“The core advantage of data is that it tells you something about the world that you didn’t know before.” – Hilary Mason, data scientist and founder of Fast Forward Labs. The entire world is expected to generate 147 zettabytes of data each year, and this figure will continue to rise. Data scarcity is a genuine problem even though we generate zettabytes of data yearly. In our quest to uncover the unknown, synthetic data becomes essential.

The immediate question that arises is, if we are generating so much data, isn’t it contradictory to say there is data scarcity? This blog covers this and other questions on synthetic data. First, we’ll define what data scarcity is. Next, we’ll explore synthetic data to answer what, why, and how. Finally, we’ll discuss how synthetic data can be used to develop innovative AI technologies. 

What is data scarcity?

Data scarcity refers to the inadequacy of available data volume required for meaningful analysis or effective training of AI and machine learning models. It’s characterized by insufficient data to achieve desired outcomes, which hinders the development of robust insights and models.

To cope, companies are exploring alternative data sources but risk getting poor-quality data. This data quality issue has long plagued machine learning (ML), with poorly labeled data, biases, and inconsistent formats leading to subpar results. Moreover, the process of cleaning and managing huge datasets can be costly. The world is generating hundreds of zettabytes of data, still why is there data scarcity? 

Why is there data scarcity?

Privacy laws are getting stricter worldwide to prevent data misuse, with the EU’s GDPR being a prime example. Additionally, OpenAI faced challenges in Italy, accused of privacy breaches and limiting its operations there. Other EU nations like Spain, Germany, and France are also scrutinizing OpenAI, potentially restricting data access. Here are more factors contributing to data scarcity:

High cost and logistical challenges: Collecting, labeling, and managing large datasets can be expensive and difficult, especially for smaller organizations or niche applications.

Rarity of occurrences: In specialized fields like rare disease diagnostics or wildlife conservation, relevant data points are scarce, making it challenging to accumulate sufficient datasets.

Privacy and regulatory concerns: Strict regulations, particularly in sensitive sectors like healthcare, limit the availability of shareable data.

Imbalanced datasets: Real-world datasets often suffer from class imbalance, where certain categories or labels are underrepresented, exacerbating data scarcity.

Emerging technologies: The rapid evolution of new technologies frequently outpaces the availability of appropriate training data.

Now that we understand data scarcity and its causes, let’s explore an example of how it can impact one of the most compliance-heavy sectors, BFSI, and how Onix solutions can help address this challenge.

Data scarcity post-EDW migration in BFSI industries

Post-EDW migration in highly regulated sectors like BFSI (Banking, Financial Services, and Insurance), data validation is critical but challenging due to regulatory constraints. Furthermore, it’s often against regulations to transfer data for testing purposes, hindering thorough validation post-migration. Using synthetic data is one method of bridging this gap. Synthetic data mimics real data characteristics without containing sensitive information, facilitating robust testing and validation post-migration.

The concept of synthetic data addresses the need for data in situations where real data is scarce, ensuring compliance with regulations while enabling essential testing and validation processes. 

With real-world data becoming scarcer due to various challenges, such as formatting issues and privacy regulations, generative AI shows promise as a remedy. To realize how big and important synthetic data is, check this report from gartner which says 60% of AI data will be synthetic by 2030. Let’s explore what exactly synthetic data is in the next section. 

Synthetic data is completely fake but authentic data

Imagine you’re developing cutting-edge AI applications, but you’re faced with a shortage of real-world data due to privacy concerns or limited availability. What happens when the data you need is scarce or locked behind privacy laws? The solution is synthetic data. Data created artificially by computers to supplement or replace real data in order to enhance AI models, safeguard sensitive information, and eliminate bias is known as synthetic data. 

Synthetic data mirrors the statistical patterns and properties of real-world data without containing any sensitive or personally identifiable information. Furthermore, this means you can simulate vast amounts of data on-demand, overcoming the constraints of traditional data collection methods.

