What is Synthetic Data? Types, Advantages and Disadvantages

One of today's most precious resources is data. But because it is expensive, sensitive, and takes time to process, gathering real data is not always an option. However, using synthetic data might be a useful substitute when training machine learning models. 

 

In this post, we will define synthetic data, discuss its applications, discuss when and why it should be utilized, as well as the generation models and tools that are now available.

 

Synthetic Data: What Is It?

 

Any information that has been created artificially and does not accurately reflect events or things in the real world is considered synthetic data. Synthetic data produced by algorithms is utilized in model datasets for validation or training. In order to test or train machine learning (ML) models, synthetic data might simulate operational or production data.

 

Synthetic data has key advantages, including the ability to generate large training datasets with no manual labeling of data and the reduction of restrictions associated with the use of regulated or sensitive data. Synthetic data can also ‌customize data to match circumstances that real data does not permit.

 

Significance of Synthetic Data

 

The power of synthetic data to provide features that otherwise wouldn't be possible with real-world data makes it crucial for a variety of applications. Synthetic data is a lifesaver when real data is scarce or when maintaining anonymity is of the utmost importance.

 

The artificial intelligence (AI) business industry is heavily dependent on this data.

 

1. For assessing some disorders and circumstances when genuine data is lacking, the medical and healthcare industry uses fake data.

 

2. Artificial data is used to train self-driving Uber and Google vehicles.

 

3. Fraud protection and detection are of utmost importance in the financial sector. Synthetic data can ‌investigate new fraudulent situations.

 

4. Data professionals can access and use centrally stored data while still protecting its anonymity thanks to synthetic data. Synthetic data can mimic the key characteristics of genuine data without divulging its true meaning, maintaining privacy.

 

5. In the research division, synthetic data enables you to create and offer innovative goods for which the essential data might not otherwise be accessible.

 

Also Read | What is a Data Pipeline? Examples and Elements

 

Types of Synthetic Data

 

In order to conceal sensitive personal information and preserve statistical details of features in the original data, synthetic data is generated at random. Three categories can be used to broadly classify types of synthetic data:

 

1. Fully Synthetic Data

 

This data is entirely made up; it contains no original data at all. Typically, the data generator for this kind of data will estimate the parameters of the density function of the features in the real data. Later, based on the estimated density functions, privacy-protected series are constructed for each feature at random. 

 

The protected series of these features are mapped to the other features of the actual data in order to rank the protected series and the real series in the same order if only a small subset of real data features are chosen to be replaced with synthetic data. 

 

Bootstrap approaches and multiple imputations are two examples of traditional techniques that can be used to generate totally synthetic data. This method has great privacy protection with a fallback on the veracity of the data because the data is entirely synthetic and no real data exists.

 

2. Partially synthetic data

 

This data only uses synthetic values to replace the values of a few chosen sensitive features. In this scenario, the genuine values are only changed if there is a substantial risk of disclosure. 

 

To protect privacy in the freshly generated data, this is done. The methods of model-based procedures and multiple imputations are used to produce partially synthetic data. These methods can also be used to impute missing values from actual data.

 

3. Hybrid Synthetic Data

 

These methods can also be used to impute missing values from actual data. Data that is produced utilizing both genuine and made-up information is called hybrid synthetic data. A similar record from the synthetic data is chosen for each random actual data record, and both are then mixed to create hybrid data. 

 

Benefits of both fully and partially synthetic data are offered. As a result, it has a reputation for offering good privacy preservation with greater utility than the other two but at the expense of taking up more memory and processing time.

 

Also Read | What is Data Pre-Processing?

 

What Justifies the Use of Synthetic Data?

 

Consider a situation in which you are trying to solve an AI issue and are unsure if you should purchase synthetic data to either partially or completely satisfy your data requirements. For your project, synthetic data may be a wonderful fit because:

 

1. Improve model robustness

 

You can access more varied data without needing to collect it for your models. With synthetic data, you may train your model using variations of the same person with various hairstyles, facial hair, glasses, head poses, etc., as well as skin tone, ethnic traits, bone structure, freckles, and other characteristics to generate diverse faces and strengthen it.

 

2. It can be obtained more quickly than "actual" data

 

Teams can generate vast amounts of synthetic data quickly. This is especially useful when the real-life data depends on sporadic events. Teams may find it difficult to get enough real-world data on extreme road conditions when gathering data for a self-driving car, for instance, due to their rarity. 

