Future of AI Training: Impact of Synthetic Data in Enhancing Data Quality & Availability

Today’s business runs on data to make informed decisions, drive growth, and stay competitive. However, collecting, labeling, training, and maintaining datasets for machine learning and artificial intelligence applications is not only a costly but also a time-consuming task. It becomes more tedious and expensive if there's no or partial access to the exact real-world datasets to generate data-driven decisions. Naturally, a poor-quality input results in poor-quality output; some early experiences with GenAI are a perfect example. Therefore, businesses need a solution that always requires complete, accurate, and timely data to deliver trusted outcomes. 

Moreover, despite the vast amount of data produced, much of it remains inaccessible, available in poor quality, or low in quantity for data science and analytics projects. This is due to strict data privacy, security, and compliance guidelines that make accessing and using real-world datasets even more challenging. With how AI has revolutionized the way we operate and make decisions, its capability to analyze vast amounts of data and automate complex processes is fundamentally changing countless industries. However, the effectiveness of AI implementation is directly proportionate to the quality of data it processes. Poor data quality can undermine AI systems, leading to inaccurate predictions, flawed decision-making, and diminished trust in AI. 

Generating synthetic data has come as a savior for many enterprises. What happens in generating synthetic data is that it artificially develops and simulates real-world events and patterns. This allows users to have the information, and predictive modeling of real-world data without exposing confidential information helps preserve data security. Subsequently leads to faster turnaround time for development workflows and projects while still allowing researchers, analysts, and decision-makers to gain valuable insights.

But what is synthetic data, how does it work and how can it benefit your business? Let’s understand how the emergence of generating synthetic data is redefining the way organization data collect, extract, and store data for further innovation and digital transformation.

Synthetic Data 101: What is It, How It Works, & What It’s Used For

According to AWS, “it’s non-human-created data that mimics real-world data and is created by computing algorithms and simulations based on generative artificial intelligence technologies.” Simply put, synthetic data is computer-generated and artificially generated data with the help of algorithms running on the input of real data or manually created data, which you want to emulate. Synthetic data generation systems are designed not only to improve AI models, but also to protect sensitive data, and mitigate biases associated with manually developed data.

So how does it work?

Synthetic data generation is powered by deep generative algorithms. The synthetic data algorithm first learns the patterns, correlations, and statistical properties of the sample data. Once it’s trained, it generates identical, synthetic data with the same mathematical properties as the actual data it is based on. These algorithms use data samples as training data, to learn the correlations, statistical properties, and data structures. Once trained, the algorithms can generate data, that is statistically and structurally identical to the original training data, however, all of the data points are synthetic.

The reasons organizations or engineers generate synthetic data are many, the primary being the scarcity of real-world data. However, synthetic data has emerged as a promising solution. Designed to closely mimic the statistical properties of real-world data, it can provide the necessary volume for training AI models while ensuring the inclusion of diverse data points. Moreover, synthetic data subjects look real, but they may not contain any of the same information, but the data looks, feels, and means the same as the original sample the algorithm was trained on.

As discussed earlier, it’s a win-win for businesses that need to deal with sensitive data and can operate without worrying about the privacy issues or costs associated with collecting, extracting, or storing large volumes of real-world data.

How Many Types of Synthetic Data Are There?

According to Amazon Web Services, there are generally two main types of synthetic data that are being generated. Namely:

• Partial synthetic data: Partial synthetic data is generated by replacing certain parts or variables of the original dataset with synthetic values. This approach preserves some real-world data elements, making it useful when you want to protect sensitive information but still retain some original data for analysis. For example, only confidential fields like names or financial details may be replaced with synthetic data, while other fields remain real.

• Full synthetic data: Fully synthetic data is completely artificial, meaning the entire dataset is generated from scratch without any reliance on real-world data. It is created using models that capture the relationships and patterns in the original data but generate new, entirely synthetic examples. Fully synthetic data is often used when privacy is a primary concern, as it doesn’t contain any actual data points from the original dataset. For instance, it can help you generate images of cars in various driving conditions to help you train self-driving cars to recognize and respond to different scenarios without needing real-world photos.
• Hybrid Synthetic Data: This is a mix of both real and synthetic data. In this approach, some records are entirely real, while others are generated synthetically. Hybrid data is often used to balance the need for data privacy with the need to retain real-world data characteristics. It allows for more flexibility in use cases where partial real-world data is still required for certain analyses.

