Unlocking the true potential of synthetic data for machine learning

Ask a data engineer or scientist what synthetic data is, and they will likely use a lot of industry jargon to explain the concept. However, by the time the conversation ends, you might still scratch your head, feeling like, “Oh, that was good, but I still couldn’t get it.” So, if you are curious about synthetic data and how it's opening new possibilities in machine learning, this blog has everything you need to know.

What is Synthetic Data?

Synthetic data is artificial data or information produced using simulations or computer algorithms. It can be generated using real-world or authentic data, computer algorithms, or programs. 

What’s incredibly fascinating about synthetic data is that you can produce it on-demand, anywhere, in any scale or volume to meet your specific project requirements, including training machine learning models. 
 

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Synthetic Data types: 

Businesses must understand the types and varieties of synthetic data they can create to meet their business goals. Based on whether the data is generated using actual data, synthetic data can be categorized into: 

• Fully synthetic data: This digitally created data set does not use real-world data. They are generated using simulations or algorithms in a wholly Digital Environment. You might need fully synthetic data to train machine learning models when real-world data does not exist or acquiring and processing it is not feasible. 
• Partially synthetic data: This type of synthetic data uses authentic data to enable data teams to produce sufficient data on demand and cost-effectively. Companies can consider partially synthetic data when there is not enough real-world data available.

Synthetic Data varieties: 

Depending on your specific project requirements, you can create synthetic data in a variety of formats including: 

• Text data: You can create synthetic text data for natural language processing (NLP) applications. 
• Media data: You can also generate synthetic data in the form of images, videos, and sounds and leverage that data to build computer vision applications.  
• Tabular data: Even in scenarios where your team needs data logs or tables for classification or regression tasks, you can consider generating synthetic data. 

Why is Synthetic Data required?

We can see multiple factors driving the need for more accurate and on-demand synthetic data: to begin with, organizations need synthetic data to secure the sensitive or personally identifiable information they possess; any leakage or theft of this kind of data can damage their credibility in the eyes of investors and customers. So naturally, organizations would prefer to avoid taking this kind of risk. With synthetic data, they try to minimize this risk while unlocking the power of data to create more personalized experiences for their customers, employees, and partners. 

A company can also consider producing synthetic data when access to the original data is highly time-consuming and expensive. Creating and using high-quality synthetic data sets makes more sense in such a scenario. 

Additionally, training AI models require a large volume of data, and data teams might have a smaller volume of original data; in this condition, data teams can also leverage existing real-world data and produce desired amounts of partial synthetic data. In a nutshell, privacy concerns, faster turnaround for product testing, and training machine learning algorithms are the most notable drivers behind the popularity and generation of data that mimics real-world occurrences. 

Synthetic Data use cases

Compared to real-events data, synthetic data is cheaper and can be created on demand to meet specific needs for data. In recent years, companies across industries and sectors have embraced synthetic data to speed up processes and generate results. Some industries and sectors that have benefited from artificial data include: 

1. Banking and financial services - Organizations operating in this space can produce and leverage synthetic data to test and validate new fraud detection methods. The Banking and Financial Services Industry can also use this data to analyze customer data to gain deeper insights into customer behavior, as most customer data tends to be highly sensitive. 
2. Healthcare - Synthetic data allows healthcare practitioners to use the record data without compromising patient confidentiality. Hospitals and healthcare professionals can gain new and deeper insights and improve patient care and diagnostics. 
3. Automotive and robotics - Synthetic data is finding increasing applications in the Automotive and Robotics Industry as generating real-events data is expensive and time-consuming. With synthetic data, autonomous systems, such as robots and self-driving cars, can be tested in thousands of simulations and built to deliver better customer experience and results. 

Why do you need Synthetic Data for your Machine-Learning projects?

Machine learning (ML) is an innovative technology that supports a culture of automation and innovation. 

According to SAP Insights, “In machine learning, algorithms are trained to find patterns and correlations in large data sets and to make the best decisions and predictions based on that analysis. As a result, machine learning applications improve with use and become more accurate with more data they access.” So it’s clear that you need to feed them a large amount of data to improve the efficiency of your machine-learning applications and models. 

The only challenge is that businesses operate amid many constraints in the real world. And the set of challenges and pressures only grows when it comes to using customer data. So to avoid or mitigate the risks associated with using real-world data, a growing number of data-driven companies are now shifting to synthetic data.

Since data teams can quickly generate high-quality data without data privacy and security concerns, training AI/ML models or building applications that rely on intelligent technologies becomes much easier. This is the chief advantage and driver behind the use of synthetic data in machine learning space. 

Conclusion  

Data is one of the most valuable resources for modern organizations. At Kellton, we are a pioneer in Data Management Solutions and help companies across industries leverage one of their most important resources to gain a quick and competitive edge in their space - their data. 

Synthetic Data is one such innovation that is on its way to revolutionizing how business and IT teams look at data and leverage it to train machine learning models and streamline processes to innovate at scale. To see how we can help you with data challenges or building your next AI/ML application, contact our team here. You can also read about our Data Engineering and AI/ML capabilities.