Synthetic data is fast becoming the smarter choice for organisations that need high-quality data without the risks of handling real-world datasets.
Unlike mock data, which is often manually created for testing scenarios and lacks depth, synthetic data is algorithmically generated to accurately mimic the properties of real-life data. This makes it a powerful tool for training AI models, testing applications, and driving business insights, but without exposing the organisation to the commercial, operational and compliance risks of working with real data.
Think of it as setting off on a demanding climb up a rocky mountain. If mock data is a pair of trainers with light tread, synthetic data is a pair of sturdy walking boots that are designed to handle the rough terrain ahead. Mock data may well be fine for the initial climb, but it lacks the grip and durability needed as the challenge becomes steeper.
It means that while mock data is perfectly fine for testing and basic use cases, it simply isn’t optimised to train mission-critical AI systems or support major business decisions. Synthetic data, on the other hand, is engineered for real-world complexity because it mirrors the patterns and depth of actual data, ensuring AI learns from realistic examples and ensuring that strategic insights are built on a solid foundation.
And this difference matters, especially for CIOs assessing their data strategies.
But the point with synthetic data is that your approach needs to be well-executed. While a quality-first synthetic approach can accurately train AI models and enable confident insights, poorly designed synthetic data can introduce bias and distortions, leading to flawed models, unreliable insights and failures.
So while we advocate for CIOs to be as enthusiastic about the potential of synthetic data as we are, the approach used to generate, validate and implement it must be fully scoped and precise.
Governments, financial institutions, and technology firms are already adopting synthetic data to overcome the limitations of real datasets. And some of the world’s most ambitious implementations have emerged here in the UK.
For example, the Office for National Statistics (ONS) has been at the forefront, exploring synthetic data to enhance accessibility while still safeguarding privacy. In a pilot study, the ONS investigated the demand and requirements for synthetic datasets, aiming to produce data that closely resembles real information without compromising confidentiality. This initiative has paved the way for safer data sharing and more robust statistical analyses, making it easier for policymakers to explore economic trends without running into regulatory roadblocks.
In the financial services sector, Lloyds Banking Group has embraced synthetic data to bolster fintech innovation. One early success has been the training of an advanced system that analyses vast amounts of transaction data to identify unusual patterns indicative of fraudulent activity. This proactive approach not only enhances security but also streamlines compliance processes and operational surveillance, ensureing a safer financial environment for its staff customers.
And Smart Data Foundry, a not-for-profit subsidiary of the University of Edinburgh, helps policymakers to access high-quality data without the privacy risks and regulatory hurdles of using real customer records, providing access to a viable alternative platform in an environment where strict data regulations can often slow down innovation.
The effectiveness of synthetic data depends on how accurately it is generated and validated. It is created using statistical modelling and machine learning, where AI analyses real-world data patterns to produce synthetic versions that reflect key characteristics without containing real personal information. But this process is not without its challenges.
Ensuring the accuracy and reliability of synthetic data requires rigorous validation. CIOs must ensure that synthetic datasets are properly compared against real-world data to confirm that statistical properties, distributions, and relationships remain intact. If synthetic data fails to reflect genuine patterns, AI models trained on it may produce flawed predictions and unreliable insights.
Bias is another concern. If the original data contains bias, the synthetic version can inadvertently replicate or even amplify those biases. Without careful oversight, this can lead to skewed insights, unfair decision making, and compliance risks. To mitigate this, leaders must implement robust quality control measures, including fairness testing and continuous monitoring.
Despite these challenges, the growing adoption of synthetic data reflects its potential to unlock new opportunities while maintaining privacy and regulatory compliance. When implemented correctly, it provides a scalable, flexible, and ethical solution for AI development, business intelligence, and secure data sharing. But its success depends on a structured approach, one that balances innovation with responsibility, ensuring synthetic data remains a powerful asset rather than an overlooked liability.
By following these steps, CIOs can confidently navigate the complexities of synthetic data, ensuring their organisation reaps the full benefits without compromising accuracy, compliance, or security.
Synthetic data isn’t just an alternative to real-world datasets, it is an enabler of innovation, security, and efficiency in AI development and business intelligence. But its success depends on precision and taking the right approach to help unlock the range of benefits it brings. Put simply, synthetic data is already transforming industries, so now is the time to identify use cases and start small scale PoCs. The leaders who act today will be the ones benefitting from tomorrow’s wave of data-driven progress.
Synthetic data provides a secure, risk-free alternative to real datasets for AI training, analytics, and business insights. Unlike mock data, it accurately mimics real-world patterns while ensuring privacy and compliance. Used in finance, government, and technology, it enables innovation without regulatory hurdles. Success depends on rigorous validation, bias control, and strategic implementation.
DeepSeek’s open-source AI model offers CxOs a low-cost, secure alternative to proprietary AI, enabling innovation, financial control, and responsible deployment while supporting public and private sector digital transformation.
Scrumconnect drives UK apprenticeships, hiring quarterly and advancing AI talent. Partnering with DfE, it's shaping digital solutions to develop future skills.
Scrumconnect is pleased to announce its successful award of G-Cloud 14 Lot 4 Framework Contract.
We are committed to continuously providing the best in tech with our people, practices and technology through our certifications.
Scrumconnect is committed to disability rights.
This enhanced government-endorsed standard demonstrates that we have implemented the essential plus security requirements for protecting your data, and that of your client’s, against unskilled internet-based cyber attackers.
We are a supplier on Crown Commercial Service’s (CCS) Cloud Compute 2 framework, a testament to our commitment to providing high-quality services to the UK public sector.
An integrated management system, designed for ISO 9001:2015 and ISO 27001:2015, operates across our business. These are regularly reviewed in order to ensure the continuous improvement to meet the needs of our customers and stakeholders.