Grantmakers know they need to be more data driven, here's how

Posted on 29 Aug 2022

By Stefanie Ball, Innovation Lab

Grantmakers understand well the role that effective data practices can play to increase the impact of their programs, with many already using various tools and practices to boost their organisation’s data capability.

Not all organisations are at the same stage of data maturity, however. For organisations that understand the importance of data but haven’t quite got to the implementation stage, the daunting part might be the “how” of using data effectively. A SmartyGrants Innovation Lab framework aimed at grantmaking organisations can help clarify your thinking and simplify your approach to grantmaking data.

Developing Data Capability as a Grantmaker outlines the most common types of data a grantmaking organisation deals with and matches them with a hierarchy of what you can do with the data.

Grantmaker framework cover

Six data sources grantmakers often use

Promotions Website traffic, social media, email campaigns, posters, flyers, community noticeboards, information sessions, training, community consultations, catering, travel
People Staff, applicants, grantees, assessors, decision-makers, beneficiaries, board members, community groups, peak bodies, members of the public
Operations Contracts, income, expenditure, assets, property, security, privacy, risk, and more
Needs Public datasets and demographic data, market analysis, surveys, requests for support
Grants Application and acquittal forms, activities, events, logistics and outputs relating to your grants programs
Impact Metrics, indicators, surveys, case studies, evidence of knowledge change or behavioural change
Data sources from framework
These data sources serve two broad purposes: the first three kinds of data have the potential to make your organisation more efficient, while the last three can make your programs more effective.

A hierarchy of uses for data

Collect and store

This level is the foundation of any data initiative. What information is your organisation gathering? Where is it being stored? Is it contained in one central database, or across multiple platforms?

Process and explore

Here you’re starting to interrogate and sort your data, and perhaps classify it. This step requires getting the data into a useable state (i.e. processing the data), exploring its main characteristics, extracting basic insights, and identifying patterns that may trigger further questions.

Report and visualise

At this level, you’re starting to paint pictures with your data, such as reports and visualisations that communicate what the data is telling you.

Learn and optimise

This is where data science really takes off. Here, at the top of the pyramid, you’re learning from your data and using it to influence your decisions, make predictions, and drive better practice. You can also use it to streamline manual tasks, making your organisation more efficient.

Hierarchy from framework
Your data goals will depend on your objectives, activities, and skillsets. It’s essential that you have experience and the right infrastructure in place in the lower rows before you attempt to execute upper-level projects.

How to use the framework in your organisation

The framework is a useful starting point for developing a strategic approach, according to Innovation Lab data scientist Dr Nathan Mifsud.

“With the support of Equity Trustees, we published a similar framework in 2020 aimed at not-for-profit organisations, as part of a broader effort to boost the data capability of the social sector. After showing it to several grantmakers and finding that there was interest, we decided it would be useful to repurpose,” said Dr Mifsud.

“Grantmakers juggle a range of data sources, so it’s easy to get lost in the weeds. The simplicity of this tool prompts organisations of various sizes and at different stages of data maturity to ask what they’re doing and why.”

Let’s look at the example of a grant program administered using SmartyGrants to help us understand the data-use goals pyramid.

Collect and store

First, forms are designed and built in SmartyGrants. Users should consider data analysis and use these considerations to guide the design of the forms. Once the grant round and application forms are ready to be published and promoted to your target audience, the collection of data commences.

Process and explore

Next, as applications are lodged and assessed, you can begin processing them to identify patterns or themes. Though much of the data processing is automated for SmartyGrants users, there is still value in having a human look at (explore) the data and ask intelligent questions. The framework suggests referring to your outcomes goals to help guide questions.

Report and visualise

You might want to generate reports or visualisations to evaluate and communicate outcomes. You can create your own reports and dashboards by exporting data from SmartyGrants to Excel, and on into reporting and visualisation tools like Power BI and Tableau, using pivot tables, charts, and other visualisations that communicate your impact in different ways. The framework offers advice about the types of questions to ask to guide your reporting.

Learn and optimise

If you’re at the apex of the pyramid, you’re starting to use data science to influence your decisions. You could use insights from your program as inputs to a model, allowing you to predict variables to improve your grantmaking (an example of this is the auto-classification tool CLASSIEfier). You could also conduct experiments to see what works bests, such as running two consecutive grants programs designed slightly differently to see which one receives more (or better) applications. Automating tasks such as classification, eligibility checks, assessments, shortlisting and more can help to make your organisation more efficient. The SmartyGrants team is working hard on building some of these data science features into the platform to make advanced analysis more accessible to all grantmakers.

What next?

The framework can be used to start conversations within your organisation and guide you as you gather information about what your organisation is already doing. These results can form the basis of an assessment of your organisation’s data maturity and help you set goals for the future.

Build a picture of the data you are already collecting to start a conversation in your organisation.
Systems approach
Take a systems approach to identify what systems you have in place, who has access, and what skills are required for work on data projects for each area.
Frame thinking
Frame your thinking by mapping out what questions you’d like to answer, and what data and data capabilities you’ll need to do so. Start simple and build from there.