Synthetic data is the information that is generated artificially. Computer algorithms or simulations play an important role to generate it.
Companies go for synthetic data generation only for two basic reasons. The first one is when the production or real data is not available. The second one is when it is necessary to keep the real data private in order to comply with the security rules and regulations. This is the reason synthetic data comes in handy for major industries like eCommerce, manufacturing, agriculture, and health.
Feeling curious to learn about it more? Well, you should read this article then. In this article, I’ll discuss what this artificial data is, its types, and the techniques and tools used to generate it.
What does synthetic data mean?
Looking at the term synthetic itself, which means artificial or fake, makes the definition of synthetic data easier to digest.
So, synthetic data is the information that’s artificially created rather than collected from real-world events. The real heroes to generate this artificial data are the algorithms.
Along with that, it shows its potential to test the operational data dataset. What it’s mainly used for? To train the synthetic data for deep learning models and to validate mathematical models.
When it comes to pen down its significant advantage, it reduces the limitations while using sensitive data. With that, it becomes possible to create specific data requirements that you can’t do with real data.
So, most of the organizations create synthetic datasets for software testing and quality assurance. Because there is too much data so, they can create diverse testing scenarios.
However, this fake data has some bottlenecks, too. For example, it can have inconsistencies when trying to mimic the complexity of real data. Also, it cannot fully replace real data because you still need accurate data to get useful results.
Why do we need synthetic data?
Well, there are three major reasons that make synthetic data a must-have. They are:
Privacy concerns
Protecting customers’ personally identifiable information (PII) is one of the major considerations for every business. Even if their minor information gets leaked, businesses are more likely to face hefty fines and lawsuits. As a result, it will damage the brand’s reputation. However, synthetic data creation reduces privacy concerns and saves from lawsuits.
Product testing
Let’s say you’re going to launch an entirely new product. When there is no product like yours, where will you get the production data from in order to test the product? Of course, there is nothing to get. However, with fake data, there is a light at the end of the tunnel. With this data, you can create as many testing cases as you want for your product testing. At the same time, there could be a human-annotated data option, but that can be too costly in terms of time and money.
Train machine learning algorithms
Machine learning requires a lot of training data to mimic accordingly. Here, real data is no doubt a good option, but that may not be enough. So, opting for synthetic data is good to go. With that done, professionals can train the algorithms in different bits that real data doesn’t offer.
What is synthetic data generation?
Synthetic data generation is the process of generating new data either by hand, with tools like Excel, or automatically, using computer simulations or algorithms. This generated data serves as the replacement of real-world data.
So, this fake or artificial data can be generated as per the real data set, or it can generate a completely new data set when there is no real data. This way, synthetic data looks like the real data. Companies can generate it in any size, time, or location, according to what suits them.
While it is fake data, it still closely reflects the mathematical or statistical properties of real-world data. That’s just because the data sets of the real objects or events are used to make synthetic data mimic them.
What’s the difference between real data and synthetic data?
To make you more sure about synthetic data, let’s discuss its differentiation with real data.
All right, as the name suggests, real data is data collected from the real world. It’s the data of the real users of their specific actions. For example, when they use smartphones or laptops, visit websites, and even when purchasing anything online. All that data is gathered through different surveys in both online and offline modes.
On the flip side, synthetic data comes into being in digital environments. This data captures the basic properties of the real data and then imitates them. That means professionals, whatever feed it will be mimicked.
So, with the availability of various synthetic data generation methods, getting the training data for machine learning models has become easier. Hence, it’s true that fake data can be a better choice than real data. Remember, it’s not a silver bullet for every problem. But this uncertainty can’t undermine its potential benefits.
What are the benefits of synthetic data?
Here, we’ll be counting down the four major benefits of this fake data:
Customizable
Synthetic data can be adjusted for specific purposes. For example, companies can use it for either software testing or for machine learning models.
Cost-effective
Finding and collecting the real data can cost too much. But fake data is readily available to meet any requirement. For instance, automotive manufacturers will have to spend more time and money collecting real vehicle crash data than synthetic data.
Faster to generate
Using the right tools and technologies, creating the synthetic data sets takes a short period. Companies don’t have to do surveys to capture the real data. The effective tools present it without letting you do any hassle.
