Meshblock background animation

Analytics, research, and data visualisation that make a difference

We help people
tell stories with data

We work with organisations, businesses, and communities to make the most of information by improving data collection, access, and understanding.

Data Visualisation

Visualisation turns data into accessible and usable information. We design simple to use interactive tools that allow users to explore the information while keeping the data secure.

Data Design

We design research projects that are efficient, effective and tailored to your information needs, and can advise on, supervise, or review projects — including through a Māori research lens.

Data Collection

Data collection can be expensive and time-consuming. We offer support for collecting data from surveys or existing sources.

Data Analysis

We can analyse a wide variety of data from administrative through to qualitative datasets. We specialise in applied social and epidemiological data analysis, including use of the Statistics New Zealand Integrated Data Infrastructure.

Training

Learning to use data should be fun, accessible, and relatable. We provide software training, short courses on data methods, statistical literacy workshops, work experience opportunities, student supervision, and scholarship and funding advice.

Data Sovereignty

Data should be used for tāngata, by tāngata, with tāngata. We help communities design research projects that meet their goals and aspirations, advising them on research design, planning, collaboration, and Māori data sovereignty decision-making.

Our work

Population Demographics Explorer

This interactive tool visualises New Zealand's projected demographic changes from 2018 through 2043, providing insights into both total population and voting-age population trends across national and regional levels.

Some of the features include interactive population pyramids, to explore age and gender distributions; an interactive map, to compare demographic trends across regions; and electoral insights, to examine voting-age populations and new voter estimates.

iNZight Software

iNZight is a free and accessible statistical visualisation software. Members of the iNZight Analytics team contribute to its development. It was initially designed for New Zealand high schools, allowing students to quickly and easily explore data and understand some statistical ideas (using the companion program VIT). However, iNZight now extends to multivariable graphics, time series, and generalised linear modelling (including modelling of data from complex surveys).

IDI Search App

IDI Search is a web app that allows researchers to search for variables that are available in the IDI and, in some cases, metadata about these variables. The app uses data from IDI variables and Data Dictionaries shared with us by Stats NZ. The data are stored in a database which can then be searched using the web app.

IDI Search was developed by Te Rourou Tātaritanga, a research group funded by an MBIE Endevaour Grant (ref 62506 ENDRP).

Map of the world with COVID hotspots

COVID Modelling

Managing director Andrew Sporle was part of the initial COVID-19 pandemic modelling team with Te Pūnaha Matatini with a particular focus on equity. He helped to create an early tool that looked at regional outcomes by age and ethnicity if the pandemic continued without public health interventions. The team won the 2020 Prime Minister's Science prize for their work.

Since then Andrew has been involved with further COVID-19 projects, including a project that aims create a population based contagion model for New Zealand (led by Dr Dion O'Neale).

Andrew has also been involved in ESR work exploring genetic subtypes, resulting in the first paper to identify on plane transmission of COVID, and a second workstream demonstrating that the most effective vaccine rollout strategy for Aotearoa was one that prioritised the needs of Māori and Pasifika.

Recently, Andrew has been involved in work around improving access to Māori data from the Ministry of Health.


Pacific Health Reporting

Pacific peoples are often treated as a single group for the purpose of reporting on health outcomes in New Zealand, but this ignores the diversity between specific Pacific ethnic populations.

This report summarises work conducted using Statistics New Zealand’s (Stats NZ) Integrated Data Infrastructure (IDI) to better capture this diversity and enable more accurate reporting on cancer outcomes (all cancers and stomach cancer) among those who identify with “Level 2” Pacific ethnicities: Samoan, Cook Islands Māori, Tongan, Niuean, Tokelauan & Fijian.

This work was supported as part of a Health Research Council (HRC) Programme Grant 17/610 led by Professor Parry Guilford at the University of Otago.

Got an idea for a project? Work with us.

We do data differently

We have more than three decades of experience in quantitative, qualitative, and mixed-methods health and social science research, working with communities, policymakers, and businesses.

The iNZight Team

We're well connected

iNZight Analytics works in partnership with government agencies, research organisations, and community groups in Aotearoa New Zealand and around the world.

University of Auckland
Ministry of Business, Innovation & Employment
University of Otago
Life Course Centre
Stats NZ
Victoria University of Wellington
University of Canterbury
Tūhono
Global Indigenous Data Alliance
Massey University
Te Pūnaha Matatini
National Science Challenges
Royal Society Te Apārangi
Health Research Council of New Zealand
Ministry of Social Development
Te Whatu Ora | Health New Zealand
Social Investment Agency
NZSA
Population Association of New Zealand
Stanford University
Australian National University
University of Tasmania
University of the South Pacific
CSIRO Data61
International Sociological Association
Horizon Europe
The Dunedin Study
University of Auckland
Ministry of Business, Innovation & Employment
University of Otago
Life Course Centre
Stats NZ
Victoria University of Wellington
University of Canterbury
Tūhono
Global Indigenous Data Alliance
Massey University
Te Pūnaha Matatini
National Science Challenges
Royal Society Te Apārangi
Health Research Council of New Zealand
Ministry of Social Development
Te Whatu Ora | Health New Zealand
Social Investment Agency
NZSA
Population Association of New Zealand
Stanford University
Australian National University
University of Tasmania
University of the South Pacific
CSIRO Data61
International Sociological Association
Horizon Europe
The Dunedin Study

Want to work with us? Get in touch.

Latest news

iAL's Nicole Satherley to present on social mobility project at two international conferences in May

Nicole Satherley will present on iNZight Analytics’ social mobility project at two international conferences in May 2026. 

Abstracts for the project were accepted for presentations at the International Association for Official Statistics conference in Vilnius, Lithuania during 12 – 14th May, and the RC28 Social Stratification (of the International Sociological Association) Spring Meeting in Seville, Spain during 20 – 22nd May.

The presentations will allow us to discuss initial project findings and the strengths of New Zealand’s national data resources to international experts in social stratification and official statistics.

You can read more about the project here, or at the link below.

20 March 2026
Publication

iAL's 2026 Summer Studentships come to a close

iAL are proud to have hosted another four students over the 2025/2026 summer break, all from the University of Auckland.

Gemma Ngari worked to develop a working prototype of a web app to visualise projections of the total population, working age population, and voting age population across a diverse range of Pacific nations.

Kasish Prasad's work looked at addressing challenges in classifying Pacific ethnic identity in official statistics. This had a particular focus on how statistics about Indo-Fijian populations is collected in New Zealand, Australia, and other countries, including Fiji.

Ken Deng's summer project aimed to improve iNZight software's iNZightRegression package by creating user-friendly tools for verifying model assumptions. Ken developed a comprehensive suite of interactive functions that guide users through checking key regression assumptions, including normality, linearity, multicollinearity and constant variance, combining statistical tests with clear visualisations.

Erena Tanabe's project focused on implementing Bayesian parameter estimation to the iNZight software using analytical methods. This included researching appropriate conjugate priors to use for the types of variables and variable combinations seen when exploring and analysing data, and coding methods for these to perform parameter estimation.

Supporting talented students is an important part of iAL's commitment to building a future applied statistics and data science workforce, and we're looking forward to seeing where these four take their careers

28 February 2026
Publication
27 January 2026
Publication