Use cases

Gain and share insight from cross boundary data analytics.

Share signal, patterns and trends across company, legal and compliance boundaries — without exposing your data.

Moving across boundaries
Pre-defined data set

Pre-defined data set

A defined subset of structured relational data, such as customer or transactional data.

Moving across boundaries

Moving across boundaries

Is being accessed or exchanged across companies, departments, systems or environments.

For an analytic use case

For an analytic use case

So that the patterns, trends and relationships in the data can be analysed in order to gain business insights

Hazy synthetic data for
Cloud Analytics

Problem

  • Many analytics services and providers are in the cloud
  • Data security concerns block access to business insight

Solution

  • Train synthetic data generators on-premise
  • Move just the safe synthetic data generator objects to the cloud
  • Harness cloud resources and services

Benefits

  • Harness cloud compute
  • Use cloud ML pipelines
Cloud Analytics

Hazy synthetic data for
External Analytics

Problem

  • The best analytics people and services are outside the building
  • Accessing their services means getting or granting data access

Solution

  • Use synthetic data technology to distill the signal in your data
  • Give external analytics access to the synthetic data
  • Allows data to be safely analysed externally

Benefits

  • Access the best specialists
  • Use external tools and services
External Analytics

Hazy synthetic data for
Data Innovation

Problem

  • Ideas and vendors often need to be tested on real data
  • Leads to delays, which slows down pace of innovation

Solution

  • Provision sandpits and labs with safe synthetic data
  • Allows for fast prototyping and validation of ideas
  • Prove vendor capabilities before resourcing live data

Benefits

  • Increase speed of innovation
  • Without risking live data
Data Innovation

Hazy synthetic data for
Data Monetisation

Problem

  • Companies can often make and gain huge value by analysing data
  • Data monetisation is often held back by data access and governance concerns

Solution

  • Distill out the generalisable signal into safe synthetic data
  • Sell access to the synthetic data
  • This allows insight to be shared without exposing individuals or real data

Benefits

  • Safely monetise your data
  • Create new revenue streams
Data Monetisation

Hazy synthetic data for
Data sourcing

Problem

  • Data often needs to be augmented from other sources
  • Hard to source and aggregate data from different silos

Solution

  • Train synthetic data generators at the edge, in each silo
  • Sync generators and analyse aggregated synthetic data; with
  • Conditional training and distributed differential privacy

Benefits

  • Access sensitive data
  • Drop-in compatible
Data sourcing
Case study
"We work with Hazy to unlock data for innovation in a way that puts our members and their data privacy first."
Alex Bannister

Alex Bannister

Director of Strategic Partnerships

As the Director of Strategic Partnerships at Nationwide, it’s Alex’s responsibility to bring on and speed up innovation across the building society. This includes evaluating and selecting the best external vendors.

In data analytics, for new approaches and vendor capabilities to be evaluated effectively, they need to be tested on realistic data. This often leads to data access constraints slowing down innovation and the pace of change.

Hazy worked with Alex’s team generate realistic synthetic transactional data that preserved the temporary and causal relationships needed to evaluate the capabilities of external vendors for an advanced data analytics use case. This allowed Nationwide to speed up and run a more realistic vendor evaluation process, without needing to go through a full compliance process.

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Next step

Contact sales

Get in touch to find out more about how Hazy can help enable cross boundary data analytics at your organisation.

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