Data is the lifeblood of any business, but it can be expensive and time-consuming to collect. This is where synthetic data comes in.
In some instances, synthetic data is a cost-effective and efficient way to generate the data you need without the hassle of collecting it yourself.
It is used in many activities, including as test data for new products, for model validation, and for AI model training.
In this article, we’ll take a look at what synthetic data is, how it’s used, and some benefits it can offer your business.
Beginner’s Guide to What is Synthetic Data
If you’re new to the world of synthetic data, here’s a beginner’s guide on what you should know. Algorithms generate artificial data.
It can be used to train machine learning models and also to test data-driven applications. This allows you to avoid having to gather a lot of data by yourself or pay someone else to do it for you.
When you use such data, you’re not just using an algorithm to generate a file. You’re actually “creating” the data that the algorithm needs to get accurate results and train properly.
This is a key distinction that sets synthetic data apart from machine learning data.
So, where do you get this type of data? The most basic kind of synthetic data can be generated by simply “playing” around with algorithms.
If you play around with a model enough, even if you create models that don’t make sense and come up with dire predictions, you will still get some of this data.
Importance of Synthetic Data
Synthetic data is data that is generated by artificial means, usually by computer programs. It is sometimes used to supplement real data to test machine learning algorithms.
As machine learning becomes more popular, the need for this data increases. This is because machine learning algorithms require a large amount of data to be effective.
Who Uses Synthetic Data?
Researchers and data scientists use it in many fields. Some use it to test algorithms and tools, while others use it for research purposes.
One of the most popular fields of machine learning is computer vision. In this field, synthetic data is used to test algorithms and the software used to train these algorithms.
In addition to AI, many apps can use this data, including image processing, the Internet of Things, and natural language processing.
What are the Limitations of Synthetic Data?
While it can be used to train machine learning models, it has several limitations.
First, it is often generated using simplified models that do not accurately reflect
reality.
This can lead to models that perform well on synthetic data but poorly on real-world data.
The quality of such data depends heavily on the quality of the input data and the model used to generate it.
The data may be biased due to input data bias. This data can only mimic the real-world data, but not make an exact copy.
This also means it may not throw up outliers that the original data has.
Finally, while synthetic data may improve a model’s performance, it is usually not representative of the actual data used in production.
It is typically a minimal subset of the data in production, and it may not include the same features a real-world application might use.
This can lead to inaccuracies in a model’s predictions.
Still relying only on real data? See why leading teams are adding synthetic data to move faster and smarter >>>> Talk to our data experts
How is Synthetic Data Created?
This type of data is generated by artificial intelligence algorithms that produce data that appears to be real but is not. Some artificial intelligence algorithms create data using simulated humans, while others generate data from natural language.
For example, a machine learning algorithm might be used to predict sentiment in sentences that contain the word “good” in context.
Another algorithm might learn to predict a financial asset price based on the stock market prices for that asset. In all of these cases, the data is generated by computer programs.
This artificial data plays a significant role in building accurate models that can predict outcomes.
You can create synthetic data by developing a generative model from an existing dataset.
This creates new data that is identical to the original data. Such a generative model learns from voluminous real-world datasets to develop data that accurately mimics the real world.
There are three such models: Generative Adversarial Networks or GANs, Variational Auto Encoders or VAEs, and Autoregressive models.
Use Cases for Synthetic Data
Synthetic data can be used to develop test cases for software development and testing. It can also be used to create training data for machine learning algorithms.
A synthetic data generator can be run multiple times to simulate different input data sets. This data is processed using machine learning algorithms and validated using test sets.
A data scientist can generate it to meet the company's needs.
Such data sets are typically needed for model development and testing or for building training data for machine learning algorithms.
When creating test cases for software development and testing, synthetic data can be used to generate test cases based on actual user data. It can also be used to build training data for machine learning algorithms.
If a data scientist has created the data sets for model development and testing, synthetic data can be used to validate the trained models and the software product once it is built.
Challenges of Synthetic Data
A significant challenge with this data is that it can be difficult to generate data that accurately represents reality.
This is because it is often hard to know all the factors that contribute to a real-world phenomenon.
Several aspects of data can be challenging to replicate synthetically. The type of data generated can also be hard to reproduce.
For example, you cannot replicate naturally random features in a synthetic data set.
Data generated in different environments can also be hard to reproduce. All Y data has been validated; it can be used in subsequent runs to predict outcomes.
Why Use Synthetic Data?
There are many reasons to use this type of data. Perhaps the most obvious is that it can generate data that is not available in the real world.
This is useful for training and testing machine learning models, as well as for debugging them.
Another reason to use this data is that it can be generated very quickly and cheaply. Once a set of features is created and tested, it can then be used to develop new data.
Synthetic data allows for better predictive analytics because the models are trained on real data that matches the original data but does not necessarily represent the real world as closely as real data does.
When you look at this data, the first thing that might catch your eye is its color. A good way to think about this type of data is as “fake” or “manipulated” data.
Although it is not truly random, it does not represent any real-world data.
So why would you use synthetic data in machine learning models? Such data can be used for many different purposes.
It is often used to support a modeling workflow, in which the model is first trained on real data and then evaluated on synthetic data to assess performance and identify issues.
Still relying only on real data? See why leading teams are adding synthetic data to move faster and smarter >>>> Talk to our data experts
Another reason to use this data is that it provides training data for a dataset that does not actually exist in the real world.
In general, any time you want to test the predictive power of a machine learning model, it is a good idea to use this type of data as a substitute for real data.
Conclusion
In today’s business world, data is everything. Collecting data can be expensive and time-consuming, but synthetic data can provide a cost-effective and efficient solution. In many cases, synthetic data is a better option than collecting real data because it can be used for activities like testing new products, validating models, and training AI models.