 

In order to speed up the laborious annotation process, data scientists can set up algorithms to automatically label the synthetic data as it is created.

 

3. It incorporates edge cases

 

A balanced dataset is preferred by machine learning algorithms. Think back to our example of facial recognition. The accuracy of their models would have improved (and in fact, several of these businesses did just this), and they would have produced a more moral model if they had produced synthetic data of darker-skinned faces to fill in their data gaps. Teams can cover all use cases, including edge cases where data is scarce or nonexistent, with the help of synthetic data.

 

4. It guards user privacy information

 

Companies working with sensitive data may encounter security difficulties depending on the industry and type of data. Personal health information (PHI), for instance, is frequently included in patient data in the healthcare industry and must be handled with the utmost security. 

 

Because synthetic data doesn't contain information about actual people, privacy issues are lessened. Consider using synthetic data as a substitute if your team needs to adhere to specific data privacy regulations.

 

When and why is Synthetic Data used?

 

From training navigational robot models to conducting research on radio signal recognition, synthetic data can be used for a variety of tasks. In reality, synthesized data can essentially fulfill any project's objective that calls for a computer simulation to forecast or study actual events. There are a number of important reasons why a company would think about employing fake data.

 

1. Time and money saving

 

If you don't have a suitable dataset, it can be much less expensive to produce synthetic data than it would be to gather it from actual events. The time factor is the same: while real data collection and processing may take weeks, months, or even years for some projects, synthesis can take just a few days to complete.

 

2. Investigating rare data

 

There are situations where accumulating data is risky or infrequent. A collection of exceptional fraud incidents is an example of rare data. Road traffic incidents, to which self-driving cars must respond, may serve as an example of dangerous real data. Then we can use synthetic accidents in their place.

 

3. Addressed Privacy concerns

 

Privacy concerns must be taken into account when sensitive data needs to be handled or supplied to third parties. Making synthetic data, as opposed to anonymization, eliminates all identity traces from the original data, producing a new, reliable data set without jeopardizing privacy.

 

4. Labeling and control are simple

 

Technically, labeling is simple with totally generated data. It is simple to automatically assign labels to trees, people, and animals in a park, for instance, if a photograph of the park is generated. We don't need to pay employees to manually label these items. Additionally, fully synthesized data is simple to control and modify.

 

Advantages and Disadvantages of Synthetic Data

 

Advantages of Synthetic Data

 

As long as the data used by data scientists exhibit correct trends, is balanced, unbiased, and of good quality, they shouldn't care if the data is real or artificial. Enriching and optimizing synthetic data enables data scientists to realize several benefits of synthetic data, including:

 

1. Data quality

 

Real-world data is not only difficult and expensive to gather, but it is frequently inaccurate or biased, which could impair the performance of a neural network. Higher data quality, balance, and variety are ensured with synthetic data. Artificially created data can apply labels and automatically fill in missing quantities, allowing for more precise prediction.

 

2. Scalability

 

Large amounts of data are necessary for machine learning. Finding the right data at the right size to train and evaluate a prediction model is frequently challenging. In order to reach a broader scale of inputs, synthetic data is used to cover the gaps left by real-world data.

 

3. Utilization simplicity

 

It is frequently easier to produce and use synthetic data. It is frequently important to protect privacy, remove errors, or transform data from various forms when gathering real-world data. Synthetic data guarantees ‌all data has a consistent format and labeling, getting rid of errors and duplicates.

 

Disadvantages of Synthetic Data

 

In order to verify that the output is accurate and consistent, especially in large datasets, output control may be complicated. The easiest approach to do this is to compare generated data with original data or human-annotated data. But once more, access to the source data is necessary for this comparison.

 

1. Outliers are challenging to map because synthetic data merely approximates real-world data; it is not a duplicate. Therefore, some outliers that are present in original data may not be covered by synthetic data. However, for some applications, outliers in the data may be more significant than typical data points.

 

2. The quality of the model depends on the data source; it is closely tied to the quality of the original data and the model used to generate the data. The biases in original data may be reflected in synthetic data. Inaccurate data could also be produced by manipulating datasets to produce fair synthetic datasets.

 

The usage of sensitive data introduces new hazards even though data analytics foster fresh insights that can be advantageous to society. It is getting easier to leak private information or economically sensitive content, which could have serious consequences for both people and organizations. 

 

Synthetic data, though not without trade-offs, undoubtedly has a part to play in resolving the conflict between maximizing data utility and protecting privacy concerns