Keeps the data privacy intact
Keeping the users’ privacy under consideration, synthetic data doesn’t give any traceable information about the actual data. This feature protects sensitive data and, as a result, leaves no room for privacy breaches. So, it sounds perfect for companies like healthcare and pharmaceutical.
What are the characteristics of synthetic data?
Data scientists aren’t concerned about the data type, whether it’s real or synthetic. They care more about whether the data is of high quality, has essential patterns and trends, and is unbiased.
So, the following are some significant characteristics of synthetic data:
Enhanced data quality
Along with being expensive and difficult to collect, real data can also have other downsides. For example, there could be some human errors, biases, and inaccuracies. Having such low-quality data could surely compromise the quality of the ML models. However, if companies step ahead to generate synthetic data options, there will be high-quality, unbiased, and diverse data.
Data’s scalability
Since the demand for the training data is at its peak so, there is no other way to see than synthetic data. It can be customized to a suitable size to train the ML models.
Simple and effective
Creating fake data doesn’t require any rocket science. In fact, using the right algorithms, the process becomes a breeze. But, it is also important to keep in mind that the synthesized data doesn’t expose any link to the actual data.
Data scientists have full control over organizing, presenting, and labeling synthetic data. This shows that companies can have high-quality and unbiased data within a few clicks.
What are the uses of synthetic data?
The applications of synthetic data can be seen in different situations. For ML, it comes to be enough and suitable. When it comes to real data, there might be restrictions due to the insecurity of privacy damage, or there might be the possibility that the data isn’t enough to meet the requirements.
There are many industries that can get the best of synthetic data creation, such as:
- Healthcare and pharmaceuticals
- Automotive and manufacturing
- Government and Public Sector
- Finance and Banking
- Robotics
- Internet advertising and digital marketing
- Intelligence and security firm
What are the two major types of synthetic data?
Before you get to know the different methods of creating synthetic data, it’s important to know the two major types of this fake data.
Fully synthetic data
This type has nothing to do with the real data. That means it has necessary variables, but it does not contain any identifiable information.
Partially synthetic data
In this type, the data originates from the original source but excludes the PII or sensitive information of the source. Because it has a connection to the original source, which is why there could be some true elements or characteristics in the arranged synthetic data set.
What are the different forms of synthetic data?
Here are some examples of synthetic data in different forms:
Text data
This data can be in the form of artificially created text in natural language processing (NLP) applications.
Tabular data
This means that the fabricated data is similar to the tables, logs, or statistics of the real data. Tabular data helps in classification tasks.
Media
Synthetic data can also appear in the form of video, image, or sound, which are used in computer vision tasks.
How to generate synthetic data?
Here are some of the best synthetic data generation methods:
Statistical distribution
This method is useful for drawing random numbers from the real statistical distribution. As a result, there will be a synthesized data replicating the similar factual data. This method is helpful for situations where real data is not available.
If data scientists have thorough information on statistical distribution in real data, they can create a data set with a random sample of distribution. To achieve that, professionals can leverage the chi-square distribution, normal distribution, exponential distribution, and more.
The more a data scientist is experienced in this method, the more accuracy the model will have.
Agent to model
Using this method, you can create a sample that can explain how real data behaves. Once the model is established, it can generate random data that follows the same patterns as the observed data. This process involves fitting the actual data to a known distribution. So, this method can be helpful in synthetic data generation.
Besides this, many other ML methods can fit the distributions. However, when data scientists go for future predictions, decision trees can become overly complex. This is because there is too much detail in the training data.
Sometimes, there is just a bit of information about the real data available. In such cases, businesses can adopt a hybrid approach to develop a data set. This data set relies on statistical distributions and produces fake data with the help of agent modeling following the real data.
Deep learning
The use of deep learning models such as Variational autoencoders (VAEs) and Generative Adversarial Networks (GANs) are utilized to produce synthetic data.
VAEs
VAEs have further encoders and decoders for different functions. For example, encoders compress and compact the real data in a specific size while the decoders overview the real data and then create synthesized data reflecting the real.