An enterprise might have sufficient resources to use original data for training new machine learning algorithms or AI models. However, statistics clearly show that synthetic data offers several advantages over actual data. Let’s explore why synthetic data is dependable.

Synthetic data is powerful and dependable

Data quality and technical complexities in synthetic data generation present challenges, including accuracy trade-offs and stakeholder skepticism. Despite these hurdles, the benefits of synthetic data outweigh its challenges, making it a powerful tool for modern analytics and AI applications. Here are some reasons why use of synthetic data is growing. 

Endless supply of customizable data

The biggest challenge in technology or science is that any invention or experiment should be scalable and repeatable. So if a computer can generate data, it means you can keep generating it. Additionally, this makes it possible to create data on demand that is tailored to precise requirements. Synthetic data can be produced in nearly limitless quantities using methods like computer simulations and generative AI models, such as transformer-based foundation models, diffusion models, and GANs. Large volumes of realistic data, including pictures and movies, can be produced by these models.

Faster AI model training

Data required to train an AI model may be scattered across multiple sources, creating roadblocks and compliance issues that delay the training process. AI model training can be completed faster and for less money by using synthetic data instead of real-world training data.  By using synthetic data, companies can expedite the training of AI models, making it faster and cheaper. Synthetic images can be created using simulators and generative AI to pretrain models effectively. This method also helps in reducing biases and enhancing the performance of AI models.

Adding more variability to datasets

Synthetic data is essential for scenarios where real-world data collection is impractical or impossible, such as in self-driving cars or customer-care chatbots. Algorithms like LAMBADA can generate synthetic sentences to fill chatbot knowledge gaps, enhancing their ability to understand and respond to a wide variety of customer requests. This attribute is crucial for enhancing AI systems’ performance in a variety of uncertain circumstances.

Mitigating risks and bias

Synthetic data can be used to test AI models for security flaws and biases. Tools can generate counterfactuals to identify and mitigate biases in AI models, helping create balanced datasets. This reduces the risk of models making discriminatory decisions and improves their overall robustness. By generating artificial data that is pre-vetted and free from biases, companies can ensure that their AI models are more accurate and fair, ultimately leading to better and more reliable outcomes. 

From the previous sections we understand that In addressing the insatiable demand for data in AI and machine learning, synthetic data emerges as a pivotal solution. Moving ahead, let’s learn about the three key methods used to generate synthetic data.

Here are three breakthrough methods used to generate synthetic data

Synthetic data is generated using computational methods and simulations to mimic statistical properties of real-world data, but without real observations. It can be text, numbers, tables, or complex types like images and videos. There are three main methods:

1. Statistical distribution: Real data’s statistical distributions (normal, exponential) are analyzed, and synthetic samples are generated to resemble the original dataset.

2. Model-based: Machine learning models replicate real data characteristics to generate artificial data with matching statistical distribution. This is effective for hybrid datasets.

3. Deep learning methods: Advanced techniques like GANs and VAEs generate high-quality synthetic data, especially for complex types such as images or time-series data.

Some key points to remember about synthetic data

• Synthetic data is created using statistical modeling, machine learning, and GANs.
• It mimics the mathematical properties and patterns of real-world data.
• It excludes private, sensitive, or personally identifiable information (PII).
• Synthetic data supplements scarce real-world data, ideal for privacy-constrained domains.

Key takeaway

Data scarcity persists due to stringent privacy laws, escalating costs, and other constraints, hindering access to sufficient real-world data for analytics and AI. Synthetic data emerges as a transformative solution, offering data that mimics real-world characteristics without compromising privacy. It’s readily available on demand, providing enterprises with a safe and versatile alternative. 

The benefits of synthetic data with Kingfisher, such as scalability, cost-effectiveness, and compliance with regulations, outweigh its challenges. As enterprises increasingly rely on data for insights and advanced AI applications, synthetic data is poised to revolutionize the tech landscape.

Data scarcity limits innovation and synthetic data offers a path forward, enabling safe experimentation and robust AI development.