Synthetic data drives privacy, governance and innovation

It’s harder than ever to remain compliant and to stay competitive.

Corporate governance is a prerequisite to getting any work done in regulated industries like banking, healthcare, even project management. Governance comes down to the four Ps:

• People
• Purpose
• Process
• Performance

We’d add a fifth P in there — Privacy. After all, data right now is worth more than oil. At Hazy, we believe privacy has to be baked into your cross-organisational planning and design.

But there’s no doubt our organisations are only growing in complexity of distributed people, distributed tooling, and distributed interactions. Industries like finance already have more stakeholders than most. And as open APIs become the modus operandi, you are opening your complex systems up to a variety of outside influences. This is right alongside the increasing complexity of the regulations themselves.

Duplication of work and data has become an inescapable consequence. So has delayed time to market while data is chained behind departmental silos. You miss out on strategic partnerships because you just can’t share your data with them fast enough.

Most importantly, improper data governance leaves a greater risk for data leaks.

In comes the opportunity of synthetic data. Synthetic data enables the highest level of governance and privacy, without the typical friction when a company is bogged down by regulations.

Synthetic data lets you comply and compete.

What is synthetic data? How is synthetic data made?

An engineer takes your raw customer data and funnels it through a synthetic data generator like Hazy. Hazy’s technology will then automatically select the optimal machine learning algorithm to train the generator.

During the training process, Hazy’s algorithms begin listening to your raw data. They identify all the statistical properties of the source data, like distributions and correlations, and they train the generator. With this knowledge, the Hazy generator creates a totally new, totally synthetic data set that still retains the same aggregate insights and user habits. This new organised, coherent data is statistically representative and behaves the same way as the original data. But it is completely artificial with zero sensitive information.

This new synthetic data retains the accuracy and consistency of the original data without being able to trace it back to a real customer and their private information. This gold standard level of privacy is called differential privacy.

Once Hazy synthetic data is created you have fully eliminated the privacy risk. You’ve made sure you’re fully compliant. Now you can allow strategic partners in and outside your company to build on and innovate with your data.

Synthetic data allows safe access to the cloud and faster third-party partnerships

There’s no doubt that the organisations that are succeeding in these trying times are those that can rapidly scale via the hybrid or public cloud. Well-regulated industries like banking and healthcare have been reticent to go all-in with the cloud. We get it. As soon as your data leaves your company campus and your servers, you’re losing control.

Synthetic data allows for a rapid, cross-organisational migration to the cloud without any of the added risks.

Instead of pseudo-anonymised data that can still lead to re-identification or redacted data that loses most of its utility, with Hazy synthetic data generation, your data never leaves your servers. You train on your real datasets on-prem. Even behind the walls of your separate departmental silos. Then you can release completely artificial data into the cloud.

Since there’s no personal information in it, you can now share synthetic data across silos within your organisation — allowing for cross-organisational strategy, insights and analytics like never before. You can also use it to test the potential of third-party integration partners and tooling on realistic — not real — data.

Synthetic data allows you to work faster with less risk. It lets you compete and become an industry disruptor once more.

Important synthetic data use cases

You can use synthetic data in the exact same way you would want to use the original data, but often can’t because it’s locked behind privacy and governance. Synthetic data safely unlocks it.

Synthetic data can be used as a replacement for many tasks including enabling a data science team with faster data to work on in developing their own models; innovation teams to share otherwise sensitive data for development or testing new capabilities; and enabling a more efficient migration of applications to cloud environments when you have sensitive data involved.

The Hazy cutting-edge AI algorithms allow organisations to gain access to insights across large multi-databases.

Synthetic data drives speed to innovation. Whether you are trying to partner across silos or across industries with third-party integrators, unlock data utility fast. No need to wait months to get authorisation to give an internal or external party access to raw data. Test any potential collaboration right away with high-quality, no risk synthetic data.

Data scientists, deep learning engineers, and testing engineers are constantly frustrated that they cannot do their jobs effectively. Innovation enclaves within larger organisations are also exasperated having to often wait six to 12 months to get potential partners on-boarded. It is a long, arduous process to provision data from different departments in order to work with that data. And often by the time they get it, it’s out of date and useless. Synthetic data allows teams to rapidly unlock access to the useful information without any identifying information.

Synthetic data has many applications across organisations. This can be allowing internal or outsourced QA and testers to properly test apps and services on realistic data before they go live. To unlock cross-organisational analytics. To migrate to the cloud without migrating any of your private data along with it. And smart synthetic data allows you to provision the value of sensitive data to both internal data science teams and potential collaboration partners.

Popular synthetic data use cases include:

• Synthetic data for data portability, across organisational silos and geographical boundaries
• Synthetic data to evaluate third-party open innovation partners more quickly
• Synthetic data for testing for safer copies of data that still relies on realistic data
• Synthetic data for data analytics to get access to accurate — but not real — data quickly to train and test machine learning algorithms
• Synthetic data for cloud migration means you don't have to put real data in the cloud, but rather store the value of the data, without any data risk

Synthetic data accomplishes all this while being 100 percent private, 100 percent compliant, by design.

It’s time to decouple the data from the privacy.