The major reason for using VAEs is that they ensure the similarity between both input and output.
GANs
An adversarial network, also known as the discriminator network, works by finding a fake dataset. Once the fake data set gets spotted, the message goes to the generator to tell them about this discrimination. Then, the generator changes the next batch of the data. That way, the discriminator improves the process of detecting fake data sets.
What are different synthetic data generation tools?
Here is a list of different tools that help to generate artificial data:
Datomize
Datomize has a Learning model. Many top-notch banks from all over the world use it. Using Datomize is too simple. It helps to link your enterprise data services together and manage complex data structures and relationships across various tables. With that, it will offer you behavioral features out of the raw data. Hence, you can create data that is similar to the original.
MOSTLY.AI
This synthetic data tool has gained immense popularity. Its great focus on ensuring high-quality privacy with AI features makes it a must-have. That means you can extract fake data from the original source without any threat of privacy breach.
Synthesized
Synthesized is a one-stop solution for data provisioning, data augmentation, collaboration, and secured sharing. When you use this tool, you can use different versions of the actual data. That’s not what it offers. The tool also tests all versions with diverse test data. So, this feature helps to pinpoint “what is missing” and “what’s the sensitive information.”
Hazy
This synthetic data creation tool primarily focuses on helping the fintech industries by training the raw banking data. With its usage, developers can improve their analytics workflows without any deceptive practices when gathering real customer data. It lets you generate the complex data of the financial service generations, which you can protect in your silos to use it later. The government doesn’t allow the sharing of financial data for research purposes. Or that may be limited. That’s where Hazy shines to help you access financial data.
Sogeti
Sogeti stands out among others because it uses Artificial Data Amplifier (ADA) technology. ADA reads and interprets any data, whether it is structured or unstructured. On top of that, this technology uses deep learning methods to imitate the ability to recognize different patterns, objects, or features.
Gretel
Gretel acts as a self-proclaimed tool that creates synthetic data in the form of statistics similar to the real data. This solution also focuses on shielding customers’ sensitive information. During the process of training the model to generate fake data, it compares the real-time information. And then use the sequence-to-sequence model for giving predictions when creating new data.
CVEDIA
It has different machine language algorithms to give computer vision solutions in order to improve object recognition systems. As well as CVEDIA improves AI rendering. To develop AI applications and sensors, this tool proves it mettle.
Rendered.AI
Rendered. AI is helpful in creating physics-centric datasets for robotics, satellites, and autonomous vehicles. Here, engineers don’t have to modify the code itself to make quick changes to the datasets. They simply go for a change in API, and that’s it. So, they can generate data on the browsers without significant computing potential. As a result, operation on ML workflows will be easier.
Oneview
This data science tool plays an important role in defence intelligence. Basically, it uses satellite images and remote sensing technologies. However, by using satellites, drones, and cameras, it can detect images even if they are blurred. That way, this algorithm creates the virtual image in a way that makes them very similar to the real-world objects.
MDClone
Our list finishes rolling out on the best tool that is helpful, particularly in healthcare businesses. Using this tool, businesses can tap into the pool of the patients’ data to make informed decisions. It’s not easy or quick for researchers to get clinical data, so they have to rely on intermediaries. MDClone gives a systematic approach to accessing the data without compromising the patients’ sensitive information.
What are Python-based libraries to generate synthetic data?
There are different Python-based libraries that businesses can use to generate artificial data for specific purposes. So, here is a list of the libraries that are suitable for different kinds of data to be created:
| Python Library | Why use it? |
| Data Synthesizer, SymPy | To increase data points |
| Fakeer, Pydbgen, Mimesis | To generate different fake addresses, contacts, names, or even data information |
| Synthetic Data Vault (SDV) | To generate relational data |
| Platipy | To generate completely fresh sample data |
| TimeSeriesGenerator, SDV | For timeseries data |
| Gretel Synthetics, Scikit-learn | For automatically generated data, no human intervention |
| Mesa | For complicated scenarios |
| Zpy, Blender | To generate image data |
| Blender | To create video data |
So, this list of libraries is actually a goldmine to meet your distinct requirements. The best part? They are open-source, and you can use them free of cost using different Python versions. But these are not the only libraries; there are many others that are appearing in the market with time.
