Synthetic data: what they are, types, methods and uses

Synthetic data expand the area of ​​research and education. It is intentionally crafted data that replicates the statistical characteristics of real data in the field of data-driven insights.

It is possible to come across sensitive data sets that cannot be made publicly available due to privacy regulations. Synthetic data can help communicate, build models, and run tests without revealing personal information.

Stay tuned as we explore the world of synthetic data and discover its different types, generation methods and tools that enable data scientists to make informed decisions while respecting privacy and ethical concerns.

What is Synthetic Data?

Synthetic data is artificially created data that replicates the qualities and statistical properties of real data, but does not contain real information from real people or real sources. It is a copy of patterns, trends and other characteristics found in real data, but without real information.

They are created using various algorithms, models or simulations to replicate the patterns, distributions and correlations found in real data. The goal is to generate data that matches the statistical properties and relationships in the original data without revealing individual identities or sensitive details.

Using this artificially generated information circumvents the limits of using regulated or sensitive data. You can customize the data to meet specific needs that would not be possible with real data. These synthetic datasets are primarily used for quality assurance and software testing.

However, you should be aware that this data also has disadvantages. Replicating the complexity of the original data can lead to discrepancies. It is important to note that this artificially generated data cannot completely replace real data, as reliable data is still required to obtain relevant results.

Why use synthetic data?

When it comes to data analysis and machine learning, synthetic data offers several advantages that make it an essential tool in your arsenal. By creating data that reflects the statistical characteristics of real-world data, you can unlock new possibilities while ensuring privacy, collaboration, and the development of robust models.

Privacy concerns

Let's assume you are working with sensitive data, such as: B. medical records, personal identifiers or financial information. Synthetic data acts as a shield that allows you to gain useful insights without violating people's privacy.

You can maintain confidentiality while conducting critical analysis by generating statistically similar data that cannot be identified with real people.

Data sharing and collaboration

This artificially generated data is a solution for situations where data sharing is challenging, such as: B. with legal boundaries, ownership issues or cross-border legislation.

By using synthetically generated datasets, you can encourage collaboration without revealing sensitive information. Researchers, institutions and companies can exchange important knowledge without the usual restrictions.

Model development and testing using synthetic data

Using synthetically generated data, you can develop accurate and efficient models. Consider this your testing room. You can tune your models efficiently by testing them with carefully prepared synthetic test data that replicates real-world distributions.

This artificial data helps you identify problems early, avoid overfitting, and ensure the accuracy of your models before deploying them in real-world scenarios.

Types of synthetic data

Synthetic data offers many methods to meet your needs. These techniques protect sensitive data while preserving important statistical insights from your original data. Synthetic data can be divided into three types, each with its own purpose and benefits:

1. fully synthetic data

This artificial data is completely made up and does not contain any original information. In this scenario, as a data generator, you would normally estimate the parameters of the feature density function present in the real data. You then randomly create protected sequences for each feature based on the projected density functions.

Suppose you decide to replace a small number of features from the real data with artificial features. The protected sequences for these features are matched to the other features found in the real data. Because of this alignment, the protected and real sequences can be classified similarly.

2. Partially synthetic data

This synthetic data comes into play when it comes to protecting privacy without compromising the integrity of your data. Here, selected sensitive characteristic values ​​that have a high risk of disclosure are replaced by synthetic alternatives.

Approaches such as multiple imputation and model-based methods are used to create this data. These methods can also be used to impute missing values ​​from your actual data. The goal is to keep the structure of your data intact while maintaining privacy.

3. Hybrid synthetic data

This data represents a robust alternative to achieve a balance between privacy and utility. A hybrid data set is created by mixing aspects of real and artificially generated data.

For each random record in your real data, a closely related record is selected from the synthetic data vault. This method combines the advantages of fully synthetic and semi-synthetic data and finds a compromise between maintaining privacy and the value of the data.

However, due to the combination of real and synthetic elements, this method may require more memory and processing time.

Methods for generating synthetic data

You can explore a number of methods for generating synthetic data, each offering a unique technique for generating data that accurately reflects the complexities of the real world.

These techniques allow you to produce datasets that retain the statistical foundations of real-world data while opening up new avenues for exploration. Let's look at these approaches:

Statistical distribution

This method involves pulling numbers from the distribution by examining real statistical distributions and reproducing similar data. If real data is not available, this factual data can be used.

Data scientists can construct a random data set if they understand the statistical distribution of real data. Normal-, Chi square-, exponential and other distributions can do this. The accuracy of the trained model largely depends on the data scientist's experience with this method.

Agent-based modeling

This method allows designing a model that explains the observed behavior and generating random data using the same model. It is the process of fitting real data to a known data distribution. Companies can use this technology to generate synthetic data.

Other machine learning approaches can also be used to fit distributions. However, when data scientists want to predict the future, the decision tree is overfitted due to its simplicity and the fact that it descends to depth.

Generative adversarial networks (GAN)

In this model, two neural networks work together to produce fake but potentially valid data points. One of these neural networks acts as a producer and creates synthetic data points. The other network acts as a judge and learns to distinguish between the fake samples generated and the real ones.

GANs can be difficult to train and very computationally intensive, but the benefits are worth it. GANs can be used to generate data that comes very close to reality.

Variational Autoencoders (VAE)

This is an unsupervised method that can learn the distribution of your original dataset. They can generate synthetic data through a two-step transformation process known as encoded-decoded architecture.

