Highlights:

  • The ability to create data that fulfills the real-time system requirements is the most helpful method for testing and development.
  • The use of synthetic data considerably lowers the cost of generating training data. Besides, it also detects privacy concerns of sensitive data retrieved from the real world.

The successful generation of ML and AI models results from large volumes of quality databases. However, data retrieval can become tedious and challenging due to several associated parameters.

Though multiple business problems can be mitigated by AI and ML tools, it demands access to sensitive customer data, often including personally identifiable information (PII). Intervening with such sensitive data results in privacy concerns and data breaches.

Moreover, accessing specific datasets is found to be very rare and expensive. For instance, the data about random real-world events or fraudulent activities. Hence, businesses opt for a data-oriented approach to AI and ML development that involves synthetic data to handle problems.

Generating synthetic data is less expensive than data collection for developing AI and ML models. Estimations reveal that the maximum chunk of data to create analytics projects and AI will be synthetically extracted in the coming years.

Let’s understand the basics, its advantages, and more details.

Defining Synthetic Data

The term itself is self-explanatory, indicating that the data is created artificially instead of retrieved from actual events. Algorithmically generated synthetic data is a type of augmentation that serves multiple purposes, such as model validation, test data for new tools and products, AI model training, and others.

Synthetic data is significant as it can meet specific conditions that real (existing) data fail to serve. It comes into the picture when privacy requisites restrict data availability, no or fewer data are available to the testers, and real-time training data becomes expensive to generate. The widespread access to storage space and computational power led to the increased use of synthetic data.

Advantages of Synthetic Data

The ability to create data that fulfills the real-time system requirements is the most helpful method for testing and development. Synthetic data is advantageous over real data in the following ways:

  • Coping with real data usage limitations: The privacy rules and regulations constrict real data usage. On the contrary, synthetic data can work over all crucial statistical properties without exposing real data, thus overcoming critical issues.
  • Data can assess and simulate newly executed tasks: When real data cannot affect the new models, synthetic data is perceived as the only solution. It can manage the unencountered conditions and provide feasible options to the system.
  • Capable of handling general statistical problems: Several properties and attributes of synthetic data, such as skipping patterns, non-responsiveness to items, and other system constraints, make the data absolutely immune to standard statistical discrepancies.
  • Integration with variables: Synthetic data can retain integration between variables and not only focus on specific statistics.

Such features indicate that synthetic data will continue to increase with data systems’ growing complexity and structure.

Various industry verticals that can leverage synthetic data for optimal benefits include healthcare, finance, robotics and automotive, security, manufacturing, IT, and social media.

Besides, machine learning, marketing, HR, agile development, and DevOps are the business domains that can primarily benefit from synthetic data.

It enables the development of innovative solutions and products when the data crucial for this development is not sufficiently available.

Application & Performance

Synthetic data serves a wide range of applications, and its quality can be measured by its effectiveness in utility. ML has been one of the most general use cases of synthetic data. In a particular experiment, data scientists were split into two groups, one working on real data and the other with synthetic. It was observed that the data scientists’ group using synthetic data produced better results for a considerably longer period than the other group.

Synthetic data is found to be more beneficial over privacy-enhancing technologies such as anonymization and data masking.

Synthetic Data Generation

The best suitable method for creation depends on the kind of synthetic data anticipated. With distinct merits and demerits, three broad categories can be assessed to choose from:

1. Fully Synthetic

No trace of original data is involved here. It becomes nearly non-feasible to re-identify single units, yet all variables are entirely available.

2. Partially Synthetic

The segment of just sensitive real data is replaced with synthetic data. The data generation process heavily relies on the imputation model, leading to less dependence on the model. However, some disclosure is allowed due to the true values within the dataset.

3. Hybrid Synthetic

The data is a function of both synthetic and real data. While ensuring the integration between dataset variables, the underlying original data distribution is evaluated to create data points. The closest synthetic data record is associated with generating hybrid data for each real data record.

General Strategies for Synthetic Data Creation

  • Numbers from the Distribution

This method involves observing real statistical distributions and recreating counterfeit data. Besides, it includes creating generative models.

  • Agent-oriented Modeling

A synthesized model elaborates an observed behavior and recreates random data with the help of the original model. The method analyzes the effect of integrations between agents on the entire system.

Synthetic Data Augmenting AI

In the domain of AI and ML, synthetic data has been growing as the prime area of RnD. Various disciplines in which the synthetic data has been serving include machine vision, autonomous (self-driving) vehicles, augmented reality, healthcare data, data synthesis tools, financial discrepancies detection, geospatial imaging, and others.

AI, deep learning, and ML applications have been making utmost use of synthetic data, enhancing its traction. Eventually, the demand for ML engineers, AI architects, DevOps experts, and other technology professionals is increasing immensely.

Autonomous Vehicle (AV) domain deploys ML expertise and huge investment for the commercial application of AI. Accessing real-world driving data for every possible scenario is found challenging. Hence, AV companies build simulation engines to generate the required data volumes and utilize AI systems analytically. The simulations can automatically create hundreds of feasible driving scenarios.

Most industry verticals immediately realized that the synthetic data capabilities could also be leveraged for multiple computer vision applications. Manufacturing, robotics, geospatial imagery, security, and many other areas deploy computer vision systems by generating AI models that demand large volumes of databases. With the help of synthetic data methodologies, companies can access training data quickly with less expense than real data retrieval. Eliminating the need for manual data labeling has been the prime advantage of synthetic data.

With synthetic data, the use cases of AI and ML and be explored to the maximum possible extent. The required data can be digitally generated in the expected volume and customized to meet the specifications. Synthetic datasets are developed with the help of 3D models of humans and objects to create motion clips.

The use of synthetic data considerably lowers the cost of generating training data. Besides, it also detects privacy concerns of sensitive data retrieved from the real world.

Synthetic Data Challenges

Despite serving numerous use cases and enhancing AI applications, synthetic data face some of the challenges mentioned below:

  • Although synthetic data resembles real-world data, it cannot be considered a precise replica. Hence, it sometimes does not include certain outliers often found in the original data.
  • The quality of synthetic data largely relies on the quality of the data generation model and input data. Synthetic data might exhibit conflicts with source data.
  • Although simpler to generate than real-world data, synthetic data is not absolutely free.
  • In some complex datasets, the synthetic data is compared with human-annotated data to ensure output accuracy.

Bottomline

The role of synthetic data in enhancing AI and ML applications is expanding rapidly. It is evident that ML algorithms are to be trained with large data volumes, which becomes challenging to generate or extract without synthetic data. Moreover, synthetic data plays a crucial role in generating algorithms for image analysis and other tasks, including the AI baseline.

In the ever-expanding realm of artificial intelligence and machine learning, synthetic data stands poised as the creative catalyst that fuels innovation, propelling us toward a future where the boundaries of possibility are redefined and the impossible becomes attainable.