Data-driven grantmaking doesn't mean replacing human judgment with algorithms — it means using available evidence to complement and improve philanthropic decision-making. Foundations that use data well make more informed decisions about where to invest, identify gaps in their portfolios, assess whether funded programmes are working, and demonstrate accountability to donors and communities.
Grantmaking decisions — which organisations to fund, at what level, for what purposes — are consequential. They determine which communities receive resources and which don't. Getting these decisions right matters enormously.
Data supports better decisions by:
Data doesn't eliminate uncertainty — but it reduces it. A funder that uses data alongside relationship and judgment makes better decisions than one that relies on either alone.
Community needs data
Understanding where need exists:
- Population data (census, Statistics NZ/ABS)
- Socioeconomic indicators (deprivation indices, poverty measures)
- Health outcome data (PHO registers, DHB data)
- Education and employment data
- Housing affordability and homelessness data
- Geographic mapping of services versus need
Evidence of programme effectiveness
What approaches work for the problems being addressed:
- Systematic reviews and meta-analyses
- Programme evaluation findings
- Practice-based evidence from grantees
- International research and implementation examples
Grantee organisational data
Assessing the health of grant applicants:
- Financial statements and ratios
- Governance structures and track record
- Staff capacity and expertise
- Previous grant performance and acquittal
Portfolio data
Understanding the foundation's own grantmaking:
- Geographic distribution of grants
- Sector and demographic distribution of grantees
- Grant size and duration patterns
- Multi-grant relationships over time
- Diversity of funded organisations
Impact and outcome data
What the funded programmes achieved:
- Participant data and outcomes
- Programme implementation fidelity
- Grantee self-reported outcomes
- Independent evaluation findings
Geographic mapping of grants and community need is one of the most powerful and underutilised data tools in philanthropy:
Tools like GIS mapping, Statistics NZ's deprivation indices, and 360Giving's GrantNav (in the UK) enable funders to visualise their funding distribution. New Zealand equivalents include the New Zealand Deprivation Index and Statistics NZ geographic data tools.
Data analysis can surface equity issues in funding:
- Are Māori and Pacific organisations receiving funding proportionate to their community need?
- Are small organisations with less grant-writing capacity excluded by application requirements?
- Are rural organisations accessing funding equitably?
- Are LGBTQI+-led organisations represented in the portfolio?
Disaggregated data — breaking down who applies, who is funded, and at what levels, by demographic category — reveals patterns that aggregate data obscures.
Important caveat: data collection from grant applicants must be handled with care — collecting demographic data requires clear purpose, informed consent, and careful privacy management.
Effective Altruism-influenced analysis
Some funders use explicit effectiveness analysis — prioritising grants to programmes with the strongest evidence of impact per dollar. This approach draws on systematic evidence review and cross-programme comparison.
Theory of change analysis
Funders assess applicants' theories of change — is the causal logic coherent? Does the evidence support the claimed pathway from activities to outcomes?
Portfolio-level learning
Using data across the grants portfolio to identify patterns — which programme types work best, which grantees consistently deliver, where investment is most productive.
Needs-based allocation
Allocating grants in proportion to community need — using deprivation indices, health data, or other need measures rather than allocation based on grant applications received.
A grants management platform generates data about every stage of the grants process:
- Application volumes and sources
- Assessment scores and patterns
- Grant amounts and duration
- Reporting patterns and quality
- Renewal and decline rates
Regular analysis of this operational data reveals patterns — assessment inconsistencies, reporting lags, funder-grantee relationship patterns — that improve operational performance.
Data in grantmaking has real limitations:
The most effective foundations use data alongside relationship, qualitative evidence, and genuine community engagement — not as a replacement for these.
Tahua's grants management platform supports data-driven grantmaking — with portfolio analytics, geographic grant mapping, grantee health tracking, diversity reporting, and the evidence dashboards that help foundations make more informed, equitable, and impactful funding decisions.