Data Buddy - Synthetic Test Data Generator

Problem Statement:

Data-driven innovation faces a roadblock: balancing privacy with data utility. Anonymization compromises data quality, while raw data exposes sensitive information. This limits analysis and model training, hindering organizations' ability to fully leverage their data. Additionally, traditional methods for acquiring test data, such as purchasing datasets or manually creating them, can be expensive, time-consuming, and limited in scope.

With our team of AI experts and AI-driven efficiencies, leveraging Microsoft's Azure GAN capabilities, we accelerate time-to-market, enhance innovation, and deliver high-quality solutions for our clients. Data Buddy: Synthetic Test Data Generator is an offering in partnership with Microsoft Azure and Powered by GANs (Generative adversarial networks).

5-day-Assessment: Our assessment will help pinpoint specific issues hindering data initiatives, understand how synthetic data can address these challenges, discover potential applications within an organization’s industry and domain, define the specific synthetic data needed to achieve desired goals, see a brief demo on how our Azure based GenAI-powered tool can generate high-quality synthetic data tailored to pointed needs, understand the potential value of synthetic data in terms of improved efficiency, cost savings, and enhanced decision-making. Let's work together to unlock the power of synthetic data and drive innovation.

Synthetic test data generator is a valuable tool for a wide range of users, including data scientists, machine learning engineers, software developers, and researchers. By replicating real-world data patterns, our tool provides diverse and high-quality datasets that can address various challenges. These datasets can be used to train and test models, simulate real-world scenarios, conduct research, and handle complex datasets while preserving relationships and dependencies.

By leveraging the Synthetic Test Data Generator, organizations can experience significant cost reductions in acquiring and labelling data ranging around 60% and model training tine acceleration of 2x to 5x.

One of the key benefits of synthetic data is its ability to mimic real data without exposing sensitive information. This can be particularly useful for businesses that need to comply with data privacy regulations, such as GDPR and CCPA. Additionally, synthetic data can be used to reduce bias in data, which can improve the accuracy and fairness of machine learning models.

For example, in our experiments we found that using synthetic data to augment real-world datasets can reduce bias in machine learning models and improve model performance by up to 10%. This can lead to significant benefits for businesses, such as improved decision-making and reduced discrimination.

Synthetic data generation also offers significant cost and time efficiencies compared to traditional data acquisition methods. Real-world data collection and labeling is expensive, time-consuming and requires expert resources. Synthetic data, on the other hand, eliminates the need for expensive data collection processes, reduces storage requirements, and allows for efficient model testing. This translates to substantial cost and time savings throughout the entire data lifecycle. Our tool enables organizations to achieve potential benefits of 10% to 20% in delivering value faster to their customers.

Synthetic Test Data Generator cuts across multiple industries and can be repurposed across environments. It empowers industries like healthcare (e.g., training AI models for medical diagnosis), finance (e.g., testing fraud detection algorithms), automotive (e.g., simulating driving scenarios), retail (e.g., improving demand forecasting), entertainment (e.g., creating new music genres), and cybersecurity (e.g., training threat detection systems).

In summary, Data Buddy's synthetic test data generator is unique in the market as it offers efficient testing and reusability. Our tool uses Microsoft Azure based GANs efficiently and covers common and edge cases for thorough testing, while our reusable model ensures cost-effective and standardized testing across use cases.