Synthetic Data Generation Techniques

Introduction

Data continues to be an integral part of the world today, from the perspective of daily interactions between humans and machines. There is an undeniable need for more data around the world. Data scientists from several organizations require access to large volumes of data to train and deploy cutting-edge machine learning or deep learning models for solving challenging problems. And this is only the start. Such an ever-increasing demand for additional data poses a challenge. Acquiring fresh data is time-consuming, expensive, or sometimes just impossible. It leads us to the question- Is there a solution to satisfactorily meet this demand? How can data scientists obtain the data they want at the scale they require while maintaining quality, balance, accuracy, and low costs? Simple, synthetic data is the answer.

Synthetic data promises to meet this demand for massive volumes of data. According to a report published by Gartner on synthetic data in June 2021, it is predicted that by the year 2030, most of the data required for AI needs will be synthetic data that is artificially generated through rules, statistical models, and/or simulations. It confirms that synthetic data is here to stay in the coming years.

Read this article to understand the key reasons for the increasing demand for synthetic data in the artificial intelligence field, followed by the benefits offered, challenges faced, the techniques employed for generating synthetic data to solve business problems, and some real-life applications.

Why is synthetic data required?

Synthetic data can be an asset to businesses for three main reasons: privacy concerns, faster turnaround for product testing, and training machine learning algorithms.

Most data privacy laws restrict businesses in the way they handle sensitive data. Any leakage and sharing of personally identifiable customer information can lead to expensive lawsuits that affect the brand image. Hence, minimizing privacy concerns is a top reason why companies invest in synthetic data and synthetic data generation techniques.

For entirely new products, any previous data remains unavailable. Moreover, human-annotated data is a costly and time-consuming process. This can be avoided if companies invested in synthetic data, which can be quickly generated and help develop reliable machine learning models.

What is synthetic data generation?

Synthetic data generation is a process in which new data is created either manually using tools like Excel or automatically using computer simulations or algorithms as a substitute for real-world data. This fake data can be generated from an actual data set or can be completely new if the real data is unavailable. The newly generated data is nearly identical to the original data.

Synthetic data can be generated at any size, at any time, and in any location. Although it is artificial, synthetic data mathematically or statistically reflects real-world data. It is similar to the real data that is collected from actual objects, events, or people for training an AI model.

Real data vs synthetic data

Real data is gathered or measured in the actual world. Such data is created every instant when an individual uses a smartphone, a laptop, or a computer, wears a smartwatch, visits a website, or makes a purchase online. Additionally, real data can also be generated through surveys (online and offline).

Synthetic data is generated in digital environments. Synthetic data is fabricated in a way that it successfully imitates the actual data in terms of basic properties, except for the part that was not acquired from any real-world occurrences.

With various techniques to generate synthetic data, the training data needs for machine learning models can be fulfilled, making the option of synthetic data highly promising as an alternative to real data. However, it cannot be stated as a fact whether synthetic data can be an answer to all real-world problems. This does not affect the significant advantages that synthetic data has to offer.

Benefits of synthetic data

Synthetic data promises to provide following benefits:

• Customizable: It is possible to create synthetic data to meet the specific needs of a business.
• Cost-effective: Synthetic data is an inexpensive option when compared to real data. Imagine an automotive manufacturer requires access to vehicle crash data for vehicle simulations. In this case, the real data will be more expensive to obtain than creating synthetic data.
• Quicker to produce: Since synthetic data is not captured from real-world events, it is possible to generate as well as construct a dataset much faster with suitable tools and hardware. This means that a huge volume of artificial data can be made available in a shorter period.
• Maintains data privacy: Synthetic data only resembles real data, but ideally, it does not contain any traceable information to the real data. This feature enables the synthetic data to be anonymous and good enough for sharing purposes. This can be a boon to healthcare and pharmaceutical companies.

Characteristics of synthetic data

Data scientists aren't concerned with the fact that the data they work upon is real or synthetic in nature. The quality of the data, with the underlying trends or patterns, and existing biases, matters more to the data scientists.

Here are some notable characteristics of synthetic data:

Improved data quality: Real-world data, other than being difficult and expensive to acquire, is also likely to be vulnerable to human errors, inaccuracies, and biases, all of which directly impact the quality of a machine learning model. However, companies can place higher confidence in the quality, diversity, and balance of the data when generating synthetic data.

