Synthetic Data vs Tagged Data,
which one to choose?
January 3, 2023
There is a debate raging in the world of data right now: synthetic data vs tagged data. Which one should you choose for your business? The answer, of course, depends on your needs.
Choose the right Data
When it comes to AI data, there are two main types: synthetic data and tagged data. So, which one should you choose?
Large synthetic scale
If you need to quickly generate large amounts of data for training machine learning models, then synthetic data is the way to go.
High Quality
If you need high-quality, real-world data for making business decisions, then tagged data is what you need. In this blog post, we will explore the pros and cons of each type of data so that you can make an informed decision for your business.
Synthetic Data limited performance & accuracy
Organizations are now turning to artificial intelligence (AI) and machine learning (ML) to help make their data work harder. But what happens when the data they train their models on is inaccurate or incomplete?
Synthetic data
This is where synthetic data comes in. Synthetic data is generated by algorithms, rather than being collected from real-world sources. This can be used to supplement or replace tagged data, which has a number of advantages.
Limitations
However, there are also some limitations to using synthetic data. One is that it can be difficult to generate data that accurately reflect reality. This can lead to lower performance and accuracy in AI models that are trained on synthetic data.
High cost
Another limitation is that synthetic data can be expensive to generate. This is because it requires significant computing power and resources. As a result, organizations need to carefully consider whether the benefits of using synthetic data justify the costs.
Tagged Data high performance & accuracy
There are many factors to consider when choosing between synthetic data and tagged data:
1- Performance & accurracy
Tagged data is often more accurate than synthetic data because it is collected from real-world sources. This means that it is less likely to contain errors. In addition, tagged data can be more easily verified for accuracy.
2- Speed VS Accurracy
Synthetic data, on the other hand, is generated by algorithms and may not be as accurate as tagged data. However, synthetic data can be generated much faster than tagged data, so it may be preferable in some situations where speed is more important than accuracy.
3- Train & Test
Tagged data is often used to train and test AI models. This is because tagged data can be easily divided into a training set and a test set. Tagged data can also be used to create synthetic data sets. Synthetic data sets are created by using a generative model to generate new data points that are similar to the training data set.
4- Tagged Data Accurracy
Tagged data is often more accurate than synthetic data sets. This is because the tagging process ensures that only relevant information is included in the training data set. Tagged data can also be more easily divided into a training set and a test set.
However, tagged data can be more expensive to obtain than synthetic data sets. In addition, the accuracy of tagged data can vary depending on the quality of the tagging process.
Applications
MedTech
E-Commerce
Agritech
Mobility
Goverments
ConstruTech
FinTech
IOT
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