What is Synthetic Data?
What is Synthetic Data?
In today’s data-driven world, the demand for high-quality, diverse datasets is ever-increasing. However, acquiring real-world data can be challenging due to privacy concerns, limited access, or simply the cost and time involved in collecting and processing large datasets. This is where synthetic data comes to the rescue.
Synthetic data can be defined as artificially generated data that imitates real-world data. It is created using computer algorithms or simulations. The aim of synthetic data generation is to overcome the limitations of using real data and mimic the statistical properties and characteristics of real data while ensuring privacy and anonymity.
Synthetic data is particularly useful when real data cannot be used due to personally identifiable information (PII) or compliance risks. It allows corporations and organizations to generate data that closely resembles real data without compromising compliance regulations.
Why is Synthetic Data Necessary?
Firstly, it helps address privacy concerns. With the increasing number of data privacy laws, businesses need to handle sensitive data with caution. Synthetic data generation methods provide a way to generate data that is not linked to real individuals, reducing the risk of privacy breaches and costly lawsuits.
Secondly, it is essential for faster product testing and development. In many cases, businesses may not have access to the real data required for testing new products or training machine learning algorithms. Fake data can be quickly generated to meet the specific requirements of the product, allowing businesses to proceed with testing and development without delays caused by data unavailability.
FInally, it enables businesses to train machine learning algorithms effectively. Real data may not always be sufficient or comprehensive enough to train algorithms. Synthetic data can be generated to supplement real data, providing a more diverse and balanced dataset for training machine learning models. This leads to better model performance and more accurate predictions.
Generation Techniques
Statistical Distribution-based Generation
Based on statistical distributions, data scientists observe the real statistical distributions of the available data and generate new data that replicates these distributions. Statistical distributions such as normal distribution, chi-square distribution, and exponential distribution are the most common for this purpose.
Data scientists need a deep understanding of the statistical distributions present in the real data to generate accurate synthetic data. The accuracy of the trained models heavily relies on the expertise of the data scientist in this method.
Agent Modeling-based Generation
Another approach to synthetic data generation involves creating a model that explains observed behavior in the real data. This model is then used to generate random data that follows the same distribution as the real data. This method is particularly useful when only a part of the real data is available.
Machine learning methods, such as decision trees, can be used to fit the distributions of the real data. A hybrid approach that combines statistical distribution-based generation and agent modeling-based generation can be used when both real data and expert knowledge are available.
Deep Learning-based Generation
Deep learning models, such as Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs), offer another powerful approach. VAEs compress and compact the real data using encoders, while decoders analyze this data to generate a representation of the real data. GANs, on the other hand, comprise a generator network responsible for creating synthetic data and a discriminator network that detects fake data.
These deep learning models can generate dummy data that closely resembles the real data, making them valuable tools for data generation. However, it is crucial to ensure that it does not reveal any links to the real information and is error-free.
Tools and Techniques
Datomize
Datomize is an Artificial Intelligence (AI) or Machine Learning (ML) model widely used by banks worldwide. It allows businesses to connect their enterprise data services and process complex structures and dependencies. Datomize’s algorithm extracts behavioral features from raw data, enabling the creation of identical data twins with the original data.
MOSTLY.AI
Focused on privacy and AI, it extracts structures and patterns from the original tables to prepare entirely different datasets, ensuring high privacy standards.
Synthesized
Synthesized is an all-in-one AI dataOps solution that provides data augmentation, collaboration, data provisioning, and secure sharing. It generates different versions of the original data and tests them with multiple test datasets, helping identify missing values and sensitive information.
Advantages
Customizability
Businesses can generate synthetic data tailored to their specific needs, ensuring that it contains the desired characteristics and properties. This level of customization allows for more targeted analysis and testing.
Cost-effectiveness
Compared to real data, it is a more cost-effective option. Acquiring real data, especially in certain industries like automotive crash data, can be expensive and time-consuming.
Faster Data Production
Given that it doesn´t depend on real-world events, it can be generated rapidly using suitable tools and hardware. This means that a large volume of artificial data can be made available in a shorter period of time, accelerating data-driven processes.
