WG4 – Increasing Trusted Data Accessibility

Growing value from our strategic data assets

AI, as with the adjacent data sciences, is dependent upon the ready – and trusted – availability of high quality, accessible data. There are huge economic, social and environmental opportunities from treating our data as a national strategic asset and using AI to realise the value contained therein.

Scope:

Increasing Trusted Data Accessibility, is focussed on progressing thinking about, and exposure to, accessing trusted data and achieving trusted data use – both key to realising the value of data.

How we’re doing it:

We are working towards a Trusted Data Playbook which will provide:

  • tools for fostering trust and confidence in the use of data;
  • examples of what constitutes trusted data use from around the globe, with recommendations for further development;
  • an overview of how trusted data is accessed including the frameworks and agreements used to enable access to trusted data;
  • case studies demonstrating the value of trusted data accessibility and use to the adoption of AI

Working Group Leadership:

Summary of mission, goals & scope

Our aim is to support New Zealand organisations to understand the level of trust in different uses of data, and to foster the trust of New Zealanders in the way that data is used. The playbook will inform the safe and trusted exchange of data and enable conversations in New Zealand, and with New Zealand, about treating data as a national asset.

Current Initiatives

Early milestones include:

  • the focus areas and categories to be covered by case studies
  • a summary of existing trusted data use work.

Challenges

We are mindful to the significant amount of work going on in the space across the public and private sector, and not wanting to replicate that effort. We see our value had as helping to provide a practical starting point, and guide for those looking to navigate trusted data use.

We are always happy to hear from organisations, and people operating in the trusted data space. We are keen to understand how they have approached these issues, the challenges they’ve had and any lessons they have learned.