Data is the primary requirement for any business or technical evolution. The widespread use of artificial intelligence needs an immense amount of data to be precise and efficient. At times, accessing data is not possible for various reasons, and that’s where synthetic data is used. The market growth for synthetic data is expected to hit $2.1 billion by 2028 with a CAGR of 45.7%.
Though this mock data is created using artificial processes, it still carries strong statistical properties of the original data it is made from. Such that it can support or swap real datasets. Let’s dive deep into how it is created, its uses, types, and pros & cons.
What is Synthetic Data?
Synthetic data is the data that is not naturally generated by people or actual events. Instead, it is made with the help of AI and machine learning (its deeper layers like DL, Gen AI). This data parrots real data and acts as a placeholder for test data used to train AI models.
Example: For instance, when you solve maths. If the real question is, what is 6n – 2n? But the practice question can be, what is 4n – 4n? So, you see the outcome and logic will be the same to get the same answers, but only the input question is simulated.
6n – 2n is real data.
4n – 4n is replicated or synthesized data.
What is the Need for Synthetic Data?
We know ‘crisis or necessity is the mother of invention.’ There are various reasons for the creation of synthetic data, as data is needed everywhere for everything. Let’s look at some of the important reasons.
Speed and effort
Data is collected manually, which is slow speed as you search through surveys, sensor data, event outcomes, etc. Moreover, it needs more resources. This process can take up to months or years to collect the amount of data required for AI training.
Data segregation
This is another challenge that backs the need for synthetic data. Before data is fed to models for training, it must be labelled according to formats and types. For instance, vehicle automation training needs ample visual data with accurate pixel-level segmentation, and it is nearly impossible to do manually.
Privacy restrictions
Some laws and regulations protect individuals’ personal information/data from being used without consent, and it is necessary. Hence, the data cannot be exchanged or transferred freely between organizations, according to privacy constraints. This hinders the collaboration and innovation in AI.
Need for special data
The general data resources, Microsoft COCO, ImageNet, etc., exist. But the majority of the business applications require niche data, which is even harder to get labeled at scale.
Gen AI needs
Artificial intelligence runs on data; it is clear, but its advanced brand of generative AI needs more precise and extensive datasets to train the models. The models must be fine-tuned, catch small details, and generate as accurate content as possible. All of it needs huge amounts of data.
Types of Synthetic Data
In technology, everything has deeper and deeper layers or segregations, and so does synthetic data. It is of three types:
1. Fully Synthetic
This data does not contain any real-world information. It is entirely newly generated data. It counts the attributes, patterns, and relationships as of real data but mimics it neck and neck.
For instance, in financial sectors, at times, there are not enough suspicious transactions to effectively train AI models for fraud detection. Then organizations can make fully synthetic data that exactly depicts fraudulent transactions. This can help improve model training.
2. Partially Synthetic
It is where synthetic data replaces a small portion of real data with fabricated information. It is useful in protecting sensitive parts of a dataset.
In case of dealing with customer data analytics, you can fabricate elements like name, contact details, and other private information, which can harm a person’s privacy.
3. Hybrid
It is a mix of fully synthetic data with real datasets. The original data is randomly coupled with their synthetic counterparts. Hybrid synthetic data serves the purpose of analyzing and driving insights from customer data without the risk of tracing sensitive information to a specific customer.
Methods to Make Synthetic Data
Generating such a type of highly simulated data involves sophisticated technical processes. Because it needs to reflect the structure and statistical properties of real-world data. Here are different approaches used:
1. Counting statistics
Here, the underlying statistics of real data are studied carefully. The statistical distribution, such as normal, exponential, or chi-square, variation, correlation, and mean. Then, by random sampling from this distribution, new data points can be generated.
For instance, if students are scoring between 70 and 90 marks, this pattern will be studied. And we see that too low or too high scores are, and thus they are nearly touching the max and minimum mark range of 70 and 90. So we will create the fake scores following a similar pattern. Like this, the marks are not of any real student, but they follow the same statistical behavior.
2. Machine learning approach
Machine learning models are widely used to create artificial data, as their fine-tuning can catch complex patterns and relationships between data. Some popular methods are:
- Generative Adversarial Networks (GANs) are two neural networks that fight head-to-head to make realistic data. They fight because one is a generator (creates the output) and the other is a discriminator (finds flaws in the output). Over time, they improve each other, creating accurate, realistic data.
- Variational Autoencoders (VAEs) construct fake data by encoding real data into a latent space for later decoding into synthetic data. Particularly great at forming structured data and images.
- Transformer-based models the most excelling arm of DL models in sequential data. Best for producing synthetic text, speech, and even code. They use an encoder, a self-attention mechanism, and decoders to understand meaning & context, decide the most important word, and then generate output by choosing the most appropriate next word, respectively.
3. Agent-based Modelling
In this ABM approach, complex systems are created in virtual environments, where individuals, also called agents, operate on preset rules. They interact with other agents and the environment. The activities generate simulated data.
For example, an agent-based model for an epidemic situation is made, which represents individuals in a population as agents. Now, understanding interactions between agents, synthetic data such as the rate of contact and chances of infection spreading can be generated. This data can help mitigate and aid such difficult situations.
Challenges in & Benefits of Synthetic Data
Though it all seems very fascinating to generate this type of data, work with elite techniques, and mimic real-world scenarios. But it is easier said than done. Of course, it has benefits, but hindrances too. This table will highlight the benefits and challenges of using and making synthetic data.
| Aspect | Benefits | Challenges |
|---|---|---|
| Privacy & Security | It protects the privacy of a person by saving their real information from being used | Still, there is doubt whether it is fully masked and can not be reverse-engineered |
| Availability of data | Can produce unlimited data for better training of AI with uncommon scenarios too | Uncommon, realistic data creation is difficult |
| Time and cost | Saves time, effort, and money with automated collection & labelling | Sophisticated models for generating this man-made data can be expensive |
| Biases | Synthetic data can reduce biases while training AI models. If you have real data that looks biased, you can create fake data with a similar pattern | Can still have or multiply existing biases |
| Scalability | Can be produced on a large scale | Production at scale is easy, but consistency and realism are difficult |
| Testing & Simulation | Testing can be safer and less risky | Matching reality is not that easy; some virtual environments may not exactly match the complications of the real world |
| Access to data & sharing | Can easily travel across departments, organizations, and teams | It is hard to access real data sometimes due to safety regulations |
Conclusion
All big bulls in the field of technology and artificial intelligence, from NVIDIA to Amazon to Google and more, are highly advocating synthetic data. Because it offers better collaboration opportunities, flexibility, and experimentation. Moreover, it is beneficial in dealing with privacy concerns in data sharing done for training AI models. There are various ways of creating imitated data that we discussed. As mentioned above, there are also significant challenges while reaping the benefits.
Use it wisely and avoid a scarcity of data, even when it is difficult to obtain data.
