Unraveling the Concept of 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 concern 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 synthetic 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.

What is Synthetic Data?

Synthetic Data refers to generating data through computer programs, for creating datasets that can assist the construction of Artificial Intelligence, and deep learning model, as well as aids in software testing. It is also required for training the data on machine learning algorithms.

Synthetic data is generated with various techniques, which includes:

• Data generation based on Distribution
• Fitting Real Data to a known distribution using the Monte Carlo method
• With the help of deep learning models such as Variational Autoencoder (VAE), and Generative Adversarial Network (GAN)

All these techniques reflect the statistical property of synthetic data.

Applications of Synthetic Data

Synthetic data is being utilized in the following sector:

1. 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. 
2. 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.
3. DevOps-  In DevOps software testing is one of the crucial steps before achieving the desired product. However, generating the real-time data can be time-consuming thus affecting the flexibility and agility during development. To counter this problem, data is generated using synthetic data toolkit, eliminates the need the waiting period for the generation of data and retains the agility, efficiency and accuracy during the development. For this reason, while software testing, this is also known as the testing data.
4. 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.
5. Security-  Like mentioned earlier, the paramount application of synthetic data is in retaining the privacy of an organization. By training synthetic data for video surveillance, it can act as an image recognition model, and in identifying the deep fakes by testing the facial recognition systems.

Challenges of Synthetic Data

Though Synthetic data has been recognized as an option that can be used instead of real-time data, it does have some limitations.

1. Inherent Biases- The quality of any model depends upon the quality of the data source and input model. As synthetic data is generated with the computational process, it is prone to reflect biases in the designed model.
2. Time-Consuming- Unlike real-time data, which is readily available, the synthetic data takes time and effort to be created.
3. Challenges in acceptance–   It is an emerging concept, and may find difficulty in acceptance amongst those organizations which do not know about its benefits.