Privacy
It does not contain any traceable information, making it anonymous and suitable for sharing purposes. This advantage is particularly significant in industries such as healthcare and pharmaceuticals, where privacy regulations are stringent.
Industries
Several sectors are regular users of this type of data, it allows them to overcome data limitations and privacy concerns.
Healthcare
In the healthcare industry, it is used to create models, test datasets for conditions that lack actual data and predict disease trends. It also plays a crucial role in medical imaging, enabling the training of AI models while complying patient privacy.
Finance and Banking
Banks and financial institutions utilize synthetic data to develop effective fraud detection methods and test in-house systems and retail applications. For example, data scientists can analyze patterns and identify potential fraud instances without compromising real customer data. This helps protect customers and minimize financial losses.
eCommerce
Improving warehousing and inventory management are one of the key benefits for eCommerce companies. Synthetic data-driven machine learning models optimize inventory levels, streamline logistics processes, and enhance the overall customer purchase experience.
Manufacturing
In manufacturing industries is key for predictive maintenance and quality control. Factories can identify potential issues and optimize manufacturing processes without relying solely on real-world data.
Automotive and Robotics
The automotive and robotics industries leverage synthetic data to simulate and train self-driving cars, drones, and robots. Synthetic data allows businesses to create realistic scenarios and test the performance of autonomous systems without risking real-world accidents or damage.
Types of Synthetic Data
1) Full
It is generated using algorithms and simulations, ensuring that the data is not identifiable and it has no connection whatsoever to real data. This type very is useful when real data is unavailable or when complete privacy is mandatory.
2) Partial
Partially synthetic data retains all the characteristics and properties of the original data, except for the sensitive information. It is generated by extracting relevant information from the actual data while ensuring that personally identifiable information (PII) is removed. Partially synthetic data is useful when businesses need to share data for research or analysis purposes while protecting privacy.
Role in Machine Learning and AI
Synthetic data plays a crucial role in the development and deployment of machine learning and AI models. It serves as a valuable resource for training and testing models, especially when real data is limited or unavailable. Organizations can create diverse datasets that cover a wide range of scenarios, improving the robustness and generalization capabilities of their models.
It is also a solution to bias in machine learning algorithms. Generating data that represents underrepresented or marginalized groups, organizations can mitigate the biases present in real data and ensure fair and equitable outcomes.
Additionally, it can be used for model validation and stress testing. Engineers can simulate extreme or rare events, so companies can assess the performance and reliability of their models in challenging scenarios, making them more reliable and resilient.
Limitations
- Reliability of the Data: The quality and reliability depend on the quality of the input data and the models used for generation. Biases in the source data may impact the accuracy and reliability of the generated data.
- Replicating Outliers: Synthetic data might not fully capture outliers present in the real data. Outliers can be crucial for certain analyses or predictions, and their absence in synthetic data can affect the accuracy of models.
- Expertise, Time, and Effort: it requires expertise, time, and effort. Data scientists need to have a deep understanding of data modeling and statistical distributions to ensure the quality and representativeness of the generated data.
- User Acceptance: Given that is a new concept, user acceptance may vary. Some stakeholders may be skeptical about relying on synthetic data for decision-making.
- Quality Check and Output Control: Data scientists need to manually check the generated data to ensure it accurately represents the real data. Complex datasets generated using algorithms require thorough quality checks to verify correctness and absence of biases.
The Future
With its scalability and flexibility, synthetic data offers solutions to data scarcity and privacy concerns. While generating accurate synthetic data requires expertise and specialized skills, advancements in AI and machine learning are making the process more accessible.
As businesses continue to rely on data for decision-making, synthetic data will play a crucial role in filling the gaps left by real data. It enables data scientists to accomplish innovative tasks that would be difficult to achieve with real-world data alone. The increasing adoption of synthetic data in various industries indicates its potential to shape the future of data-driven decision making.
Wrapping Up
Synthetic data is a valuable asset for businesses and organizations when real data is unavailable or privacy concerns arise. It offers customizability, cost-effectiveness, and faster data production, while maintaining data privacy and security. Synthetic data finds applications in healthcare, finance, eCommerce, manufacturing, automotive, and robotics industries, enabling businesses to make data-driven decisions and improve overall performance.
