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FAQ with our CEO

Short FAQ with our CEO, Padraig:

Explain us very briefly your proposal?

Every enterprise wants to leverage their data. But, governments want to regulate it and consumers want to protect it. These are at odds with one another. So we saw a need for a platform that could meet the needs of everyone. To democratise access to data in the enterprise. But at the same time, provide the necessary controls on the data to enforce any regulation, and ensure that there was transparency as to who is using the data and why.

QueryLayer gives you that granular, dynamic control — without copying data or writing a single line of code. It allows an organisation to open up access to raw data while protecting that data against the leakage of personal information. The result is instant data self-service with powerful security and oversight.

How does GDPR affect Fintech startups?

On one level, we are all very aware of the intent behind GDPR -to give citizens back control over their personal data. This means that businesses are expected to understand exactly where personal data within their business lies, why it was collected and how it will be protected. And all FinTechs have a legal obligation to meet these requirements.

But there is another level to GDPR - one which is pushing forward the adoption of a new generation of cyber-tools. These tools are being laid on top of the availability of compute and the availability of storage which has been driving innovation in FinTech over the past few years.

The FinTech startups in this space are reacting to the opportunity around GDPR to provide the solutions and the tooling that helps Financial Institutes to make sense of and manage of their data. A lot of these solutions are being seen as an effective way for banks and financial institutions to fulfil the Article 30 requirement (knowing what and where personal data has been processed).

We see QueryLayer as being part of this new wave of intelligent data tools that is helping FinTechs use their data smarter across the enterprise.

How can data anonymisation safely increase data analysis? Data anonymisation is a technique that can be used to protect private information in your data while preserving, to varying degrees, the utility of that data. If a dataset is perfectly anonymized, there is no risk in identifying an individual from that data, but that data also might be useless.

In many cases, anonymization techniques can be “fragile,” which is to say that even once you believe the utility vs. privacy tradeoff is balanced, the security of anonymised datasets can be dependent upon a variety of external factors that are hard to control.

So what does this really mean in practice?

It means that anonymization without context isn’t the solution. What you need is a dynamic data abstraction layer.

A dynamic data abstraction layer is a thin layer that sits on top of all your data, making decisions based on your policies and how data is presented to consumers. This allows organizations to restrict access based on certain purposes (analytical context), or certain attributes of a user.

Most importantly, it actually enables better data science because this abstraction layer provides a consistent and unified view of the data, which makes sharing analysis easier, as the data comes from the same place for everyone.

What application does QueryLayer have beyond Fintech?

Healthcare is one of the most rapidly changing industries in the world. New illnesses emerge, or old illnesses reappear. Treatments – often technology-based – are constantly evolving. Due to the well-being aspect of healthcare, the industry is often the subject of intense scrutiny, and so finds itself adapting to new laws, regulations, or social perceptions.

Modern healthcare companies are heavily reliant on data. Sensitive and personal information is continuously collected through personal assistant apps. QueryLayer can help the healthcare industry to new develop new data-based business models and services without compromising the privacy of users.

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