Data for whom? Removing barriers to sharing for the benefit of Māori
Dr Kiri Dell, Ngapera Riley, CEO figure.nz
Ref Decolonising Methodologies by Linda Tuhiwai Smith
The academy privileges a certain type of knowing, but indigenous people have other ways as well (which we all use to some degree) eg
- Sense perception – I felt it
- Imagination – I envisioned it
- Memory – I remembered it
- Inherited – My nanny told me
- Faith – God told me
Example of using data badly: MOTU (economic research centre) put out research a few years ago comparing Māori and other ethnicities and concluding that collectivist beliefs were holding Māori back from economic success. This was not taken well by Māori…. Researchers made sweeping statements about Māori culture where they had no research; compared Māori to completely different groups (eg African Americans, Chinese) with different histories and belief systems; interpreted through white male lens.
Pull the wings off a dragonfly and look – you get a red pepperpod!
Add wings to a pepperpod, and look – you get a red dragonfly
Figure.nz set up as charity to democratise data – aims to provide valid and ethical data. Data is important but so is context and people behind it. Draws licensed data from over a hundred sources. Exists for the benefit of Aotearoa so believe if they can get data right for Māori they can get it right for all. Kaupapa that data is for everyone not just experts.
Partnering with Te Mana Rarauranga – Māori Data Sovereignty Network; and with nine government agencies who’ve got a lot of data that was never meant to be shared so navigating benefits and dangers of sharing. How will data be used, for whom, why?
Data is never perfect – it’s just one tool alongside experience and connections. Māori data has traditionally not been collected well – cf especially the latest census – so have to be careful about conclusions drawn.
Figure.NZ – over 44,000 charts and datasets (CSVs and images) around people, travel, health, education, employment, economy, environment, social welfare, technology, broken down by geographic area. Very careful to publish metadata around sources etc.
Original data was a mess so have been working hard to tidy it up. Check sources, make sure it’s statistically valid (no small datasets) – have a robust process to work with source to make sure the metadata explains methodology and context.
Focused on public aggregate data but starting to use other sources. Wondering how to safely share research data. Excited to see people have started publishing theses etc with CC licensing.