Top Features For Your Synthetic Data Generation System

There are various synthetic data creation tools available on the market today. Before making a final choice, make sure your selection includes these 8 key features:

1. Rules-based synthetic data creation augments existing test datasets when data is sparse that you can adjust or permutate to the existing data.
2. Ensure that it supports AI- and ML-based synthetic data creation; this is done to produce synthetic data on demand to enable speed and scalability.
3. The system must allow you to filter data to accurately segment it, that's emulation of data is a must.
4. Make sure the system ensures referential integrity, wherein data from one database matches with data to preserve data consistency wherever the information resides.
5. Ability to create datasets where no source data for AI/ML model training exists.
6. Connection with all databases and automation pipelines without disrupting your current infrastructure as well as your existing test automation and CI/CD tools.
7. It must let you test teams provision data all by themselves, independent of a centralized system that few can operate that boosts speed and agility.
8. GUI for synthetic data creation built into your test data management tools to avoid added costs and integration requirements.

Synthetic Data Use Case: Real-World Scenario

From healthcare to fraud detection systems, synthetic data has applications that have touched various industries and sectors that are leveraging it. Few examples:

• Health insurance company Anthem works with Google Cloud to generate synthetic data.

• American Express & J.P. Morgan are using synthetic financial data to improve fraud detection.

• Google’s self-driving car project “Waymo” has employed synthetic data for training its self-driving cars.

• Nvidia has developed platforms like Omniverse for creating synthetic data to train AI models.

• Amazon is using synthetic data to train Alexa's language system.

7 Common Problems with Real Data And How Synthetic Data Fixes Them

As AI and machine learning continue to transform industries, the demand for high-quality, diverse, and abundant data has never been higher. Real-world data, while valuable, often falls short of meeting the complex needs of modern AI systems. Synthetic data - artificially generated information that mimics the statistical properties of real data - has emerged as a powerful solution to these challenges.

#1. Data Scarcity

Many organizations face a critical shortage of real-world data, especially in specialized fields or for rare events. This scarcity can lead to undertrained models and poor performance in real-world applications. However, utilizing synthetic data generation produces vast amounts of training data, allowing AI models to learn from a much larger dataset than would be available using only real-world data. Thus, this abundance of synthetic data helps in creating more robust and generalized models.

#2. Privacy Concerns

Data privacy concerns and regulatory challenges have been consistent issues when using real-world data, especially for industries that deal in sensitive industries like healthcare or finance. When leveraging synthetic data, the issue can be managed. Since synthetic data is generated by mimicking the statistical properties of sensitive data sets without containing any actual personal information, this allows organizations to develop and test AI models without risking privacy violations or running afoul of regulations like GDPR or HIPAA.

#3. Bias & Lack of Diversity

Real-world datasets often contain inherent biases that reflect societal inequalities or data collection limitations. These biases can lead to AI models that perpetuate or even amplify unfair treatment of certain groups. When organizations generate synthetic data, it can be deliberately designed to represent diverse scenarios and underrepresented groups, especially for businesses that need to analyze data from regions or populations with limited information available. This can be done by carefully controlling the data generation process, thus developers can create datasets that are more balanced and inclusive, leading to fairer and more equitable AI models.

#4. Scarcity of Data

Organizations often face insufficient data with stricter privacy laws or, at times, how collecting, labeling, and managing large datasets seems to be costlier and difficult to store. This leads to difficulty in achieving desired outcomes, which hinders the development of robust insights and models. But with synthetic data generation, it’s not an issue, as it lets you produce data on demand and at an almost unlimited scale. They can also pre-label (categorize or mark) the data they generate for machine learning use cases. You get access to structured and labeled data without going through the process of transforming raw data from scratch. You can also add synthetic data to the total volume of data that you have, yielding more training data for analysis.

#5. Edge Cases & Rare Events

Real-world data often lacks examples of important but rare scenarios or edge cases. This can leave AI models unprepared for unusual but critical situations. With synthetic data, developers can engineer datasets that include a wide range of edge cases and rare events. This ensures that models are trained on and can handle even the most unusual scenarios, improving their robustness and reliability.

