In the digital age, data reigns supreme, serving as the backbone of all industries.
It drives informed decision making and strategy across a range of disciplines. And with the advent of new transformative technologies reliant on data, this significance has only grown stronger. Particularly in an AI-centric world, where algorithms are only as good as the data they are trained on, the value of quality datasets cannot be overstated.
However, obtaining and utilising real-world data poses considerable challenges for organisations. Concerns surrounding data privacy are on the rise, in addition to other obstacles that present throughout the data lifecycle; factors like data scarcity, feasibility of data generation and cost only further compound the complexity of the data acquisition process.
The shortcomings of real-world data make it arduous to extract meaningful insights to make informed business decisions. In an era where organisations strive to foster a culture of data-driven decision-making, having complete and representative datasets should be the standard. And while it is often considered the ideal source, the lack of diversity that can be found in real-world datasets, as well as the aforementioned costs, data scarcity, and privacy concerns reveals a clear gap for a better solution.
This is where synthetic data comes into play.
How synthetic data navigates the pitfalls of real-world data
Generated by computer simulations, synthetic data can replicate the statistical and structural features of real-world data. It enables organisations to generate the desired volume of training data without compromising privacy and security, whilst also saving valuable time and resources. Moreover, synthetic data helps create more diverse datasets, mitigating the pitfalls of bias that often accompany real-world data alone. Synthetic data works to enhance real-world data and offers a plethora of benefits that position it as an invaluable resource for organisations seeking to optimise their data practices.
Synthetic data elevates data practices by working to supplement real-world data, which often contains sensitive details such as personally identifiable information (PII), which must be kept secure. This is especially prevalent when dealing with financial statements or ID documents in the finance sector, for example. Synthetic data provides a solution by generating datasets that preserve the statistical properties of the original data while eliminating the need for PII, effectively addressing these concerns.
In certain fields, real-world data may be scarce and require significant investment. Similarly, comprehensive annotation of data can be very time-consuming. Synthetic data, on the other hand, can be tailored to a desired set of criteria, encompassing a wide range of corner cases. It can be generated rapidly and in large volumes, enabling users to test and refine models without incurring the high costs associated with data collection. It’s no question that synthetic data is sure to become an indispensable tool in modern data practices across a range of sectors.
Which industries are making use of synthetic data?
From manufacturing and automotives, to insurance and banking, virtually every sector stands to gain from leveraging synthetic data. In fact, some industries have already begun reaping the benefits. This is particularly the case in sectors requiring vast amounts of training data to ensure AI systems are prepared to encounter a range of scenarios, both common and infrequent. Synthetic data is able to provide diverse datasets in a fraction of the time, massively streamlining the process.
The banking and finance industries, for example, have been quick to deploy synthetic data. They are recognised for prioritising security standards for clients given how integral PII is to the livelihood of the sectors. Synthetic data can be leveraged to ensure privacy-compliance in building representative datasets and establish trust with consumers. It will undoubtedly continue to play an important role in the future of the industry, particularly as access to customer and transaction data becomes more restricted. In an age where people crave personalisation but also privacy, synthetic data can aid to bridge the gap between insight and innovation.
Retailers have also been making use of synthetic data primarily for training computer vision systems for object detection purposes such as inventory management. The scope has expanded to include a range of data-centric AI applications such as point of sale monitoring and forecasting. Enabling insights to be much more readily-available in order to drive decision making, synthetic data is becoming a key driver of success in the sector.
The potential applications of synthetic data are virtually limitless and its benefits are sure to transform the way in which data-related challenges are handled. This is not to take away from the integral role of real-world data. Instead, we can expect to see the two work hand in hand to drive innovation to the next level.
About the Author
Steve Harris is CEO of Mindtech. Mindtech develop unique solutions for AI enabled visual processing, democratising the availability of AI training data through the creation and management of synthetic data. Our Chameleon AI Platform provides everything required to create massive data sets for training of visual AI systems.
