Using Data Integration to build digital twins without compromising data privacy

SPEAKERS

Alexander Alten Chief Executive Officer & founder, Databloom AI

ABOUT THE SESSION

Alexander Alten, CEO and co-founder of Databloom AI, is pioneering a future where organizations can harness the full power of data without compromising privacy. At the heart of this vision is Blossom Sky, Databloom’s AI-powered platform that enables federated data processing across distributed systems—streamlining integration while preserving data sovereignty. With a strong focus on eliminating the traditional complexities of data management, Alexander is leading the charge toward faster, more secure digital transformation. He will share insights into how intelligent data integration can be used to build accurate and responsive digital twins, empowering enterprises to simulate real-world systems while ensuring compliance with data privacy regulations.

He will share his views on how federated learning is redefining digital twin development by unlocking collaboration without compromising data ownership. Alexander Alten, CEO and Co-founder of Databloom AI, will explore how organizations can harness insights from distributed data sources while keeping sensitive information secure and decentralized. With Blossom Sky, Databloom’s AI-powered integration platform, enterprises can process data across diverse engines—eliminating silos and reducing the need for costly, centralized data infrastructure. He will emphasize how this approach supports privacy regulations, reduces legal risks, and enhances cybersecurity posture. Particularly in digital twin applications, this strategy enables the creation of smarter, more accurate models that mirror real-world assets and systems. He will also highlight how federated learning cuts storage and compute expenses while enabling seamless collaboration between departments or partners. By combining performance, efficiency, and privacy, Alexander demonstrates how federated data processing is a practical path to accelerating digital transformation across industries.

Key Topics-

1. Federated learning allows organizations to combine data from different sources, improving the accuracy of models. This is especially important for digital twin models, which can be used to predict the performance of real-world assets
 

2. Organizations can keep control over their own data while still being able to collaborate on machine learning projects, which will help reduce the risk of data breaches and other security issues.
 

3. Cost-effective: Businesses can reduce the costs associated with data storage and processing by combining information from different sources to create more accurate models.

For more on this conference or to access the session, reach out to us at [email protected]. 

This content is copyrighted by PTN Events Private Limited. Any recording and posting of this content is strictly prohibited