Scalability of data: With the increasing demand for massive amounts of training data, data scientists are pressed to opt for synthetic data. It can be adapted in size to fit the training needs of the machine learning models.

Simple and effective: Creating fake data is quite simple when using algorithms. But it is important to ensure that the generated synthetic data does not reveal any links to the real data, is error-free, and does not have additional biases. Data scientists enjoy complete control over how synthetic data is organized, presented, and labeled. That indicates that companies can access a ready-to-use source of high-quality, trustworthy data with a few clicks.

Where can synthetic data be used?

Synthetic data finds applicability in a variety of situations. Sufficient, good-quality data remains a prerequisite when it comes to machine learning. At times, access to real data might be restricted due to privacy concerns, while at other times, it might occur that there isn't enough data to satisfactorily train the machine learning model. Sometimes, synthetic data is generated to serve as complementary data, which helps in improving the machine learning model.

Many industries can reap substantial benefits from synthetic data:

• Banking and financial services
• Healthcare and pharmaceuticals
• Automotive and manufacturing
• Robotics
• Internet advertising and digital marketing
• Intelligence and security firms

Types of synthetic data

When opting for the most appropriate method for creating synthetic data, it is essential to be aware of the type of synthetic data required to solve a business problem.

Fully synthetic and partially synthetic data are the two categories of synthetic data. Fully synthetic data does not have any connection to real data. This indicates that all the required variables are available, yet the data is not identifiable.

Partially synthetic data retains all the information from the original data except the sensitive information. It is extracted from the actual data, which is why sometimes the true values are likely to remain in the curated synthetic data set.

Different varieties of synthetic data

Text data: Synthetic data can be artificially generated text in Natural language processing (NLP) applications.

Tabular data: Tabular synthetic data refers to artificially generated data like real-life data logs or tables useful for classification or regression tasks.

Media: Synthetic data can also be synthetic video, image, or sound to be used in computer vision applications.

Techniques to generate synthetic data

For building a synthetic data set, the following techniques are used:

Based on statistical distribution: The approach here is to draw numbers from a distribution, i.e., by observing real statistical distributions, similar fake data can be reproduced. There are certain situations where real data simply does not exist. If a data scientist has a thorough understanding of the statistical distribution in real data, then he can create a dataset containing a random sample of any distribution. This can be achieved by using a statistical probability distribution such as the normal distribution, exponential distribution, chi-square distribution, lognormal distribution, and more. The accuracy of the trained model will be heavily dependent on the data scientist's expertise in the scenario.

Based on an agent to model: In this method, a model is created that explains an observed behavior, and then it generates random data with the same model. This is essentially fitting actual data to known distribution data. Then businesses can use the Monte Carlo method for synthetic data generation. Besides this, machine learning models like the decision trees can also be used to fit the distributions. However, data scientists need to keep an eye on the prediction as the decision trees generally tend to overfit due to simplicity as well as going up to full depth.

Also, in some cases, a part of real data is available. Here, companies can use a hybrid approach to create synthetic data, i.e., build a part of the dataset based on statistical distributions and generate the other part of the synthetic data using agent modeling based on real data.

Using deep learning: Techniques to generate synthetic data include the use of Deep learning models that employ a Variational autoencoder (VAE) or Generative Adversarial Network (GAN) models.

VAEs are a type of unsupervised machine learning model. They consist of encoders that compress and compact the original data while the decoders analyze this data to generate a representation of the actual data. The main goal of a VAE is to ensure that both input and output data remain extremely similar.

GAN models or adversarial networks are two competing neural networks. The first network is the generator network that is responsible for creating synthetic data. The second network is the discriminator network that functions by trying to determine which dataset is fake by comparing the generated synthetic data with real data. Upon identifying a fake dataset, the discriminator notifies the generator. The generator then modifies the next batch of data fed to the discriminator. Thus, the discriminator improves over a period in detecting fake datasets. This type of model is frequently used in the healthcare sector for medical imaging and the financial sector for fraud detection.

There is another technique being used by data scientists to generate additional data called Data Augmentation. However, it should not be confused with synthetic data. Data Augmentation is simply a process where new data is added to an existing real dataset. For example, generating multiple images from an existing image by changing the orientation, brightness, zoom, and more. Sometimes, only personal information is removed from the actual data set before use. This is called data anonymization, and a set of such data is also not to be considered synthetic data.

