Te Pā Raraunga: The Development of a Data Governance Model for Ngāti Hauiti

Data have always been a taonga (treasure) for tribal communities, supporting aspirations and wellbeing. In 2005, Ngāti Hauiti established Aotearoa's first tribally‐owned health research centre, Whakauae Research Services Ltd., to build research capacity among Ngāti Hauiti uri (descendants) and undertake high‐quality, Māori‐centred health research to improve outcomes for Māori. While data stewardship has always been important to Whakauae, the emergence of Indigenous Data Sovereignty and Indigenous Data Governance has enabled the organisation to further strengthen its approach. This article presents the early stages of developing a Māori Data Governance model as a case study for organisations working with Māori, especially tribal data. The process involved mapping Ngāti Hauiti's data ecosystem and then co‐developing a governance model, Te Pā Raraunga, with a cultural advisor, informed by tribal knowledge. The model uses the metaphor of a pā tūwatawata (fort defended by a stockade), with each part representing a key aspect of data governance identified through the mapping exercise. The article further discusses the model's implementation and next steps for the socialisation of the model. Developing internal capacity and collective understanding are highlighted as necessary for Whakauae and Ngāti Hauiti's successful enactment of data governance, as well as for other organisations starting a data governance journey.

Authors

Institutions

Publication Details

Journal
Journal of the Royal Society of New Zealand
Published
2026-05-14
DOI
https://doi.org/10.1002/snz2.70052
Primary Topic
Indigenous Health, Education, and Rights
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Te Pā Raraunga: The Development of a Data Governance Model for Ngāti Hauiti

Logan Hamley, Amohia Boulton, Darcy Karaka
Journal of the Royal Society of New Zealand
Indigenous Health, Education, and Rights
article

Te Pā Raraunga: The Development of a Data Governance Model for Ngāti Hauiti

Logan Hamley, Amohia Boulton, Darcy Karaka
article en

Abstract

Data have always been a taonga (treasure) for tribal communities, supporting aspirations and wellbeing. In 2005, Ngāti Hauiti established Aotearoa's first tribally‐owned health research centre, Whakauae Research Services Ltd., to build research capacity among Ngāti Hauiti uri (descendants) and undertake high‐quality, Māori‐centred health research to improve outcomes for Māori. While data stewardship has always been important to Whakauae, the emergence of Indigenous Data Sovereignty and Indigenous Data Governance has enabled the organisation to further strengthen its approach. This article presents the early stages of developing a Māori Data Governance model as a case study for organisations working with Māori, especially tribal data. The process involved mapping Ngāti Hauiti's data ecosystem and then co‐developing a governance model, Te Pā Raraunga, with a cultural advisor, informed by tribal knowledge. The model uses the metaphor of a pā tūwatawata (fort defended by a stockade), with each part representing a key aspect of data governance identified through the mapping exercise. The article further discusses the model's implementation and next steps for the socialisation of the model. Developing internal capacity and collective understanding are highlighted as necessary for Whakauae and Ngāti Hauiti's successful enactment of data governance, as well as for other organisations starting a data governance journey.

Journal of the Royal Society of New ZealandVol. 56(3)
Whakauae Research (New Zealand) (NZ)
Openalex Percentile: Top 3%
Indigenous Health, Education, and Rights
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.