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We’ve all seen those scenes from sci-fi movies where a scientist runs a program and then an artificial intelligence (AI) model magically appears to provide some mind-blowing results. It would be great if we could simply ask our AI model to provide us with output without having to first provide it with a data set. However, this is not how AI works. In order for an AI model to provide us with the desired product, it must first be trained on a data set. Data is a critical component of AI and deep learning that provides the foundation needed to create robust AI and predictive models. Sometimes, real data can be insufficient or inaccurate. To make effective use of data, it is imperative to conduct a thorough qualitative control and quantitative assessment. In most cases, the lack of quality resources is identified as the main obstacle to successful data-based decision-making.
So, what happens when we don’t have enough data to train our AI model? This is where synthetic data comes into the picture. Synthetic data is artificially generated data, created to mimic the behaviour of live data, and can be used to train an AI model. In fact, according to Gartner, AI and data analytics projects will employ 60 per cent of synthetic data by 2024, and by 2030, AI models will totally replace real data. It can be generated in the digital world in numerous ways such as computer simulations, statistical modeling, etc., but the most common method is to use algorithms to generate data that resembles real-world data. The goal is to take advantage of existing information or simulate new states, where some of this can have huge strategic value. Augmented data, at the same time, is real data that has been enhanced with additional information. This can be used to improve the quality of the data or to add more context to it. Both synthetic and augmented data have their own advantages and disadvantages, but ultimately, they both serve the same purpose: to provide us with better data to work with.
Synthetic data: a must-have for organizations?
According to McKinsey’s ‘State of AI‘ study, 49 per cent of high-performing AI organizations are already utilizing synthetic data to train their AI models. Synthetic data being the creation of another dataset, is designed to be more similar to a real dataset. This is often used by companies to create more efficient AI models for predictive analytics. There are several reasons why companies are making a drastic shift towards the utilization of synthetic data for training AI models:
- In certain cases, due to a number of variables, it might be difficult or even impossible to find high-quality, real-world data. Therefore, synthetic data can help overcome this issue of limited data sets.
- Synthetic data can be generated specifically for our AI model. This means that it will be more effective than the real-world data, which might not be relevant to our specific model. Thus, utilizing synthetic data gives users more control over the input factors, which can result in more precise outcomes.
- It can help us save time and money by avoiding the need to collect and label. Synthetic data also makes the entire process faster as the barriers of privacy and the security protocols that frequently make obtaining and using data time-consuming; are removed with synthetic data. However, it is important to remember that synthetic data is still just an approximation of reality.
Risks with synthetic data
There are several practical use cases for synthetic data in every possible business, and it has great promise. Academic research indicates that the greatest training results come from data sets with 90% synthetic data and 10% real-world data. Deloitte Consulting’s findings that an AI model trained using 80% synthetic data showed similar accuracy to a model based on real data further support this research. Despite these advantages, there are also some potential risks associated with synthetic data. One of the risks is that the synthesized data may not accurately represent the real-world phenomenon that it is supposed to represent. This could lead to inaccurate results from the model. Additionally, if the model is based on a biased or flawed dataset, then the resulting synthetic data may also be biased or flawed. Therefore, synthetic data does have the potential to improve the training of AI models, but caution is needed to realize their full potential in the field of artificial intelligence.
Way forward for synthetic data
As synthetic data becomes more prevalent, it will have a disruptive impact across industries. Moreover, it will play a significant role in transforming the economics of data. The quality training data needed to achieve competitive advantage will be more accessible, affordable, and consistent than ever before. We are living in an exciting time to be developing new ways to harness the power of data to drive better and sustainable innovations, where we can use our knowledge and know-how to be at the heart of an industry that will shape our future.