Artificial intelligence thrives on data. But in today’s world, access to high-quality real data is drying up. Collecting it is costly, subject to strict legal controls, and often riddled with privacy concerns. That scarcity has led to a powerful alternative: synthetic data. More organisations now believe that artificially generated datasets will fuel the next generation of AI, offering speed, scalability, and safety where traditional data falls short.
By 2026, experts estimate that about 60% of AI training material could be synthetic. Tech leaders such as Google, Microsoft, and OpenAI are investing heavily in this area, recognising that true progress in AI is no longer only about better models – it’s about revolutionising the source of data itself.
Defining Synthetic Data
Synthetic data is artificially created information designed to replicate the patterns, structures, and statistical behaviours of real-world datasets – without exposing personal or sensitive details. Unlike anonymised data, it contains no actual records and carries zero risk of identifying individuals.
The purpose remains the same as with real data: powering machine learning models, validating algorithms, and testing systems. But synthetic data does it with complete flexibility, improved control, and full compliance with frameworks such as the General Data Protection Regulation (GDPR).
How Synthetic Data Is Produced
Depending on the use case, data scientists generate synthetic datasets using a variety of approaches:
- Rule-based engines for structured formats like transaction tables, business records, or time-series inputs.
- Statistical modelling that mirrors the probability distributions in the original data.
- Deep learning techniques, including generative adversarial networks (GANs) and diffusion models, can craft synthetic images, speech, or even highly realistic text.
The end result is a dataset that is statistically valid, privacy-safe, and ready to train or test machine learning models.
Why AI Innovation Is Slowing: The Data Crisis
Breakthroughs in AI depend on abundant, high-quality information. Yet, across industries, projects stall due to the absence of usable datasets. Studies show that over 80% of AI initiatives falter because the data is incomplete, inaccurate, or legally restricted.
Contributors to this shortage include:
- Stringent privacy laws like GDPR and CCPA.
- High re-identification risks, with anonymised data still traceable in up to 80% of cases.
- Time-consuming, expensive processes for collection, labelling, and compliance.
- Gaps in coverage, particularly around rare events and minority representation.
This growing mismatch means companies face a bottleneck not in algorithms, but in the raw material required to power them.
The Hidden Cost of Real-World Data
Using authentic data for AI is never simple. Behind every dataset is a web of cost, effort, and risk:
- Expensive fieldwork to obtain information.
- Approval delays in regulated industries.
- Reliance on human annotators.
- Potential fines for non-compliance.
Corporations spend over $2.7 billion annually preparing AI-ready datasets – yet still encounter gaps and inconsistencies. Smaller firms often cannot afford such barriers, pushing them toward synthetic solutions that can deliver customised datasets instantly.
Limitations of Real Data in AI Training
Real data reflects the world, but not always reliably. It often carries bias, lacks rare cases, or omits underrepresented groups. This not only reduces accuracy but can embed discrimination directly into AI systems.
Moreover, datasets frequently contain personally identifiable information, making them unsafe for sensitive domains like healthcare or finance. Even pseudonymization offers little protection, since most datasets remain vulnerable to re-identification.
Synthetic data avoids these pitfalls, creating representative, high-quality datasets without linking back to individuals.
Collection and Labelling: The Costly Bottleneck
Gathering and annotating real data requires:
- Field studies of rare scenarios.
- Consent management and legal reviews.
- Manual expert labelling.
- Regulatory approvals.
Together, these steps consume time and money, delaying innovation. By contrast, synthetic data can fill gaps instantly – producing edge cases or balanced classes at scale. Companies report cost savings of up to 70% and faster deployment cycles.
Privacy Laws and GDPR Compliance
One of the greatest obstacles to AI adoption is data privacy. Even anonymised datasets pose risks of identification. Under GDPR, firms must guarantee true anonymisation, yet the standards are nearly impossible to meet. Fines for non-compliance can be severe.
Synthetic data resolves this dilemma: it contains no personal identifiers and is generated from scratch. This allows teams to share and use data freely without crossing legal red lines.
The Problem of Bias
AI cannot rise above the bias in its training sets. From hiring software to healthcare diagnostics, skewed datasets create skewed systems. Synthetic data provides a remedy: teams can design balanced, diverse datasets that reflect ethical considerations, not just statistical ones. With modern fairness frameworks, synthetic generation actively supports the creation of more inclusive AI.
Copyright and Intellectual Property Risks
Scraping internet data introduces another minefield – copyright. Texts, images, audio, and code online are often protected by law. Many AI models have been trained on copyrighted content without authorisation, leading to lawsuits and regulatory scrutiny.
Synthetic data sidesteps this issue entirely. Being artificially produced, it is copyright-free by design, ensuring safe, compliant use across industries.
Why Synthetic Data Matters
The strategic advantages of synthetic datasets are becoming clear:
- Lower cost: up to 70% savings.
- Faster development: instant data tailored to use cases.
- Privacy by design: no personal identifiers, no GDPR hurdles.
- Stronger models: especially valuable in data-scarce domains like healthcare.
- Versatility: supports formats from structured tables to speech and images.
A Recursive Future: AI Training AI
As models grow, their hunger for data grows too. The next frontier is AI generating synthetic data to train other AI systems. Through GANs and diffusion models, teams can simulate rare scenarios, improve accuracy, and scale learning cycles without waiting for real-world events.
Synthetic data is becoming not just a substitute, but a renewable resource powering continuous improvement.
Linvelo’s Role
At Linvelo, we help organisations embrace synthetic data for real business outcomes. With over 70 specialists – developers, consultants, and data experts – we create privacy-compliant, scalable solutions that turn complex AI challenges into working software.
👉 Interested in solving your toughest data bottlenecks? Start your project with us today.
FAQs
How is synthetic data created?
By statistical modelling and deep learning methods such as GANs. These generate realistic samples without exposing personal data.
Can synthetic data replace real data?
It often complements real datasets. In certain fields, it can even serve as the primary source, provided quality checks are in place.
Where is synthetic data most useful?
In healthcare, finance, autonomous systems, and any domain where privacy or scarcity is a challenge.
How do I measure dataset quality?
Key factors include fidelity to real data, utility in model performance, and privacy protection.
