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Synthetic Data: Fueling AI Progress While Navigating Privacy and Regulatory Landscapes

Synthetic data can be defined as data generated by computers in a way that simulates real data in specific ways. However, synthetic data is not actual; it can simulate data produced by applying algorithms designed to mimic the trends and relationships seen in actual data.

It is essential, mainly when real data cannot be obtained threatening the privacy of the people and the data is obtained from or is challenging to obtain.

Especially in the case of artificial intelligence training, it is essential as a mechanism to reduce the system’s exposure risk to the real and sensitive or confidential data it is supposed to use. With new technology or research that may require that one generates data before a license is issued or if there is a need for the generation of data in the absence of enough data sensitivity, confidential or those data that are sensitive to the people that gave it are never used.

ket research from primary and reliable sources:

  • Gartner has forecasted that synthetic data is rapidly growing, and they have predicted that 60% of AI data, from the 1% in 2021, will be synthetic. It will have a primary destination for simulating future scenarios and derisking AI.


  • From Fortune Business Insights, the global synthetic data generation market size is projected to grow from $351.2 million in 2023 to $2,339.8 million by 2030 at a CAGR of 31.1%.

  • Grand View Research has forecasted a 35% CAGR for North America from 2023-2030.

It looks like data creation is experiencing a complete revolution.

Why? So far, data used by AI algorithms has been collected from the real world; it has created questioning and is included in laws and regulations about privacy, other sensitive information, copyrights, and more.


The advantages of synthetic data are much more, especially in terms of cost and efficiency. Synthetic data generation is much more inexpensive and faster than real data collection. Therefore, tends to make it possible to increase the efficiency of testing and training, including artificial intelligence. Synthetic data also dramatically reduces a number of risks, such as data privacy, since it does not involve the use of real personal information. It can also simulate rare events or, in general, be used to fill cases in the data that are hard to capture with real information. It is evident their value  n complex problem-solving and innovation,


The main disadvantage of synthetic data is that it is not a reflection of real life in most cases, but it could be created based on wrong assumptions or incomplete information. Overreliance on synthetic data may result in missed opportunities related to new real-world outputs.

Some research calls for an update of current regulations and laws, which are currently based on #collected data.

Changing governance, auditing activities, privacy fundamentals, and ethics are also challenging.

In conclusion, the technical benefits and challenges should consider the risks for privacy regulations and laws and those related to the reliability of the AI processes and outcomes with the use of data in business continuity.


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