What are the challenges and limitations of synthetic data usage?
Discussing just advantages and overlooking the limitations isn’t fair, right? However, it’s important to list the limitations of using synthetic data.
Reliability of the data
You’ll surely agree with the fact that quality output is only possible with quality input. The same goes for the machine learning/deep learning model. Their quality depends on their data source. And in that context, high-quality synthetic data for ML comes out through the high-quality input data and the model to create the data.
That means it’s important to ensure that the actual data is free of biases. If any bias remains in the actual data, synthetic data will also include them. So, while thinking about how to generate synthetic data for machine learning, professionals have to intervene to ensure the input data is error-free.
Moreover, professionals should also pay attention to validate and verify the quality of the data before they use it for predictions.
Mimicking outliers
Synthetic data mimics the real-world data rather than being an exact duplicate of it. Therefore, synthetic data shouldn’t include the outliers that are in the actual data set. Outliers in the data hold more importance than normal data.
Demands expertise, time, and efforts
No doubt synthetic data is easy to collect and produce as compared to real data. But that’s not as simple as it looks. Behind the generation process, it demands an amalgamation of expertise, time, and effort.
Awareness
This fake data realm is very new and not exposed to everyone. That way, users might not trust its advantages as they haven’t used or seen it. In that aspect, the real thing to do is spread awareness of how this data can simplify our lives.
Quality check of complex datasets
While the fake data imitates the real-world data, it still needs to be manually checked. This is important, particularly in complex datasets that are generated automatically before being fed to the ML models.
What are the real-world applications of synthetic data?
There are different areas where the applications of synthetic data can be seen, for example:
Healthcare
The healthcare sector uses this data to create models and different datasets for testing different conditions that real data lacks. For example, in the domain of AI imaging, fake data is helpful to train different models while maintaining the sensitive information of the patients. As well as, they are using it to predict different diseases. This helps to deal with the diseases beforehand.
Agriculture
The agriculture field also gets the best of this data. For example, with computer vision applications, the data can help to predict different crop diseases, crop yield, fruit and flower identification, and more.
Banking and Finance
This is one of the most sensitive domains where there are more chances of fraud. Hence, with the help of synthetic data generation, data scientists are creating effective methods that can help detect fraud in different ways.
eCommerce
eCommerce reaps the benefits of this data by developing effective ML models. They help in productive inventory management and warehousing. Not to mention, these models can present the customer’s online purchase experiences. And that can help businesses make informed decisions.
Manufacturing
For predictive maintenance and quality control, manufacturing companies are using fake data.
Calamities prediction and risk management
Natural calamities can appear at any time without giving any red signal before. However, synthetic data predicts what calamity is near to happening. That way, the government manages how to prevent it and lower the risks.
Automotive & Robotics
You might have self-driving cars, vehicles, or drones once in your life. Companies are taking advantage of this data to simulate and train them.
What is the future of synthetic data?
So far, we have discussed the methods and advantages of synthetic data, but the real question is still unanswered. “Will synthetic data take the place of the real-world data?” Or “Is future bound with synthetic data?”
The crystal clear answer is yes in both cases. But to generate quality fake data, there must be human expertise and efforts rather than solely relying on AI tools. That means unbiased and top-notch quality synthetic data requires professionals who are proficient in synthetic data generator tools. Also, they must be adept at managing risky frameworks.
Since this data is good to reflect the real-world environments, so data scientists can achieve innovative things with it. And because it saves our time from collecting real data, it is wise to say that synthetic data is the future.
Final Thoughts
In conclusion, synthetic data generation is a far better option than real data. We’ve discussed that when the real data is not enough to meet our requirements, this fake data comes in handy. Also, we have shed light on different techniques to generate it as well as its major benefits. Plus, we’ve talked about the applications of this data in the real world and the challenges that appear when using it.
We can’t abandon real data at all. For business decision-making, real data is a good way to be involved. But if real data is not enough, synthetic data can be the next solution. However, to generate this fake data, there must be experienced data scientists who have a holistic understanding of data modeling. Lastly, the generated data must be similar to the real data but not an exact duplicate.