Data has become central to how effective funders operate. Where earlier generations of grantmakers relied primarily on relationship knowledge and assessment judgment, today's sophisticated funders also use data — portfolio analysis, sector mapping, outcome tracking, demographic analysis — to understand the impact of their grantmaking and make better decisions.
The challenge is that philanthropic data is often poor quality, poorly structured, and underanalysed. This guide covers how funders can use data to improve grantmaking without becoming paralysed by measurement.
Portfolio understanding
At its most basic, portfolio data answers: what are we funding, how much, for how long, and with what results? A clear picture of the portfolio — by sector, geography, organisation size, grant size, and outcome — is the foundation of informed grantmaking.
Without this view, funders often don't realise they're significantly over-funding some areas and under-funding others; making many small grants where fewer larger ones would be more effective; or funding organisations with strong track records while leaving similar-quality organisations unfunded simply through lack of information.
Sector mapping
Where are the gaps? Who is working in a field, at what scale, with what resources? Sector mapping combines grantmaker portfolio data with external data (Charities Register information, MBIE data, other funder information) to create a picture of the philanthropic ecosystem in a given domain.
This helps funders identify: organisations that are significantly under-resourced relative to the scale of need they address; gaps where no organisations are working; concentrations where many funders are pursuing the same organisations.
Equity analysis
Is grantmaking reaching the communities it should? Demographic analysis of grant recipients — organisation size, geographic location, governance composition, communities served — reveals whether equity commitments are reflected in actual grants.
Common findings from equity analyses: large, well-established organisations receive disproportionate funding; regional and rural organisations are underserved; Māori and Pacific-led organisations receive less per capita than their community proportionately needs; smaller grants go to organisations serving communities with higher deprivation.
Impact tracking
What outcomes is the portfolio producing? Aggregating outcome data across grants — across different organisations, programmes, and timeframes — builds a portfolio-level picture of what the funder's investment is achieving.
This requires consistent outcome frameworks: if every grant uses different outcome indicators, the data can't be aggregated. Some funders use standardised outcome domains (economic wellbeing, physical health, mental wellbeing, social connection) and ask grantees to report against these regardless of their specific programme.
Predictive and decision-support analysis
More advanced funders use data to support assessment decisions — tracking organisations over time, identifying patterns in which types of interventions produce outcomes, and using past grant performance to inform future assessments.
Internal grantmaker data
This data exists in most funders' systems but is often siloed, inconsistently structured, and underanalysed.
External public data
Grantee-provided data
Quantitative and qualitative reporting data from grantees — outputs, outcomes, financial information, stories of change.
Inconsistency: Application forms change, staff change data entry practices, different programmes collect different information. Data accumulated over years may not be comparable across time or programme.
Quality: Grantee reporting data is often self-reported and not independently verified. Outcome data may reflect what grantees think funders want to hear rather than what's actually happening.
Coverage: Not all grants include the same reporting requirements; some old data is missing; some organisations fail to report. Gaps in data make analysis unreliable.
Analysis capacity: Many smaller funders don't have staff with data analysis skills. Data sits in grants management systems without being used.
Comparability: Outcome indicators are not standardised across the sector, making it impossible to compare grants or funders directly.
For funders who haven't used data analytically, pragmatic first steps:
Audit what you have: What data is currently collected in your grants management system? How consistent is it? What are the biggest gaps?
Standardise going forward: Agree on consistent data collection — standard organisation categories, consistent geographic coding, shared outcome domains. Don't try to retrospectively fix old data; focus on consistent collection from here.
Build basic portfolio views: A simple analysis of your grants by sector, geography, organisation size, and grant size is immediately useful. This doesn't require sophisticated tools — a well-structured spreadsheet is sufficient to start.
Add outcome tracking: Define 5-10 high-level outcome domains that span your portfolio. Ask grantees to indicate which outcomes their grant contributes to. Even approximate categorisation creates a more meaningful picture than no outcome data.
Invest in tools: Grants management systems that make reporting, analysis, and data visualization accessible are worth the investment. The goal is making data exploration easy for non-technical staff.
Philanthropic data involves personal information — about grant applicants, grantees, and in some cases the communities they serve. Privacy obligations apply:
Tahua's grants management platform is built with analytics in mind — configurable reporting, portfolio views, and the data structure that makes philanthropic analytics useful rather than just accumulated. It gives funders the tools to move from managing grants to understanding their impact.