The VAE model produces a reconstruction error that can be reduced through iterative training sessions. With VAE you get a tool that allows you to generate data that is very similar to the distribution of your real data set.

challenges and considerations

When working with synthetic data, you must be prepared for a number of challenges and limitations that can impact the effectiveness and applicability of the data:

• Accuracy of data distribution: It may be difficult to reproduce the exact distribution of the real data, which may introduce errors in the artificially generated data.
• Maintaining Correlations: It is difficult to maintain complicated correlations and dependencies between variables, which affects the reliability of the synthetic data.
• Generalization to real data: Models trained on artificial data may not perform as well as expected on real data, so they need to be fully validated.
• Data protection versus benefits: It can be difficult to find an acceptable balance between privacy and data utility because strict anonymization can compromise the representativeness of the data.
• Validation and quality assurance: Since there is no ground truth, extensive validation procedures are required to ensure the quality and reliability of the synthetic information.
• Ethical and legal considerations: Misuse of artificial data can raise ethical questions and have legal implications, highlighting the importance of appropriate user agreements.

Validation and evaluation of synthetic data

When working with synthetic data, thorough validation and evaluation is required to ensure its quality, applicability and reliability. How this data can be effectively validated and evaluated is explained below:

Data quality measurement

• Comparison of descriptive statistics: To verify consistency, compare the statistical attributes of this artificial data with the actual data (e.g. mean, Variance, distribution).
• Visual inspection: Visually identify discrepancies and variances by comparing the synthetic data with the real data.
• Outlier detection: Look for outliers that could affect the quality of the artificial data and the performance of the model.

Ensuring usability and validity

• Adaptation to the applicationl: Determine whether the artificial data meets the requirements of your specific use case or research problem fulfill.
• Impact of the model: Train machine learning models and evaluate their value using real data.
• expertise: Involve subject matter experts in the validation process to ensure that the artificial data captures the essential subject-specific properties.

Benchmarking synthetic data

• Benchmarking with real data: If possible, compare the generated data with real data to determine its accuracy.
• Model performance: Compare the performance of machine learning models trained on synthetic data to models trained on real data.
• sensitivity analysis: Determine the sensitivity of the results to changes in data parameters and generation methods.

Continuous development

• Feedback loop: Continuously improve and adjust data based on validation and assessment feedback.
• Gradual changes: Gradually adapt the creation processes to improve data quality and comparison.

Use of synthetic data

Synthetic data has applications in a variety of real-world scenarios and offers solutions to a variety of challenges in different areas. Here are some notable use cases where synthetic data proves its value:

• Healthcare and medical research: Artificially generated data is used to disseminate and evaluate medical data without compromising patient privacy. Simulating patient records, medical images and genetic data allows researchers to develop and test algorithms without revealing sensitive data.
• Financial analytics: This artificial data is used to test investment strategies, risk management models and trading algorithms. Analysts can test alternative scenarios and draw informed conclusions without using sensitive financial data by replicating market behavior and financial data.
• Fraud detection: Without revealing customer data, financial institutions can develop synthetic transaction data that simulates fraud. This helps develop and improve fraud detection systems.
• Sozialwissenschaft: Without violating privacy, social scientists can analyse trends, habits and social interactions. Researchers can study and model human behavior, conduct surveys, and simulate social environments to understand the dynamics of society.
• Online privacy protection: Fake data can protect consumer privacy in privacy-sensitive applications such as online advertising or personalized recommendation systems. Advertisers and platforms can optimize advertising targeting and user experience by using synthetic user profiles and behaviors to maintain user anonymity.

Future trends in synthetic data

There are several interesting trends shaping the future of synthetic data and will influence the way data is generated and used for a variety of purposes:

• Adaptation to your needs: In the future, technologies will become available that allow you to customize synthetic data for specific industries or your own needs, which will increase its relevance.
• Federated learning and a focus on data protection: Artificial data is used with federated learning and fine-grained data protection strategies to ensure data protection when training models cooperatively.
• The rise of data augmentation: Synthetic information will increasingly complement real-world datasets through data augmentation, improving the resilience and performance of models.
• Ethical and bias considerations: Tools are being developed to detect and mitigate bias, which will promote fairness in AI applications. Learn more about the impact of generative AI on research and knowledge.
• Standardization and transparency: To improve reliability and transparency, keep an eye on initiatives to standardize data methods and develop reference datasets.
• Integration of transfer learning: Synthetic information could be crucial in pre-training models on simulated data, which will reduce the need for real big data for certain tasks.

Conclusion

The potential of synthetic data is becoming increasingly clear. Strategically adding them to your toolkit will help you deal with obstacles creatively and accurately.

Data scientists can fully exploit the potential of synthetic data. Their expertise can lead the way in protecting privacy, developing models enriched by diverse and adaptable data sets, and collaborating across conventional boundaries.

QuestionPro can be an important resource for exploiting the power of synthetic data. The platform enables you to fully exploit the benefits of synthetic data for your research, analysis and decision-making processes using a wide range of tools and features.

Take advantage of that Survey software from QuestionPro to collect accurate data from your target audience. This real data serves as the basis for creating meaningful fake data. QuestionPro allows you to transform raw survey responses into structured data sets, creating a seamless transition from raw data to synthesized information.

With QuestionPro's comprehensive tools and expertise, you can confidently enter the future of data science.

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KEYWORDS OF THIS BLOG POST

Synthetic data | data | Synthetic