AI generates fictional characters, scenarios, or datasets, it raises questions about responsibility and accountability. Poorly managed or misleading synthetic content could spread misinformation, create misunderstandings, or unintentionally disseminate false information, potentially causing negative impacts on society.

Synthetic data isn’t neutral

The uncomfortable truth is that synthetic data is not a neutral input. It carries the fingerprints of its creator, usually a large foundation model trained on public internet content.

This means when you use that data, you’re importing someone else’s worldview, assumptions and statistical boundaries into your strategy. Without realizing it, a model’s simulation of reality is shaping your innovation road map, not your own reality.

What you get is a plausible and polished, but ultimately generic, version of the world. Just like how Instagram photos often look polished and consistent but lack the messy, imperfect details of real life, synthetic data can miss the quirks, irregularities, and edge cases that real-world data naturally contains.

Best practices for CTOs for a successful synthetic data strategy

To effectively harness the power of synthetic data while mitigating the risks, here are some actionable steps CTOs can take.

Start small

Don’t jump in right away. Begin by generating a small, focused synthetic dataset for a specific, non-critical task. Compare the performance of the model trained on this data to understand the impact initally.

Adopt a hybrid approach

The most effective strategy is a hybrid one, using a small amount of high-quality real data to fine-tune your generative models and a larger volume of synthetic data for training at scale. This combination gives you the best of both worlds: real-world fidelity and synthetic scalability.

Identify and examine source assumptions

Synthetic data tends to mirror developer convenience, not domain complexity. Your models may be digesting generalized scenarios instead of your unique market truths.

To ensure relevance and reliability, carefully evaluate the foundations of your synthetic data: Ask questions like:

Are prompts customized or copied? Are synthetic scenarios seeded with real insights? :Does this synthetic data reflect our real-world business reality, or is it just based on broad, generic patterns that may not apply?

By interrogating these assumptions, you can ensure that synthetic data truly represents your business context and drives meaningful AI outcomes.

Invest in expertise and tools

Generating high-quality synthetic data is a specialized skill. Whether you hire a dedicated team or partner with a vendor, ensure you have access to experts in machine learning, statistics, and data synthesis. Choose tools that offer strong validation and evaluation frameworks to help you measure data quality and model performance.

Remember: Don’t see synthetic data as a one-size-fits-all solution. But when it fits, it can be a powerful tool — especially when combined with a clear understanding of where it adds value, where it doesn’t, and how to get the most out of it.

In all, success with synthetic data depends less on the hype and more on making the right foundational choices. So if you are working with synthetic data, ask how it will be used, who will create it, and what standards it needs to meet. That’s where the difference lies — not in the generation itself, but in the thinking behind it.

Future of synthetic data

Gartner predicts synthetic data will surpass real data in AI model training by 2030, with the market growing from $351.2 million in 2023 to USD 2,339.8 million by 2030, at a CAGR of 31.1%.

The data speaks volumes. It signifies that synthetic data will completely overshadow real data in AI models in the future.

The era of synthetic data is here, and it’s set to reshape the landscape of AI development. It offers a clear path to overcoming the most significant barriers to innovation: data scarcity, privacy concerns, and cost.

Synthetic data will turbocharge the spread of AI across society by democratizing access to data. It will serve as a key catalyst for our AI-driven future.

The companies that learn to master the art of data synthesis will be the ones that build faster, more innovative, and more responsible AI solutions.

In brief

The future of technology isn’t just about automation; it’s about smarter, faster, and more predictive decision-making. Synthetic data are a key piece of that puzzle, and forward-thinking tech leaders should explore how to integrate it now rather than scrambling to catch up tomorrow.