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.


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.



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01 Map customer requirements & needs

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02 Define Scope of work (SOW) & execution plan (Gantt)

We outline the specific deliverables and milestones that need to be achieved. An execution plan (Gantt) is also created, outlining the timeline and resources required to complete the project.

03 Carefully select the right package of services & technologies to maximize performance

The selection is made based on the specific requirements of the project, as well as the performance goals that need to be achieved.

04 Ongoing review & monitoring of SOW & Gantt

The final step involves ongoing monitoring and review of the SOW and Gantt to ensure that the project is on track and that all milestones are being met. This step also includes regular check-ins and feedback sessions to ensure that the customer's needs are being met and that the AI solution is achieving the desired results.


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