Large Language Models (LLMs) are transforming how enterprises innovate, from automating decisions to powering AI co-pilots and accelerating business agility. Yet behind every successful LLM lies one critical factor: high-quality data.
However, in practice, most organisations still rely on live data to train their models. Developing prompts, training and models requires much more sophistication than using partially masked live data. Live datasets lack the diversity and edge-case coverage LLMs need, while creating compliance risks that can stall entire AI programs.
Synthetic data generation changes that. By generating safe, diverse and scalable datasets on demand, it removes data bottlenecks and fuels faster, safer and smarter AI development.
In this blog, we’ll explore how synthetic data not only enhances LLM accuracy, but also helps enterprises overcome broader data management challenges, improving coverage, accelerating automation and enabling real-time data provisioning across complex systems.
The challenges of training LLMs
Data quality sits at the heart of every successful LLM. Yet for most enterprises, achieving that quality is far from straightforward. Data is fragmented, poorly understood and systems are siloed. Compliance pressures are mounting and AI technology is rapidly changing at the same time as companies are pushing LLMs to wide-spread production use.
Relying solely on real-world data for training only compounds the problems, introducing compliance risks, gaps in coverage and data governance complexity that can slow innovation and inflate costs.
Here are four of the biggest LLM training challenges enterprises face today:
- Data silos and application complexity - Enterprise data is scattered across countless systems. Without strong governance, inconsistencies and inefficiencies quickly creep into training workflows.
- Rising regulatory and compliance pressure - Regulations like GDPR, the AI Act, DORA and industry-specific compliance standards demand strict, real-time oversight of sensitive data.
- Volume, coverage and quality at odds - Low-quality, inconsistent , noisy or redundant data can degrade performance and inflate training costs. Striking the right balance between data quantity, quality and coverage is a major challenge.
- Infrastructure and scalability limits - Traditional storage solutions typically can't meet the performance, reliability or redundancy requirements needed to train large language models at scale.
These challenges highlight a simple truth: real-world data alone can’t keep up with enterprise AI ambitions. Practically, the sheer volume of data, linked with a poor understanding and data quality, is a significant barrier to a systematic and reliable LLM implementation.
How to systemically test LLMs
Join Curiosity’s Chairman & Co-Founder, Huw Price and Head of Solution Engineering, Harry Burn as they unpack how Large Language Models are reshaping the way enterprises test.
Synthetic data: A smarter way to train and test LLMs
Synthetic data offers a faster, safer and more scalable way to train and test LLMs, without compromising on accuracy or compliance.
By generating clean, diverse and regulation-ready datasets on demand, synthetic data not only supplements real data, it enhances it. It gives enterprises the freedom to explore new scenarios, test edge cases and fine-tune models with confidence, fuelling high-performing, ethically sound and continuously improving LLMs.
Here’s how synthetic data is redefining the way enterprises approach AI training and testing:
- Synthetic data generation at scale: By automatically generating high-quality, domain-specific datasets, synthetic data ensures LLMs are exposed to diverse as well as representative data.
- Compliance and quality by design: By simulating production-like data without using real identities, synthetic data enforces privacy from the outset, removing the need for masking or obfuscation.
- A proactive approach to data management: Continuous generation, automated provisioning and real-time validation ensure environments always have compliant, fit-for-purpose data, reducing rework, defects and business risk.
- High-quality data delivered consistently: Synthetic data enables teams to deliver clean, representative datasets across languages, domains and formats, ensuring every dataset supports fairness, relevance and model excellence.
Together, these capabilities make synthetic data a cornerstone of modern AI readiness, empowering organisations to innovate faster while maintaining control, compliance and confidence.
“Generation” tools for ML training data
As synthetic data becomes essential to enterprise AI adoption, a wave of machine learning–based data generation tools has emerged. These tools use algorithms to replicate the statistical patterns of production data, offering a safer, privacy-conscious alternative to using real datasets.
However, tools focused solely on statistical similarity often miss the deeper complexity of enterprise data, its relationships, hierarchies and dependencies. Without data profiling, lineage tracking and referential integrity, the output may appear realistic but fails to behave like real data in production, leading to broken test environments, inaccurate training and compliance risks.
For LLMs, this gap is critical. Models rely on context-rich, diverse and logically connected data to capture language, behaviour and domain nuances. Data that only “looks” real is not enough, it must reflect real-world logic and cross-system dependencies.
Enterprises therefore need more than generation, they need precision, structure and control. This is where Curiosity’s Enterprise Test Data® platform takes synthetic data generation to the next level. Combining intelligent generation with deep data understanding, profiling, validation and governance to deliver synthetic data that’s both realistic and reliable across every stage of AI and software delivery.
Synthetic data generation with Curiosity’s Enterprise Test Data®
Curiosity’s Enterprise Test Data® platform transforms how enterprises generate, understand and deliver data, turning synthetic data into a driver of speed, quality and compliance.
By combining machine learning, advanced profiling and intelligent automation, Enterprise Test Data® builds a version-controlled “metadata catalogue” that maps every relationship and dependency across your whole ecosystem. These reusable definitions ensure every dataset is accurate, compliant and referentially intact, empowering teams to generate trusted data on demand.
Unlike standalone ML data generators, Enterprise Test Data® offers five complementary techniques to meet the full range of enterprise data needs:
- A centralised metadata catalogue: Bring order to data complexity. Provide a single source of truth for data definitions, relationships, dependencies and formats across your whole enterprise.
- AI acceleration and deep learning: Embed AI and deep learning to eliminate data challenges, remove bottlenecks and ensuring data is securely delivered to the right teams, exactly when they need it.
- Data design and modelling: Design and generate synthetic data that mirrors real-world systems, modelled business variations, and comprehensive equivalence classes. You can transform data creation from guesswork into a systematic, deliberate, coverage-driven process.
- Robust data governance: Provide continuous, AI-powered governance that protects data integrity and compliance across every environment.
- End-to-end data management: Eliminate bottlenecks with a comprehensive, end-to-end toolkit, combining synthetic data, cloning, masking, provisioning and virtualisation in one solution.
Together, these methods provide the flexibility and intelligence needed to cover every AI test case and business requirement, all while ensuring compliance and accuracy across complex environments.
As data management continues to evolve, Enterprise Test Data® remains at the forefront of AI & LLM data demands.
LLM training data without limits
Enterprises are embracing LLMs to automate decisions, accelerate development and drive new forms of intelligence across the business. LLM training, accuracy and reliability depends on one thing above all: high-quality, diverse and compliant data.
Curiosity’s Enterprise Test Data® platform unifies generation, governance and automation into a single AI-driven ecosystem, providing the intelligence, control and scalability needed to power next-generation LLMs. Teams can design, generate and provision complete datasets that mirror real-world systems with precision, ensuring every model is trained on data that’s as clean, fair and contextually rich as the enterprise itself.
As LLMs and AI co-pilots reshape how organisations use and understand data, synthetic data will define the future of responsible AI. Together, with Curiosity’s Enterprise Test Data®, you’re not just keeping pace, you’re building LLMs that learn faster, perform better and scale without limits.
How to turbocharge your data with AI
Join Curiosity's Head of Solution Engineering, Harry Burn and Solutions Engineer, Toby Richardson to see how AI can revolutionise your development processes and test data management.
