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The Complete Guide to Synthetic Data + Nurdle

Hetal Bhatt
Reading time: 12 min
When it comes to data science and AI (artificial intelligence) development, finding good data is like a never-ending treasure hunt… And it’s nowhere near as fun as sailing around the Caribbean!

Getting real-world data can be a real pain in the neck, especially since high-quality textual training data is projected to run out entirely by 2026. Data for rarer edge cases is even harder to come by, while sourcing and preparing real data (when you can find it) costs an arm and a leg and can take weeks to complete. On top of that, data privacy concerns and privacy regulations make it even more complicated to use real-world data for training AI models.

But there is hope!

Synthetic data is gaining popularity across different industries for AI development. In fact, tech firm Gartner estimates that by the end of 2024, 60% of data used for training AI will be synthetic. In this guide, we'll dive into synthetic data, synthetic data for machine learning, and synthetic data augmentation and explore what it is, how it has evolved, and why it's so important.
Comprehensive guide to synthetic data: Learn how synthetic data can revolutionize AI training and model development in our detailed guide.

In this blog post

Understanding Synthetic Data

1. What is Synthetic Data?

Synthetic data is generated using algorithms and relevant data models rather than being collected in real-world online spaces. It is designed to mimic characteristics of real-world data while ensuring that privacy is protected. Nurdle creates synthetic data with a unique approach by seeding it with a kernel of real data to create “lookalike” datasets that closely resemble domain-specific data and real-life scenarios. This is particularly advantageous for data scientists since it saves 80% of the time that they would normally spend sourcing and preparing real data. Using synthetic data also helps reduce the risk of models producing hallucinations or bias since synthetic data is diverse and can easily be generated in large volumes.

Overall, synthetic data is an essential tool for anyone working with AI, as it allows them to understand data better, develop better models, and generate data quicker while not running amok of data privacy laws.

2. Types of Synthetic Data

There are different types of synthetic data, with each one tailored to suit different use cases and modeling objectives.
First-Generation vs. Second-Generation Synthetic Data
The evolution of synthetic data for AI can be categorized into two main generations:
First-Generation Synthetic Data

First-generation synthetic data relies heavily on traditional statistical methods like randomization and sampling, which are fine for machine learning predictive analytics and structured data in tabular formats.

However, those methods can’t quite capture the complexity and subtleties of real-world data. As a result, first-generation synthetic data models often have lackluster performance and limited applicability, especially for generative AI or classifiers. In simpler terms, first-generation synthetic data is great for tabular data but not so great at creating realistic, unstructured data that resembles real-world scenarios.
Second-Generation Synthetic Data

Second-generation synthetic data is an upgraded version of first-generation synthetic data that uses advanced machine learning techniques like GANs, VAEs, and deep learning architectures. This makes it really good at modeling real-world data and creating datasets for unstructured text like chat messages, social media posts, or emails.

This is Nurdle’s specialty! We're the first provider of second-generation synthetic data that’s specifically designed to resemble realistic, human-like communications (or “unstructured data”).

Nurdle has the world's largest proprietary data vault of human-to-human interaction from social networks, gaming sites, dating apps, messaging platforms, and chat content in 40 different languages. To generate unstructured synthetic data, Nurdle combines a small sample of your data with relevant samples from our data vault to create “lookalike” datasets that are ready to train generative AI and detection classifiers.

The best part? You don’t have to wait weeks for data prep!
Tabular Synthetic Data vs. Non-Tabular Synthetic Data
Synthetic data doesn’t always come in traditional tabular formats. It also can encompass a wide spectrum of data types, including both tabular and non-tabular data:
Tabular Synthetic Data

In the realm of structured data, synthetic data is widely usable in scenarios involving databases, spreadsheets, and relational datasets.

Specifically, tabular synthetic data can replicate the statistical properties of real tabular data while ensuring privacy. This is particularly important in closely regulated industries where tabular data with sensitive information about employees or customers is prevalent, such as e-commerce, healthcare, and finance.

By using techniques like statistical matching and Monte Carlo simulation (Cool name, right?), synthetic tabular data can help data scientists build and evaluate AI models without compromising privacy.
Here are a few synthetic data examples of Synthetic Tabular Data:
Non-Tabular Synthetic Data

Non-tabular data refers to data that does not fit neatly into traditional rows and columns (i.e., Spreadsheets and databases). Instead, non-tabular data often comes in various formats like text, images, audio, video, and other less rigid structures.
Here are a few examples of text content that is Non-Tabular Data:
  • Blog posts, articles, and other written content for content marketing.
  • Email newsletters, marketing copy, and SMS messages.
  • Social media posts, captions, and comments.
Generating synthetic data from real data for non-tabular data can be especially tricky. Fortunately, Nurdle is a pioneer in creating synthetic data for non-tabular data that closely resembles real-world scenarios.

