Introduction to Artificial Intelligence and Data
Artificial intelligence is set to transform organisations across all industries, but access to quality data represents one of the key barriers to success. Data has been the main fuel for the digital era, but in today’s AI-powered world, it’s more like the engine – driving intelligence. The organisation that has the largest volume, best quality and most unique data is going to be able to create the more powerful and accurate AI applications.
What is Synthetic Data?
Synthetic data refers to data which has been created artificially. It is an approximation of real-world data, replicating its characteristics based on true attributes, but excludes anything that could distort results or be personally identifiable. It accurately reflects the characteristics of real-world data and comes in different formats, including structured (artificial database tables, client records), unstructured (text, images, videos) or even synthetic users.
Today’s Data Obstacles
For many organisations the pathway to utilising AI applications is littered with data related challenges:
- Privacy and regulatory issues – GDPR and general sensitivity around data privacy makes it hard to get hold of, and use, many forms of data for AI model development.
- Data scarcity and quality issues – AI applications need vast quantities of data and in specialised industries, or for rare events, there might not be available data.
- Cost and feasibility barriers – Collecting, sorting and tagging real world data can be expensive and time consuming, which can delay AI projects.
- Inherent biases – unintentional biases can often be found in real world data, which can have an impact on reputation, or other outcomes, if it manifests.
How Synthetic Data Helps
Synthetic data can be generated based on preexisting real world data but without using any personal or private information. By maintaining any statistical or other common attributes it can act the same as real world data but overcomes restrictive legal hurdles and ethical dilemmas.
Overcoming Privacy Challenges
Synthetic data can be used in regulated industries where data protection requirements are high. As synthetic data is essentially anonymous, it isn’t subject to any ethical and confidentiality constraints. In healthcare, patient data is heavily regulated under laws like HIPAA and GDPR, making it challenging to use real world datasets for research, AI model development, or clinical decision support. Hospitals and research institutions are turning to synthetic data as a solution – creating statistically accurate, yet entirely artificial patient records that mirror real world clinical scenarios without exposing any personal information.
Addressing Data Imbalances
In specialised industries or for rare events, there may simply not be enough real data available so synthetic data can supplement these gaps. This could cover scenarios such as under-representation from a particular group, mimicking an unusual event or creating test scenarios that would be unlikely to happen frequently enough to have good data for. Real world data can often have inherent attributes which lead to unfair or inaccurate outcomes, potentially causing financial harm and reputational damage. Synthetic data can be created to balance out shortfalls, giving a more representative dataset.
Cost-Effective
For many organisations acquiring real world data can be prohibitively expensive. The process of collecting, sorting, and tagging data for AI training is often time-consuming, complex, and resource intensive. In contrast, synthetic data offers a cost-effective, predictable alternative. For businesses with limited budgets, it removes the upfront need for large-scale data collection and preparation, significantly reducing costs. The result being a more streamlined path to testing and deploying AI solutions.
Synthetically Powered AI is Here to Stay
For organisations striving to harness AI’s potential, synthetic data represents a pivotal solution to overcoming many of the barriers that slow development down. It addresses privacy and compliance challenges, fills critical data gaps, reduces costs, and helps eliminate biases – all while accelerating model training and validation. The market momentum is clear. Gartner predicted that by 2024, 60 per cent of the data used for AI development will be synthetic and have suggested that synthetic data will likely overtake real data by 2030 as the dominant resource for AI model training.
Conclusion
Synthetic data is transforming how businesses develop and implement AI technologies. It is becoming the unsung hero of AI development, particularly for organisations with limited access to data or struggling with privacy, regulatory, or cost barriers. By embracing synthetic data early, organisations will be better positioned to develop robust AI capabilities, deliver faster innovation, and remain compliant in an increasingly regulated environment.
FAQs
- What is synthetic data?
Synthetic data refers to data which has been created artificially, replicating the characteristics of real-world data. - Why is synthetic data important?
Synthetic data is important because it helps overcome many of the barriers that slow AI development down, such as privacy and compliance challenges, data scarcity and quality issues, and cost and feasibility barriers. - Can synthetic data replace real data?
Synthetic data won’t replace real data entirely, but it will become an essential tool – enabling businesses to unlock AI’s potential at speed, scale, and lower cost. - What are the benefits of synthetic data?
The benefits of synthetic data include overcoming privacy challenges, addressing data imbalances, and being cost-effective. - What is the future of synthetic data?
The market momentum is clear, with Gartner predicting that by 2024, 60 per cent of the data used for AI development will be synthetic, and synthetic data will likely overtake real data by 2030 as the dominant resource for AI model training.