#6. Data Annotation Costs

Labeling real-world data for supervised learning is often a time-consuming and expensive process, requiring significant human effort. While using synthetic data, you don't face the issues as it can be automatically labeled during the generation process, thus significantly reducing the time and cost associated with data annotation. This allows organizations to create large, accurately labeled datasets much more efficiently than with manual annotation of real-world data.

#7. Data Refresh and Adaptation

Real-world data can quickly become outdated, especially in rapidly changing environments. Collecting and processing new data to keep AI models up-to-date is often a slow and resource-intensive process. Synthetic data generation allows for rapid creation and iteration of datasets. As new trends or patterns emerge, the data generation process can be quickly adjusted to produce up-to-date training data, ensuring that AI models remain relevant and effective.

Challenges and Considerations: What’s Next?

Despite the opportunities and benefits synthetic data offers, it also comes with a few challenges. Let’s discuss some general limitations and challenges you will likely experience with synthetic data generation: 

Quality control

Even though generating synthetic data allows you to simulate datasets based on real-world scenarios, data quality, and accuracy are the primary concerns for many. However, ensuring that no one can trace synthetic data points back to real information may require a reduction in accuracy. A trade-off in privacy and accuracy could impact quality. While performing manual checks of synthetic data before using it may help in overcoming this issue, it’s time-consuming, especially if you need to generate lots of synthetic data.

Requires expertise, time, and effort

This is another challenge: the scarcity of talent and expertise to generate synthetic data. One needs intricate and complex techniques, rules, and current methods to ensure its accuracy and utility. You need high expertise in this field before you generate any useful synthetic data while keeping the perfect imitation of its real-world counterpart. Though technical workshops and training may help your engineers and developers to work around the systems, it’s going to be both a resource- and cost-intensive task. 

End-user confusion

Synthetic data generation is a new concept while it augments and supplements tools for businesses, but individuals and professionals who have not seen its advantages may not be ready to trust the predictions based on it. This means that awareness about the value of synthetic data to drive more user acceptance needs to be created first. On the flip side, others may over-emphasize the results due to the controlled aspect of generation. Therefore, a balanced communication of the limits of this technology, its outcomes, and its positive outlook needs to be relayed to the end-users whether they're the companies’ stakeholders, employees, or customers, ensuring they understand both benefits and shortfalls.

Innovation With Synthetic Data: The Right Way

As a new field, synthetic data is opening new possibilities for industries be it finance, eCommerce, retailers, and top software development companies, among others. Moreover, it’s resolving some of the biggest challenges in data management, such as privacy, data availability, and quality. All this while helping businesses maintain a high level of data protection and regulatory compliance. As an organization, if you too intend to utilize synthetic data generation, consider the following factors:

• Clearly outline your goals (identify your purpose for using synthetic data and the business reasons behind it.)
• Decide on the required quality of synthetic data (what percentage of similarity to real-world data is enough, or do you need a higher level of accuracy or low?)
• Evaluate the cost of generating the synthetic data (Will there be a clear return on investment?)
• Address security and regulatory concerns, including GDPR compliance. (Focus on the regions or countries you intend to offer your services or deliver projects to.

Wrapping It Up

There’s no doubt that real data will mostly be preferred for business decision-making and other critical operations like policy-making or research. However, when such real raw data is unavailable for analysis, synthetic data is the next best solution. It offers powerful solutions to many of its limitations. As technology continues to advance, we can expect synthetic data to play an increasingly crucial role in AI and machine learning development.

Its ability to provide abundant, diverse, and adaptable datasets while addressing ethical and practical concerns positions synthetic data as a key enabler of future AI innovations. There are challenges and a few downsides to using this new data generation technique, but the positive outweighs them, as synthetic data is paving the way for more robust, fair, and effective AI systems. 

If you're facing challenges with your AI projects due to data limitations, it's time to explore the potential of synthetic data. Start by identifying which of these 7 problems are most relevant to your work, and consider how synthetic data generation could help overcome these hurdles. Embracing this innovative approach could be the key to unlocking new possibilities in your AI development journey. 

How can Binmile support your synthetic data generation efforts?

With a proven track record of delivering cutting-edge business solutions, our AI-driven digital transformation company can help you witness the transformative power of AI. Our expertise can enhance your AI capabilities and initiatives to find new revenue streams, achieve unprecedented productivity levels, and help you drive sustainable growth.