By using a small sample of real data, Nurdle is able to create synthetic training data that perform equally as well as the actual data, which better helps data scientists fine-tune their models and train their classifiers. This is particularly useful for unstructured data like social media posts, customer reviews, and sales emails.

Nurdle's synthetic data augmentation is a game-changer in the world of AI and data science.
Human-Generated & Labeled Data
Nurdle Data
Mostly AI
Go crack your molars on some gravel you pos.
Try making better pictures you sociopath.
The effect of the temperature on the structure of the glassy carbon-glass surface, with a slight temperature increase at a low temperature.
*No new synthetic data produced; re-ordered upload.

3. How to Use Synthetic Data in Machine Learning

Synthetic data offers many benefits in the data preparation and model-training processes.

By using synthetic training data, data scientists can save a lot of time and resources that would have been spent on sourcing and preparing real-world data. Usually, this process takes weeks or even months to complete and makes data scientists hate their jobs. Synthetic data can generate large volumes of diverse datasets for machine learning in a matter of hours, which makes it an ideal choice for the two main tasks of AI development:

  • Synthetic dataset generation for fine-tuning LLMs based on specific datasets.
  • Synthetic dataset generation for training classifiers that require a lot of data.

And since synthetic dataset generation is able to generate a diverse set of synthetic data that closely resembles real-world scenarios, synthetic data helps reduce the risk of hallucinations or bias in AI models.
Fine-tuning Generative AI LLMs
Fine-tuning is the process of tailoring an LLM to a specific domain or task. Synthetic training data plays a crucial role in supplementing datasets that are used for training models and fine-tuning pre-trained language models.

With synthetic data, AI models can include synthetic training data for edge cases and rare scenarios which often are tough to find “in the wild” via real-world data. This, in turn, can facilitate better results in text classification, sentiment analysis, and other natural language processing (NLP) tasks.

Side note: Nurdle is creating synthetic data from real data can fine-tune AI model performance for specific use cases or improve overall model accuracy – seriously, we’ve got range!
Build and Train Classifiers
Data scientists often encounter the cold start problem, where they struggle to gather enough use-case-specific real data to effectively train, fine-tune, and launch their classifiers. Even after deployment, further data is needed to refine the classifier for enhanced accuracy. Recent studies have indicated that training classifiers using synthetic data can lead to more precise models compared to those trained solely on real data. This approach not only yields superior performance on real-world tasks but also addresses ethical, privacy, and copyright concerns associated with using genuine datasets.

Enterprises grappling with this challenge can turn to Nurdle, a solution designed to empower data science teams. Nurdle leverages advanced algorithms to generate synthetic data sets comprising thousands of rows, derived from a small initial sample of real data. This synthetic data can be utilized across a spectrum of classifiers, including intent classifiers, content moderation classifiers, sentiment classifiers, and more. By harnessing Nurdle's capabilities, data scientists can effectively overcome the cold start problem, enabling them to develop highly accurate classifiers essential for various applications.
Data Preparation
Sourcing and preparing real-world data can be a time-consuming and expensive process. It often takes months to complete and takes up a significant amount of resources. It’s such a timesuck that it can cause delays in launching AI projects.It also comes with regulatory compliance concerns since the data contains PII (personally identifiable information).

On the flip side, using synthetic data can be a cost effective and scalable process. And that kind of efficiency can accelerate development and speed up the delivery of AI projects. Also, since synthetic data is artificial data, synthetic data is GDPR compliant.

Nurdle, a synthetic data provider, handles all of the data sourcing, data cleaning, and data labeling, which slashes time spent on data preparation to a matter of hours. This can free up 80% of data scientists' time, allowing them to focus on higher-level tasks in the generative AI development cycle.

After figuring out what your AI model needs, Nurdle can generate custom synthetic datasets for unstructured text within 24 hours – you can literally start testing and iterating the next day! By using Nurdle's tailor-made and high-quality synthetic datasets, organizations can dodge the high costs of AI development and focus on building better products.
If that sounds like a lot of work, it’s because it is! Data scientists say they spend around 80% of their time on data prep and management – and 76% say it’s the worst part of their job.
Synthetic data is absolutely vital for data scientists and AI product managers. It offers a pragmatic answer to data dilemmas like the scarcity of edge-case datasets and the high costs of storing actual data. As the artificial intelligence field continues to evolve, it will be crucial to explore second-generation synthetic data methodologies and how to use them across tabular and non-tabular data domains.
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