A Full-fledged Guide on Synthetic Data for Those Aiming to Use AI Technologies for Good!

The digital-first generation of technology has made the need for readily available information a must since a lot can happen over proper datasets. The inevitable growth of artificial intelligence (AI) technology stands as a testament to the statement since all AI solutions use data of some form or another. With recording, storing, and processing real-world data becoming increasingly complex in a world where privacy is increasingly becoming important, synthetic data generation becomes crucial. This type of data is essential for creating high-quality products that can contribute to the betterment of the Web3 industry. This guide provides extensive information on what synthetic data is and how useful it is for businesses in the current era.

Types and Formats of Synthetic Data

Like any other data, synthetic data comes in all types and formats, easing the process for users using AI technology for various industrial and research use cases. The below subsections discuss the types and formats of synthetic data in detail.

Types of Synthetic Data

1. Fully Synthetic Data

Fully synthetic data is fully generated by AI algorithms and does not contain any original information. This kind of data is useful for applications where using original data might endanger the well-being of individuals or organizations. Multiple imputations and bootstrap methods are popular ways to create fully synthetic data.

2. Partially Synthetic Data

Partially synthetic data could contain bits of original information, and the AI algorithm would replace selected aspects of the data with random information. This is useful in applications where only some points of personal information are deemed too sensitive to reveal than others. Model-based techniques and multiple imputations are popular ways to form partially synthetic data.

3. Hybrid Synthetic Data

Hybrid synthetic data contains a mix of real and artificial data, where synthetic data is used in place of random aspects of real-world data. Due to the level of complexity involved, this type of data proves to be extremely useful despite incurring higher memory space and processing time.

Formats of Synthetic Data

1. Textual

Textual synthetic data is generated by AI algorithms that resemble realistic text. Although replicating the tone of realistic content was initially challenging, the advent of advanced machine learning models and natural language processing algorithms make it possible step by step.

2. Tabular

Tabular synthetic data closely resembles information in tabulated forms that can be used for classification and comparison. The increasing importance of data analysis and research has made the requirements clear for generated data in a tabular form that can help accelerate advancements in various fields.

3. Multimedia

Multimedia synthetic data usually involves images, audio, and videos created using AI models. These can be utilized to train models using computer vision to find and comprehend data in digital format. As the need for digital identification increases, this kind of synthetic data can boost developments.

Wide-ranging Use Cases of Synthetic Data

Synthetic data has various use cases in a time when information is wealth, with various industries finding the need for more data imperative. While having real-world information always helps, the difficulties associated with acquiring it make it challenging for startup projects and researchers. Synthetic data generation, in this case, becomes helpful since it resembles original data forms. The sections below discuss some of the cases of synthetic data being used in the modern day.

• ML Model Training

  Synthetic data can be used to train machine learning models for use cases where access to real-world information is limited, costly, or sensitive. Using generated datasets replicating real-world information, models can be trained to enhance their generalization capabilities, which could be useful in the long run.

• ML Model Testing

  Artificially generated data can be used to test and validate the performance of ML-based models under various conditions. Doing so enables developers to conduct extensive testing and quality assurance practices without needing to collect actual data, which would be infeasible for most projects.

• Augmentation

  Generative information from AI algorithms can be used for data augmentation, a practice where training datasets are diversified and widened. This can come in handy in situations where an ample volume of real-world information is not readily available or hard to acquire to train an AI model.

• Anonymization

  Synthetic data from AI solutions can be used for anonymization, where alternative information is created in place of sensitive and personal real-world data. Using anonymized information based on synthetic datasets can be crucial for training AI models without worrying about ethical and legal implications surrounding the use of real-world information.

• Simulation

  Artificial data can be used in simulation and modeling to develop virtual environments and scenarios that could not be formed under real-world conditions. High-quality simulations can be created without compromising the privacy of any individual or organization by tapping into data that only replicates the statistical behavior and patterns noticed in real-world information.

• Marketing

  Synthetic data generation can be greatly useful for promotional campaigns. It lets marketers gain diverse insights and perspectives that can help them improve their performance. By creating synthetic customer personas, marketing teams can frame a comprehensive campaign without worrying about the implications of handling sensitive data of real people.

• Cybersecurity

  Data generated using synthetic algorithms can play a pivotal role in the cybersecurity field to let projects strengthen their capabilities to fight harder cyber attacks. Using artificial information here can be helpful for cybersecurity teams since it can offer diversified data and scenarios that could be difficult to construct in the real world.

• Fraud Detection

  Another use case where synthetic data can be critical is fraud detection in the financial sector. Artificially generated information can be used to recreate situations and patterns that reflect fraudulent behavior in financial transactions without needing to utilize the personal data of others. Doing so can help financial institutions like banks stay up with the improved tactics followed by malicious actors.

Differentiating Synthetic Data from Real and Dummy Counterparts

If you have wondered how synthetic data is different from real and dummy data, you are not alone. While you might have recognized the apparent differences between synthetic and real data, there is a high chance that you might think that synthetic data is the same as dummy data. This section is here to resolve your concerns with the correct differences.

Firstly, let us differentiate synthetic data from real data. Real data is obviously the information one collects from real-world resources, while synthetic data, as we know, is generated using machine learning models or algorithms. As we emphasized earlier, real data does not always represent the true diversity of possibilities, and the chances for acquiring such voluminous information are incredibly challenging for a number of reasons.

It is time to explore how synthetic data and dummy data differ from each other. Dummy data is often created manually by developers involved in testing applications, whereas synthetic data is generated by sophisticated algorithms. The latter typically reflect more diversity than the former due to the intensive training the algorithms undergo, not to mention developers might not always identify all the testing possibilities surrounding an application.

Ways to Ensure the Quality of Synthetic Data

AI model developers should ensure that the quality of synthetic data they use is appropriate for the use case their model will be working on. There are multiple ways to ensure the quality of artificially generated data for training ML-based models, some of which are discussed below:

• Using an Apt Generative Model

  Using an appropriate generative AI model with the necessary configuration is vital for ensuring the quality of synthetic data. Adjusting the parameters just right could be crucial in enhancing the quality of output from meager to excellent, which takes a lot of exploration and experimentation while abiding by privacy policies.

• Validating Against Known Values

  To ensure that the resulting synthetic data is of higher quality, validating the output against known values in the real-world data source could be helpful. Analyzing the statistical parameters of both datasets and checking their resemblance is key to determining the quality of generated data.

• Testing Regularly for Defects

  Regularly testing the synthetic data output for potential defects can pave the way for improving the quality of output. With every testing iteration, newer errors will be found, which would enhance the output’s nature, ultimately establishing the highest quality possible. Getting insights from professionals in the relevant domain could be helpful as well.

• Evaluating Various Trade-offs

  Evaluating trade-offs in aspects like privacy and utility of generative data could help identify the quality of information. Adequate measures can ensure ample levels of privacy while preserving the usefulness of data without causing many problems that could lead to unsavory situations.