Synth Studio: A Paradigm Shift for Data Generation, Data Privacy, and Innovation

The demand for high quality data surged across the industrial sectors due to an increase in applications of AI, big data, and advanced analytics. To make sense of data, and develop applications, it is pertinent to use and share the data to build pipelines, train and deploy data solutions. At the same time, growing data breaches and cyber-threats, data privacy concerns and stringent regulations such as HIPAA, GDPR, CCPA and CPA made the organizations be more responsible in managing the data. Therefore, organizations are concerned about sharing data to the external parties, and worried about sharing the sensitive data, which is stifling the organizations to leverage data to develop pipelines, train and deploy AI/ML models.

The current practices to overcome the data quality and diversity challenges are approaches such as augmentation, anonymization and differential privacy to improve the quality and usefulness of available data in different ways. Augmentation creates new data points to supplement an existing dataset in size and improve the accuracy of machine learning models. For example, image data may be rotated to create a new one. Anonymization – a widely used practice in financial and health care sectors – removes or masks personally identifiable information from a dataset to protect privacy while still retaining important properties for analysis. Differential privacy adds random noise to the data so that it becomes difficult to identify individual data point. Each of these approaches have limitations with respect to compromises made in the utility of the data while preserving the privacy.

To overcome the problems due to data sharing as well as the compromises made in preserving the privacy, organizations are turning to synthetic data as a powerful solution. Synthetic data simulates the characteristics and patterns of real-world data without concealing identifiable information about individuals or entities and generates artificial data. In this blog, we highlight the importance of synthetic data in businesses, make comparisons with other possible alternatives to synthetic data and finally discuss some potential use cases to leverage synthetic data.

To build robust AI models, developers need diverse and high-quality labelled datasets. Gartner reported that 85% of AI projects are likely to deliver erroneous outcomes due to bias in data or algorithms. Furthermore, as IBM reported, poor quality data cost businesses a whopping $3.1 trillion per year. This highlights the need for high quality training data that is diverse, representative, and free of biases. Synthetic data addresses these issues by generating large, diverse datasets: free of privacy concerns and biases. In fact, MIT researchers found in a study that synthetic data was as effective as real-world data for training machine learning models in certain cases. A study from the International Association of Privacy Professionals found that 75% of respondents planned to increase the use of synthetic data to address privacy concerns. A recent report by Research And Markets estimated that the market for synthetic biology data is expected to reach $77.5 billion by 2030, driven by the growing demand for high quality training data for machine learning and other applications. These findings suggest that synthetic data is becoming an increasingly important tool for businesses and organizations that rely on data driven decision making.

Synthetic data is gaining popularity as a solution to overcome data challenges in various sectors such as automobile, manufacturing, banking, healthcare, security, surveillance, autonomous vehicles, and retail. For example, in the realm of HR analytics, synthetic data proves to be a valuable tool in addressing privacy and sensitive information concerns. Organizations can use synthetic tabular datasets to train talent management recommendation engines without compromising the confidentiality of employee details. By simulating diverse scenarios and profiles, synthetic data facilitates the development of robust algorithms that can suggest optimal career paths, training programs, and team compositions. Similarly, in sales analytics, businesses can utilize synthetic data to create advanced models for predicting customer churn without exposing actual customer information. This approach allows companies to refine their strategies, improve customer retention efforts, and optimize sales processes, all while maintaining the privacy and security of individual customer data. The versatility of synthetic tabular data holds significant promise across various industries, offering a solid foundation for innovation in analytics and decision-making without putting sensitive information at risk.

The synthetic data market is growing steadily with many open-source tools and techniques. Mphasis’ Synth Studio, a synthetic data generation and enrichment solution leverages state-of-the-art algorithms and proprietary methodologies to build a scalable and secure pipeline to generate technically sound and privacy protected synthetic datasets. The goal of our offering is to apply state-of-the-art practices and proprietary methods to help our clients with their diverse data requirements.

To try out some components of synthetic data solution, you can subscribe to Mphasis' Tabular Synthetic Data Generator, Mphasis' Relational Synthetic Data Generator, or Structured Synthetic Data Evaluator on AWS Marketplace for machine learning. The current listings will help you generate single tabular data, relational tabular data and evaluate your tabular data. To know more about Synth Studio visit our solution page Synth Studio.