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Data powers the modern world. From healthcare to finance, transportation to retail, data is the backbone of everything we do. This is only amplified in the age of digital transformation with the rise of new, game-changing technologies intrinsically powered by data. Its importance cannot be overstated, especially in an AI-centric world where algorithms rely on vast amounts of training. These algorithms are only as good as what they are trained on, making high-quality data invaluable.
That being said, achieving this is no mean feat. Organisations are finding it increasingly difficult to collect and utilise real-world data. Data privacy and security are the subject of growing concern. Moreover, wider issues present themselves throughout the data lifecycle, particularly with sourcing and processing, whether that be regarding the volume needed, feasibility of data generation or related expenses.
Data can be messy, incomplete, or otherwise difficult to work with. In particular, a lack of consistency or accuracy can make it difficult to draw meaningful insights and make informed business decisions. In an age where businesses try to foster a culture of data-driven decision making, empowering teams to use data to inform their work, having complete, representative datasets is non-negotiable.
Real-world data can be limited in its diversity, particularly if it is collected from a single source or in a specific context, which can often be the case due to time or budgetary restraints, particularly for large-scale projects. So, while it is often regarded as the ideal source, it can be accompanied by a number of problems including privacy issues, cost, and data scarcity. There is a clear gap for a better solution that works with real-world data to provide the desired results.
Generated by a computer simulation, synthetic data is able to mimic features of real-world data, statistically and structurally matching it. It can help organisations generate masses of training data without risking privacy and security, saving them time and money in the process. Not only that, it can also create more diverse and representative datasets, helping to avoid the pitfalls of bias that can come with real-world data alone. But how does it do this?
How synthetic data can fuel a data-centric world
While at first glance, it may seem counterintuitive to use artificially generated data in lieu of ‘the real thing’, synthetic data enhances real world data, offering a multitude of benefits positioning it as an invaluable resource for organisations looking to optimise their data practices.
By working with real-world data, synthetic data is able to elevate an organisation’s practices. As discussed, real-world data can contain sensitive details, such as personally identifiable information (PII), which must be kept secure, especially in the case of financial statements or ID documents. Synthetic data offers a solution by generating datasets that preserve the statistical
properties of the original data while mitigating privacy concerns by eliminating the need for PII altogether.
Furthermore, synthetic data can be generated in large volumes, quickly and inexpensively. In some fields, there may be a scarcity of real-world data available for analysis. This can be for a number of reasons. Sometimes it can simply be unfeasible to collect data, which is the case in emerging areas such as autonomous vehicles. Other times, dedicated time needed for comprehensive annotation can be impractical.
Synthetic data, however, can be generated to meet specific criteria, using a wide range of scenarios to ensure that it is also consistent and representative of corner cases. This can allow users to test and refine models without incurring the high costs associated with data collection and allow them to apply their efforts more productively.
Industries that can benefit from leveraging synthetic data
All sectors can probably stand to gain from deploying synthetic data. Some that have already begun leveraging its advantages include the automotive and retail industries. These fields require masses of data to ensure training is representative of the users and scenarios that the AI will encounter. Synthetic data eases the process, providing diverse datasets at a fraction of the time.
The automotive industry was one of the first proponents of synthetic data, and its focus was largely centred around autonomous vehicles. However, more recently, this scope is expanding to other areas such as in-cabin monitoring and number plate recognition. These technologies rely on accurate identification and the ability to detect corner cases in the event of accidents, for example, making synthetic data the ideal, practical solution to address these needs.
Retailers are making use of synthetic data across a number of applications including inventory management, point of sale monitoring and forecasting. It is a key driver of business success, enabling hidden insights to inform decision making. In sectors such as finance and healthcare, where PII is integral, synthetic data can be leveraged to ensure data security and establish trust with clients. The potential applications are virtually limitless.
The advantages of synthetic data over traditional real-world data alone make it an indispensable tool in modern data practices. Its ability to save time and costs, enhance privacy protection, and offer data diversity is transforming the way we approach data-related challenges. Real-world data still has a role to play and there is no taking away from its integral contribution to training AI systems. We can expect the two to supplement one another for even more innovative uses in the future.