For Cloud Analytics
Run analytics workloads in the cloud without exposing your data.
- Harness cloud compute
- Use cloud ML pipelines
Enable cross boundary data analytics
Hazy’s synthetic data generation lets you create business insight across company, legal and compliance boundaries — without moving or exposing your data.
Synthetic data
A new paradigm in information transfer
Hazy uses advanced generative models to distill the signal in your data before condensing it back into safe synthetic data.
1. Train
Hazy uses generative models to understand and extract the signal in your data.
2. Distill
These models can then be moved safely across company, legal and compliance boundaries.
3. Condense
Before then being used to generate statistically equivalent synthetic data.
4. Use
That's drop-in compatible with your existing analytics code and workflows.
Use cases
For cross boundary data analytics
This allows you to gain and share insights across organisational, legal and compliance boundaries, without moving or exposing your data.
For External Analytics
Access specialist external data analysts and externally hosted tools and services.
- Access the best specialists
- Use external tools and services
For Data Innovation
Evaluate algorithms, projects and vendors without data governance headaches.
- Increase speed of innovation
- Without risking live data
For Data Monetisation
Sell insights and leverage the value in your data without exposing sensitive information.
- Safely monetise your data
- Create new revenue streams
For Data sourcing
Access, aggregate and integrate synthetic data from internal and external sources.
- Access sensitive data
- Drop-in compatible
Products
In enterprise financial services
Hazy focuses purely on enabling enterprise analytics and specialises in the financial services data domain.
Enterprise class capability
An enterprise class software platform and a track record of successfully enabling real world enterprise data analytics.
Financial services data
Advanced generative models that can preserve the relationships in transactional time-series data and real-world customer CIS models.
Read about how we reduced time, cost and risk for Nationwide Building Society by enabling them to generate highly representative synthetic data for transactions.
Formal privacy guarantees
Formal differential privacy guarantees that ensure individual-level privacy and can be configured to optimise fundamental privacy vs utility trade-offs.
Case study
"Often one of our biggest challenges as we develop innovative analytics solutions is accessing customer data."
Global Centre for Innovation
Hazy helped the Accenture Dock team deliver a major data analytics project for a large financial services customer. Accenture were aiming to provide an advanced analytics capability. However, their ability to do so was blocked by data access constraints.
Hazy generated a synthetic version of their customer’s data that preserved the core signal required for the analytics project. This unblocked Accenture’s ability to analyse the data and deliver key business insight to their financial services customer.
“Hazy has the potential to transform the way everyone interacts with Microsoft’s cloud technology and unlock huge value for our customers.”
“By 2022, 40% of data used to train AI models will be synthetically generated.”
“At Nationwide, we’re using Hazy to unlock our data for testing and data science in a way that signicantly reduces data leakage risk.”