Mastering data management for AI: How synthetic data unlocks new possibilities

AI promises to transform how organisations operate.  But one critical issue is holding many technology leaders back from realising these benefits: data quality. Whether you're in the public or private sector, messy, fragmented or missing data blocks AI adoption more than any other technical challenge.  

The good news is that synthetic data offers an immediate, practical solution.  One that unlocks innovation while preserving the highest standards of control and compliance. 

The data challenge that's holding you back 

Organisations face three critical data problems when deploying AI.  

The scale of this challenge is urgent.  30% of all generative AI projects are expected to be abandoned by the end of 2025, primarily due to poor data quality, inadequate risk controls and unclear business value. Compounding this, 42% of organisations identify a shortage of skills and staff as the biggest challenge to achieving high data quality, directly impacting AI readiness. While a lack of skilled professionals is usually a top-cited barrier, data quality and integrity issues are consistently highlighted across the industry as critical obstacles to scaling AI initiatives. 

These aren't abstract concerns. Without access to high-quality data, AI models fail to deliver the reliable outcomes needed to justify investment and scale with confidence. 

How synthetic data is a game-changer for AI development 

Synthetic data is artificially generated information that mimics the statistical properties and patterns of real-world data without containing actual personal or sensitive information. Unlike mock data, which typically consists of simplified ‘dummy records’ created for basic testing, synthetic datasets preserve the complexity of true relationships such as correlations between age demographics and service use or fraud risk and transaction type.   

This realism opens new doors. You no longer need to wait months or years for real-world data to materialise. Synthetic data provides immediate access to rich, nuanced datasets, allowing you to accelerate your AI journey from months to weeks without compromising on privacy. 

According to Straits Research, the global synthetic data generation market will reach $4,630.47 million by 2032, growing at over 37.3% CAGR.  But Gartner warns: 60% of data and analytics leaders will face critical failures.  A reminder that implementation matters as much as strategy. 

Privacy and compliance by design 

Privacy and compliance are non-negotiable. Fortunately, synthetic data supports regulatory alignment:  

Crucially, with 2i’s approach, synthetic data generation happens entirely within your environment, ensuring full control over your assets and reducing reputational risk.  This gives you the ability to test new use cases with complete confidence without compromising individual privacy or exposing live data. 

Beyond records: synthetic people, biometrics and real-world use 

At 2i, we’re going one step further.  We’re generating synthetic people including faces, voices, fingerprints and even DNA profiles which open new possibilities for testing and development. 

These synthetic biometrics allow organisations to test identity verification systems, train facial recognition algorithms and develop voice-activated services without the privacy concerns and consent complexities that real biometric data presents. For public sector departments, this means the ability to test citizen-facing services more thoroughly and maintain the highest standards of data protection. 

Real-world applications are already proving valuable. Law enforcement agencies are using synthetic data to share sensitive information externally for research and development, preserving privacy and complying with legal constraints. Government departments use synthetic data to simulate traffic flows, model hospital capacity and test welfare policy changes, all without risking exposure of real citizen data. In financial services, institutions leverage synthetic data for risk modelling, fraud detection and algorithmic trading, enabling robust model validation and bias mitigation without exposing real customer data. The UK's Financial Conduct Authority has even established a Synthetic Data Expert Group to explore responsible innovation and data sharing in financial markets. 

Don't wait for perfect data – start with what you can control 

The biggest mistake organisations make?  Waiting for perfect data. This perfectionist mindset causes paralysis.  Teams stall, investing months, even years, in data cleansing and delaying innovation. 

Start small.  Start now.  Use synthetic data to run pilot projects, build capability, generate insights and continue your long-term data quality improvements. 

Partnering with experts who understand both the technical and compliance aspects of synthetic data generation ensures you can move forward with clarity and control.  

Why partner with 2i? 

We bring: