5 Benefits of Synthetic Data You Cannot Ignore

AI needs data to learn and predict results. But often collecting and compiling data can be pricey and time-consuming. Also, real data could be biased, imbalanced, and unusable due to privacy regulations. To bypass these limitations, AI proponents and deep neural network developers are now banking on Synthetic Data.
The blog below lists all the prime benefits of synthetic data.

Breaking Down Synthetic Data

Synthetic data is an artificially generated class of data that works as an effective alternative to real data. Contrary to the real data collected from the real world, synthetic data is “made-up” information generated by computer simulations or algorithms. Although synthetic data can be artificial, it reflects real-world data both statistically and mathematically.

Real Data	Synthetic Data
Derived	Generated
Collected from real-world sources and natural settings	Produced through various computer algorithms and models
Original data	Emulates statistical properties of original data
Might carry sensitive information	Mimics real data yet without sensitive information
Availability based on existence of relevant datasets	Generated as per demand

When comparing synthetic data vs real data, multiple researchers have confirmed that synthetic data is as reliable - or in some cases- even better for training an AI model. Join our machine learning certification course to learn more about synthetic data.
Most organisations are leveraging synthetic data to enhance small, existing datasets. Plus, synthetic data can make it easier to test AI models when real data is inaccessible, classified, or shifted. Also, synthetic data is highly useful in testing a new system when either no live data exists or when data is biased. In fact, by 2030 synthetic data will completely overshadow real data in AI models (Source: Gartner).

Undoubtedly, synthetic data hold immense possibilities and benefits. Let’s dive into the synthetic data benefits in the section below.

Advantages of Synthetic Data

Among the top benefits of Synthetic Data, data privacy and data security claim the top berth. Plus, artificially accumulated data is safe and completely anonymous. Below we have listed the key reasons behind the rising popularity of synthetic data in the current times.

1. Data Privacy and Security

Data privacy and synthetic data go hand in hand. With synthetic data, organisations can create alternative datasets, mimicking the properties of real data without sharing sensitive information. It helps organisations in analysing and testing without risking exposure to private data.
In industries like healthcare and finance, where privacy and data security are highly important, synthetic data generation could be immensely helpful. Data scientists can use synthetic patient data for research and development without compromising patient confidentiality. Similarly, financial institutions can model transactions and customer behaviours without using actual customer data.


2. Cost-effectiveness

Cost efficiency is one of the key benefits of adopting synthetic data.
Acquiring, cleaning, and sorting real data is expensive. However, synthetic data generation is significantly cheaper compared to real data. With synthetic data, organisations, especially startups and small businesses, can cut down on operational costs big time.


3. Data Diversity and Scalability

Real data does not always cover all the possible scenarios, thereby leading to biased models and limited insights. Collecting sufficient real-world data can be difficult, impacting the accuracy of the models.
Synthetic data, on the other hand, assures excellent data diversity and scalability. Banking on synthetic data, industries can create large-scale, varied datasets that push the boundaries of innovation.


4. Rapid Prototyping and Testing

Synthetic data reduces time spent on data collection and preparation, thereby expediting the entire designing and testing phase. With this data, developers can quickly identify the flaws in their systems and refine their solutions efficiently.


5. Overcoming Data Limitations

Often when data is limited, synthetic data can provide additional samples to improve the model’s performance. Since synthetic data resembles data derived from real-world patterns, it can address the problem of information shortage.
Business Success Stories: An online language learning platform could use synthetic data to simulate conversations in less commonly spoken languages. This customised approach would help to enhance the user experience and attract a broader audience, even when authentic data is scarce.

Challenges and Considerations

However, synthetic data is not free from limitations. Critics raise the question of accuracy of synthetic data in representing real-world scenarios. Oftentimes researchers resort to advanced techniques like generative adversarial networks (GANs) and domain adaptation to make up for the authenticity of the data . These methods aim to create synthetic data that closely mimics the statistical properties and patterns of real data.
Another limitation of synthetic data is ethical implications. Researchers have pointed out that when synthetic data is used to replace or manipulate real data, it largely happens without proper disclosure. This could lead to skewed results or misleading conclusions.

Final words

Despite the challenges, synthetic data is fast gaining traction given its long list of benefits of real data.
Synthetic data resolves many glitches, including data privacy and security. Since artificial data mimics real-world patterns while ensuring individual anonymity, it helps to ensure the privacy of the subject. Plus, synthetic dataset generation is cheaper and easy to procure.
Speaking about the future, experts have predicted the rise of generative ai synthetic data in the coming times. The Gen AI integration will help to produce richer or fairer versions of real-world data, thereby improving the quality of synthetic data. Businesses will increasingly rely on synthetic data to reduce costs, enhance privacy, and overcome data limitations, resulting in improved decision-making and innovation. Sectors like healthcare, finance, and manufacturing will benefit from more accurate simulations and models, driving innovation and efficiency.