Synthetic Data: Unraveling Artificial Intelligence for Enterprises

By Davide Delle Cave, S2E Search & Observability Business Line Manager

What is synthetic data and how is it generated?

Synthetic data is information that has been artificially produced rather than derived from real events. They are created algorithmically and are used as a proxy for test datasets and production or operational data, to validate mathematical models, and to train machine learning (ML) models. While collecting high-quality data from the real world is difficult, expensive, and time-consuming, synthetic data technology allows users to quickly, easily, and digitally generate data in the desired quantity, customized to their specific needs.

It is important to note that the process of synthesizing data varies depending on the tools, algo-rithms, and the particular use case. The following are three common techniques for creating syn-thetic data:

• Drawing from distributions. Randomly selecting numbers from distributions is a prevalent method of generating syn-thetic data. While this approach may not capture the intricacies of real data, it can produce distributions closely resembling real-world data.

• Agent-based modeling. This simulation technique involves creating unique agents that interact with each other, yielding a synthetic model mirroring reality. Particularly valuable when analyzing interac-tions within complex systems involving diverse agents like cell phones, individuals, or software programs.

• Generated models. These algorithms generate synthetic data that has the same statistical characteristics as the real data. Based on the training data, generative models are able to detect statistical pat-terns and relationships, thereby generating new synthetic data similar to the originals. Ex-amples include Generative Adversarial Networks (GAN) and Variational Autoencoder (VAE) models.

SOURCE: https://www.techtarget.com/searchcio/definition/synthetic-data 

S2E and Clearbox

Together with Clearbox AI, a leader in the synthetic data field, we provide innovative, secure, and often indispensable solutions for data analysis and management. In regulatory contexts (GDPR) or the new AI Act, synthetic data allows individuals and organizations to preserve their privacy and respect by extracting data that have no correlation with the original ones but are still sufficient for direct analysis, to study phenomena to poor events (such as fraud), to train new models, or as part of testing.

In particular, in the context of test automation and continuous testing, not only are application sys-tems and execution environments generated (Infrastructure as a Code), but also the databases are loaded with synthetic data (Data as a Code), enabling Quality Assurance to perform tests rapidly and efficiently with the correct application environment and the appropriate data whenever neces-sary. Synthetic data also has the advantage of reproducibility, as the entire synthetic database can be generated from metadata; as a result, it is possible to regenerate the database when needed and delete it after use, saving space and money. Our partnership with Clearbox AI provides organiza-tions with comprehensive support in the rapidly expanding and adopting field of generative artifi-cial intelligence. Our experience accelerates the rapid grounding of the platform, i.e. in its effec-tive management and in the ability to offer consultancy both on data architecture and on their op-erational management. As a result, companies can enhance their initiatives with Synthetic Data, thereby maximizing their effectiveness and efficiency.

Clearbox AI Enterprise Solution

It consists of a proprietary, agnostic solution that helps companies launch AI and Analytics projects by generating high-quality synthetic data that can be used for predictive analytics, process improvement, or growth forecasting. With synthetic data, you can overcome problems of data imbalance and scarcity, create them from scratch or from structured data sources, such as those found in a relational database or a data warehouse, and accelerate the development of models. Furthermore, synthetic generation is a GDPR-compliant anonymization technique that preserves the privacy and usefulness of the original data, thereby reducing the risks associated with sharing, utilizing, and retaining it. Fully dockerized solution that can be installed on-premises or in the cloud.

Applicability 

Healthcare and finance are two critical sectors to which synthetic data finds application, as the precision, confidentiality, and value of the data are crucial to the success of new products. In the context of testing, obsolete masking techniques are overcome. In machine learning, they are a popular choice for training models when there is a lack of data. As synthetic data can be progressively improved over time with new real data, their value increases at a relatively low cost.

Synthetic data has the following advantages:

1. Compliance with data privacy and security laws.
2. They enable the study of rare or new phenomena that have little data.
3. Democratization and availability of data within the organization, overcoming conservation and re-producibility limitations.
4. Improve data quality and reduce risk associated with data-driven monetization initiatives.
5. The transparency of the synthetic data allows you to develop models based on Explainable AI, use-ful in applications that require documentation and understanding.
6. Data generation from algorithms: “Data as a Code” goes hand in hand with “Infrastructure as a Code” practices. In this way, the entire ICT asset is generated programmatically, thereby reducing setup costs.

Conclusions

Among the most promising technologies on the market, synthetic data is one of the main resources available to companies for improving artificial intelligence and data management processes.