We live in an era when data drives virtually everything we do. The power and importance of data are undeniable today. Advanced analytics and artificial intelligence are being used more and more by organizations to improve operational processes, improve the customer experience, or come up with new products or services. Still, for businesses to stay competitive in the market today, they need easy access to data, which isn't always easy to find or even use. Because of concerns about privacy, it is especially hard for business owners to get accurate data.  This is where Synthetic data plays a part in solving this problem by creating a data set that replaces accurate data. The statistical properties and patterns of an existing dataset are sampled and recreated using a model of that dataset. Even though the algorithm creates new data that is based on the original data, the new data set is just as accurate and, most importantly, does not pose any privacy risks.

Synthetic Data: Its Importance

Synthetic data is useful for businesses because it lets users create features that aren't possible with real-world data. Even if you don't have any real data, you can make your own if you know a lot about how data sets are organized. While the best-fit distribution helps to produce synthetic data in the presence of real data, a hybrid approach is used in the presence of a handful of real data. Researchers can learn a lot from synthetic data, especially in the clinical and healthcare fields, where it can be used to test conditions and cases for which there is no real data.

Additionally, there are enough reasons to use synthetic data to train AI. Cost is the primary one. Each year, businesses spend billions of dollars on their acquisition, management, processing, and analysis of real data. With synthetic data, creating new data becomes affordable and fast once the generative model is in place.

Also, there may be rules about how real data can be used because of privacy laws. Synthetic data can imitate the statistical properties of real data without revealing real data. This makes data anonymous, eliminates the chance that data can be traced back, and keeps privacy from being broken. Above all, synthetic data can be made, shared, and thrown away whenever it is needed.

Training systems, too, can benefit from synthetic data. In addition to examining the current system performance, synthetic data can also be used to train new systems on situations that aren't represented by real-world data.

Synthetic Data: Its Business Value

Even though synthetic data is still in its early stages, it's projected to grow dramatically in the coming years because it offers businesses security, speed, and scale when handling data and artificial intelligence. According to Gartner [1] by 2024, synthetic data will account for 60% of the data used in the development of analytics and AI projects.

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Securing Sensitive Data

The main benefit of synthetic data is that it makes it less likely that important data will be leaked.  With data encryption and data anonymization, the original data and the information contained within it can be protected. If the original data is being used, there will always be some risk of it becoming compromised. This isn't possible with synthetic data because it doesn't hide or change the real data. Traditional methods of making data anonymous don't work as well as synthetic data generation because it starts from scratch instead of changing and destroying an existing dataset.

This is clear from the fact that doctors in Nordic countries [2] use synthetic datasets to study diseases and make custom diagnoses because they have the most complete health data in the world. In the same way, Google Waymo's [3] self-driving cars are trained with artificial data, and Toyota Research Institute's [4] dynamic scene understanding is used for self-driving cars.

Quicker Data Accessibility

Another big challenge companies face is getting access to their data quickly enough so they can start generating value from it. Synthetic data gets rid of the privacy and security protocols that can make getting and using data hard and take a long time. Since synthetic data is removed Although there are concerns about privacy and security, businesses can benefit because it takes less time to get to and use data.

By generating synthetic data from original data, companies can overcome this access barrier. Using the new data models, the team can keep updating and modeling the data and get new insights that can help the business do better. Also, businesses can get around the problem of not having enough data to train machine learning models and get solutions up and running faster by making their own data.

Scalability

Companies often have trouble because they don't have enough internal data to see a wide picture. Synthetic data can be helpful for them because it expands the range of data analysis and makes solutions better. With synthetic data sets, businesses can add information from many different sources to their own data to make it more useful. This enables businesses to gain a better understanding of the problem they're trying to solve and provide more accurate answers without compromising any privacy concerns.

Conclusion

Just like every other thing that has its pros and cons, even synthetic data has its limits. Businesses might have a hard time finding skilled people who know a lot about AI and can create synthetic data and test it well. With AI, there is always a chance of bias that cannot be entirely ruled out. But then, businesses can think about changing AI models to create a more fair and representative set of fake data. There is also a chance that people won't accept the synthetic data because they don't trust the quality of the data or the accuracy of the results.

Yet, there are many possible applications for synthetic data across many industries. Let’s understand that this field is relatively new. Synthetic data is created, applied, and functional in fewer instances. With data becoming more complex and more closely guarded, the creation and application of synthetic data in real-world scenarios will only grow in the time to come.

References:
[1] Gartner Prediction - https://gtnr.it/3hFI1gB
[2] Synthetic health data can ensure better disease prevention and treatment - https://bit.ly/3pDANy5
[3] Waymo is using AI to simulate autonomous vehicle camera data -  https://bit.ly/3HEeMFn
[4] Scaling up Synthetic Supervision for Computer Vision - https://bit.ly/34esb9x