In the modern data-driven world, the adoption of synthetic data is rapidly increasing. But what is synthetic data, and what are the driving factors behind this widespread adoption? Where do we use synthetic data? What are the pros and cons? This article covers everything you need to know about synthetic data.
Real vs. synthetic data
Real data can also be referred to as real-world data. These are data collected from real-world events or events triggered by real-world scenarios that produce relevant data to the event that had occurred. Examples of real-world data include data collected from users through social media platforms, data collected from users via their smart devices (watches, phones, etc), and data collected by online retail stores – to provide customized recommendations and products. These data can be active or passive, based on how they are collected.
Active data collection is where the users consciously provide information, such as during an online survey or delivery addresses and contact details on online stores. Passive data are the kind of data that are collected mostly based on user interactions across various points that are spread across the platform or the website. For instance, the recommendation section on most websites is based on a user’s past interactions with certain topics/ products that were available on the website and its related content.
Now, when it comes to synthetic data, these data are digitally generated. This means the generated data actually mimics the baseline properties of the real-world data without needing a real-life instance to generate data. Data scientists consider synthetic data a highly promising alternative in many cases where real data has been previously utilized.
Use-cases of synthetic data
Synthetic data generation finds its usability in various forms across various verticals. For starters, training machine learning modules requires a massive amount of data so that the machine can identify patterns and gather insights from the provided data. In some cases, access to real-world data may not be provided to machine learning modules due to privacy-related concerns. In cases like these, synthetic data comes into the picture and fills in the void created by not having access to sufficient data to train machine learning modules.
Other examples of industries that benefit from synthetic data are banking, robotics, security firms, social media, advertising and digital marketing firms.
Pros and cons of synthetic data:
- Compared to real-world data, synthetic data is considerably cheaper. This is based on multiple factors, such as the requirement of a hardware device/software application and event-based triggers to produce data.
- Synthetic data generation is highly customizable and flexible enough to accommodate specific business use cases.
- Privacy concerns are no longer an issue when utilizing synthetically generated data, as the data only replicates the underlying pattern of the real data but not the data itself.
- Rather than waiting for events to happen in real life and then collecting data and process it, synthetic data can be made available in a relatively shorter period of time. With more advanced tools, this process can be even more optimized.
- The basic purpose of creating synthetic data is to replicate real-world data. Since this data is being used to train AI/ML models and develop applications, among other things, it is important to check the correctness of the data. This becomes even more complex if the data produced is in bulk quantities. Any AI/ML module can produce only data as good as its source. Hence it is incredibly important to check the quality of synthetic data, which acts as the input data for AI/ML modules.
- Synthetic data generation is a relatively newer subject in the data realm. This means that most verticles need to be made aware of the advent of synthetic data. This would also result in delayed acceptance by the overall public, which would affect the possibility of massive adoption of synthetic data.
Popular synthetic data providers:
K2View provides an all-in-one synthetic data generation tool which can be used for quality testing, compliance checks, software development and, last by not least, training AI/ML data modules. K2View provides additional services on top of synthetic data generation, such as, a rules engine and dask masking services, to protect clients’ data. Their self-service platform provides complete control for the client to generate and integrate the generated synthetic data into CI/CD pipelines or ML modules.
Genrocket specializes in producing synthetic data for quality engineering and machine learning exclusively. They also provide a self-service platform, which is already being used by many SMEs. Moreover, the platform provides complete customer security by never utilizing or storing any of its customers’ data.
Tonic provides synthetic data generation service by mimicking their clients’ production data for various purposes like QA, testing and product development. Tonic provides best-in-market service by having a dedicated team that excels in synthetic data development and management while staying intact with compliance and maintaining data security.
It is evident that synthetic data is definitely the way forward for the future of innovation. But with more advanced and more complex piece of technology and tools being right around the corner, it is essential to look towards generating synthetic data in a much more optimized way rather than just using AI tools. Companies have to invest in training people to get more hands-on experience on AI and understand complex frameworks. This is just to ensure that the generated data is close to the actual data. But with synthetic data in the picture, the field of data analytics and data science is set to break unimaginable barriers and bring in new, more efficient ways to handle data.