Tools for generating synthetic data

Few python-based libraries can be used to generate synthetic data for specific business requirements. It is important to select an appropriate Python tool for the kind of data required to be generated.

The following table highlights available Python libraries for specific tasks.

All these libraries are open-source and free to use with different Python versions. This is not an exhaustive list as newer tools get added frequently.

Challenges and limitations for generating and using synthetic data

Although synthetic data offers several advantages that can help businesses with data science initiatives, it nevertheless has certain limitations:

1. Reliability of the data: It is a well-known fact that any machine learning/deep learning model is only as good as its data source. In this context, the quality of synthetic data is significantly associated with the quality of the input data and the model used to generate the data. It is important to ensure that there are no biases in source data else; those may be very well reflected in the synthetic data. Additionally, the quality of the data should be validated and verified before using it for any predictions.
2. Replicating outliers: Synthetic data can only resemble real-world data; it cannot be an exact duplicate. As a result, synthetic data may not cover some outliers that exist in genuine data. Outliers in the data might be more important than normal data.
3. Requires expertise, time, and effort: While synthetic data might be easier and inexpensive to produce when compared with real data, it does require a certain level of expertise, time, and effort.
4. User acceptance: Synthetic data is a new notion, and people who have not seen its advantages may not be ready to trust the predictions based on it. This means that there is first a need to create awareness about the value of synthetic data to drive more user acceptance.
5. Quality check and output control: The goal of creating synthetic data is to mimic real-world data. The manual check of the data becomes critical. For complex datasets generated automatically using algorithms, it is imperative to ensure the correctness of the data before implementing it in machine learning/deep learning models.

Some real-world applications of synthetic data

Here are some real-world examples where synthetic data is being actively used.

1. Healthcare: Healthcare organizations are using synthetic data to model and create a variety of tests for certain conditions for scenarios where actual data does not exist. In the field of medical imaging, synthetic data is being used to train AI models while always ensuring patient privacy. Additionally, they are employing synthetic data to forecast and predict trends of diseases.
2. Agriculture: Synthetic data is helpful in computer vision applications that assist in predicting crop yield, crop disease detection, seed/fruit/flower identification, plant growth models, and more.
3. Banking and Finance: Banks and financial institutions can better identify and prevent online fraud as data scientists can design and develop new effective fraud detection methods using synthetic data.
4. Ecommerce: Companies derive the benefits of efficient warehousing and inventory management as well as an improved customer online purchase experiences through advanced machine learning models trained on synthetic data.
5. Manufacturing: Companies are benefitting from synthetic data for predictive maintenance and quality control.
6. Disaster prediction and risk management: Government organizations are using synthetic data for predicting natural calamities for disaster prevention and lowering the risks.
7. Automotive & Robotics: Companies make use of synthetic data to simulate and train self-driving cars/ autonomous vehicles, drones, or robots.

Future and outlook of synthetic data

Earlier in this article, we have seen the different techniques and the advantages of synthetic data. But then, two questions come to mind 'If synthetic data is so great, why isn't everyone using it?' and 'Can synthetic data completely replace the real data?'

Yes, synthetic data is a smarter and more scalable substitute to real-world records. But there's more to it! It is essential to realize that creating accurate synthetic data requires more effort than just automating it using an AI tool. Generating correct synthetic data requires data scientists with truly advanced knowledge of AI and specialized skills in handling sophisticated frameworks. Bias in the dataset needs to be avoided at all costs as trained models on such data will be skewed and far from reality. This calls for timely adjustments to the dataset, if possible, to create a true representation of the actual data or the AI models to consider the present biases. This way, a company can ensure that generated synthetic data can fulfill the goal for which it was created. Nevertheless, synthetic data aims to facilitate the data scientists to accomplish new and innovative things that will be tough to achieve with real-world data only in the data-driven future.

Conclusion

There are certain situations where synthetic data can address the data shortage or the lack of relevant data within a business or an organization. We also saw which techniques can help to generate synthetic data and who can benefit from it. Furthermore, we discussed some challenges involved in working with synthetic data, along with a few real-life examples of industries where synthetic data is being used.

Real data will always be preferred for business decision-making. But when such real raw data is unavailable for analysis, realistic data is the next best solution. However, it needs to be considered that to generate synthetic data; we do require data scientists with a strong understanding of data modeling. Additionally, a clear understanding of the real data and its environment is crucial too. This is necessary to ensure that the data being generated is as close to the actual data as possible if it is available.