The generation of synthetic data is not new and the AI business world has an abundance of dependency on synthetic data

Every day, a plethora of data is collected by organizations, to generate datasets that would help in running algorithms. And this data is compiled from an assortment of unidentifiable sources. Data scientists face the challenge of collecting, segregating, and handling the data, which delays the process of generating accurate datasets in a given time frame. To rectify this problem, some organizations are procuring synthetic data, where data is generated through the computing processes of the systems and constructs datasets faster, as compared to the datasets created with real-world data. Unlike real-world data, synthetic data is invented and imagined.

The generation of synthetic data is not new, but as the demand for data-driven operations has increased, privacy infringement is one of the major concerns amongst organizations. With the heavy dependence of organizations on data, over the past few years, the incidents of cyber-attacks and malware have increased, where organizations were rendered to face heavy losses. To mitigate such incidents, the organizations are now looking forward to generating data without affecting the privacy of the organization.

Product testing is another area where organizations are facing challenges as either the required data doesn't exist or remains unavailable. By procuring data from the computing programs, a model can be created that will help in product testing.

Synthetic Data Over Real Data 

• Real data may have usage constraints due to privacy rules or other regulations. Data can replicate all-important statistical properties of real data without exposing real data, thereby eliminating the issue.
• Immunity to some common statistical problems. These can include item nonresponse, skip patterns, and other logical constraints.
• Synthetic data aims to preserve the multivariate relationships between variables instead of specific statistics alone.

Application of Synthetic data in the World of Artificial Intelligence 

• Self-Driving cars–  By integrating the synthetic data in machine learning algorithms, large datasets are generated, which are then applied for stimulating self-driving cars.
• Security–  Like mentioned earlier, the paramount application of synthetic data is in retaining the privacy of an organization. By training data for video surveillance, it can act as an image recognition model, and in identifying the deep fakes by testing the facial recognition systems.
• Marketing–  With the help of synthetic data, marketing units can improve the marketing spend, by utilizing the invented and imagined data as a property to assess the detailed and individual marketing stimulations. As marketing stimulation often involves the customer's consent, this is not possible with the real-time data. 
• Research–  Synthetic data is often perceived to be an ideal option during research work and clinical trials, as it assists in building preliminary models of research by aiding to understand the specific statistical properties and tuning parameters of related algorithms.
• DevOps–  In DevOps software testing is one of the crucial steps before achieving the desired product. However, generating real-time data can be time-consuming thus affecting the flexibility and agility during development. To counter this problem, data is generated using a synthetic data toolkit, eliminates the need for the waiting period for the generation of data, and retains the agility, efficiency, and accuracy during the development. For this reason, software testing is also known as testing data.

Importance of Synthetic Data 

AI business world has an abundance of dependency on synthetic data

• In the medical and healthcare sector, synthetic data is used for testing certain conditions and cases for which real data does not exist.
• ML-based Uber and Google's self-driving cars are trained with the use of synthetic data.
• In the financial sector, fraud detection and protection are very critical. New fraudulent cases can be examined with the help of synthetic data.
• Synthetic data enables data professionals to access the use of centrally recorded data while still maintaining the confidentiality of the data. Data comes with the power to replicate the important features of real data without exposing the true sense of it, thereby keeping privacy intact.
• In the research department, data helps you develop and deliver innovative products for which necessary data otherwise might not be available.