Why Hazy

What is synthetic data?


More accessible

Synthetic data does not contain any real data points so can be shared freely. Say goodbye to lengthy governance processes associated with real data.


Lower risk

Synthetic data is a way to protect customer data without compromising on data quality and performance. It is the go-to tool for data privacy and compliance.    


Highly versatile

Legacy anonymisation techniques destroy the utility of your real data while being prone to attacks. Robust synthetic data software enables users to fine-tune the privacy, utility and fidelity of their synthetic datasets to solve a broad range of use cases.

Synthetic data is:

An artificial version of your real data

Synthetic data is artificially generated data that imitates real-world data patterns, characteristics, and structures. It uses generative AI techniques to mimic the statistical properties and distributions of the original data without containing any real information from individuals or sources.

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Synthetic data is:

A drop-in replacement for production data

Generative models are trained with real data as an input. Unlike mock data, the resulting synthetic data is highly representative and can be fine-tuned for multiple use cases such as testing, analytics and data sharing. Organisations are de-risking their data workflows with safe replicas of their production data.

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Synthetic data is:

Smarter and safer than traditional anonymisation techniques

Hazy used advanced privacy techniques to ensure there is no 1:1 mapping to the original data, reducing the risk of re-identification. Synthetic data generated with Hazy does not contain any real information or PII which means it can be used freely, ensuring data compliance.

Anonymisation vs Pseudonymisation vs Synthetic Data

Who is synthetic data for?


Developers & Engineers

For testing applications, APIs, and software products. Identify and rectify any issues without exposing real user data.


Data Scientists & Analysts

For model development, testing, and validation. Improve algorithm accuracy, understand data patterns, and refine predictive models.


BI & Analytics teams

For trend analysis, visualisation, and business insights. Supports strategic decision-making by helping understand data patterns and enhancing BI tools.


Innovation & Research teams

To develop proof of concepts, simulate experiments, and conduct research in various fields, fostering innovation and advancements.


Partnerships teams

Organisations collaborating with external organisations or forming partnerships can share synthetic data and insights without disclosing proprietary or sensitive information.

Example synthetic data

Synthetic data is an industry-agnostic solution spanning fields such as finance, healthcare, telecommunications and insurance. Here are some examples of common synthetic datasets: 


Customer data

Attributes: Name, age, gender, location, purchasing history, preferences.


Financial data

Attributes: Account number, transaction history, balances, income, expenditures.


Network data

Attributes: Network nodes, connection details, traffic patterns, network events.


Healthcare data

Attributes: Patient ID, medical history, diagnoses, treatments.


Geospatial data

Attributes: Latitude, longitude, altitude, geographic features.


Commercial data

Attributes: Product ID, category, price, customer reviews, sales data.

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