Certified B Corporation


Key initiatives

CLASSIE is a landmark initiative that enables systematic classification of social sector initiatives and entities – a social change dictionary.

The taxonomy draws from the US Foundation Center’s well-respected Philanthropy Classification System, as well as a deep well of knowledge contributed by a team of more than 50 subject matter experts across Australia and New Zealand.

CLASSIE (Classification of Social Sector Initiatives and Entities) is used to create consistency across Our Community platforms and beyond – without consistency, global insights are very difficult to extract.

Developing a common language helps us join the dots and derive insights that cut across a range of government, business and community activities.

Read more about CLASSIE →

Our Community’s CLASSIEfier project involves applying data science to the task of auto-classifying written records (e.g. grant applications, appeal descriptions, mission statements). This allows us to classify past records instantly, reveal funding patterns and create benchmarking insights.

Read more about CLASSIEfier →

Our Community House is a co-working space opened in North Melbourne in 2019. Supported by Equity Trustees, activities undertaken by the Innovation Lab’s OC House project include:

  • Co-locating not-for-profit organisations with a supervised, skilled team of data scientists. Data scientists are available for casual consultations with OC House members, as well as establishing an ongoing, one-on-one relationship with 6-8 organisations per annum, and undertaking two semi-intensive flagship projects per annum;
  • Upskilling social sector organisations in data science through consultations (per above), mentoring, workshops and webinars;
  • Scaling access to data science information through development and dissemination of case studies, help sheets and templates, and using our flagship projects to develop reusable tools (e.g. data analysis/visualisations, client personas, client journey analyses, etc);
  • Fostering a community of ‘data scientists for good’ in Australia by creating a pipeline from university and corporate partners to social sector organisations and projects, and by hosting ‘data science for good’ meetups, hacks and other events;
  • Identifying and fostering opportunities for not-for-profit, governmental and academic organisations to participate in joint data science projects that result in widespread application and benefit

See what’s been happening →

We want to make it easier for community groups to recruit, raise funds, fast-track grant applications and manage all of their information in one place.

OurFile centralises and systematises the storage and retrieval of reusable information about social sector organisations.

A central repository will be linked with Our Community’s numerous online systems (including SmartyGrants, GiveNow and the Join In, Join Up! directory), making uploading and updating of data into online appeals, grant applications and a range of other services and platforms instant and effortless.

OurFile also lays the groundwork for the creation of networking, benchmarking and governance/oversight tools that can help social sector organisations identify allies and collaborators, compare themselves with other organisations in their field or of their type, and super-charge their performance.

Millions of hours and billions of donors are flowing into the social sector each year – to what end? We want to know what changes are being created, as well as learning how we can replicate the good changes and avoid the bad ones.

We’re working on the creation of a universal Outcomes Classification (part of CLASSIE) that can be used to help us collect and categorise information about what changes are being sought, and how those initiatives are panning out.

In addition, we’re working on systems that can be embedded into our platforms (our ‘Outcomes Engine’) that will help generate better-designed, better-interrogated, better-understood social-change initiatives.

Importantly, we want to showcase what is learned along the way to anyone and everyone working towards the same aim. The Centre for What Works will reveal insights about what activities and outputs seem to lead to particular outcomes, while the Plans and Tools Bank will provide a knowledge base of templates and tools associated with interventions that work.

Our ‘What Works’ initiatives will help our partners close the design → deliver → evaluate → design loop.

Machines can’t do everything, but they can do a lot of things.

It’s a brave new world, but we’re getting started. Our first major step into this arena is an investigation of how algorithms and artificial intelligence can be used to eliminate bias and speed up grant application assessments. We’re also looking for ways to assist grantwriters to draft and check their applications, to improve the quality of applications submitted. Next up is an online donations experience that’s tailor-made for the user.

In our artificial intelligence projects, we always aim to build explainable models. We avoid black-box algorithms (opaque systems of decision-making) as much as possible, focusing instead on methods that allow us to quantify the why in the prediction. Why did our method lead to this decision? What are the strengths and weaknesses of the algorithm? Are we propagating existing biases in the data, or eliminating them? How will our model behave in the future? We believe these questions are imperative, and more important than a 0.1% more accurate model.

We support transparency in the social sector, particularly when it comes to knowing and showing where the money is going, and the effect that money is having in creating social change.

There is an increasing level of interest in open datasets, as well as new regulations that seek to apply transparency to the work of government.

We want to help platform users open up their data and provide them with tools to navigate it. Work is under way to adopt an open data standard for grants information in SmartyGrants, and provide a one-click tool to allow willing grantmakers and users to export what they want, where they want.

We’re creating a range of tools to help grantmakers and grant recipients uncover and remove biases, creating fairer, more effective social change.

In our first initiative we worked with the Australian Women Donors Network to create greater gender awareness in the awarding of grants. Gender-wise grantmaking occurs when grantees are encouraged to consider the social disadvantage women and girls face when designing and delivering grants-funded projects and programs, and where grantmakers themselves consider gender when setting and reviewing funding priorities. This work included research into ways in which organisations can apply a gender lens to their work.

Awareness-raising is never enough for us. We want to create tools that will turn awareness into action. Three new Gender Lens standard fields were introduced to SmartyGrants in 2016. We’re monitoring the use of these fields as part of our efforts to ensure this important work can stick and spread.

More about our gender-lens work →

Turning data into action: Analysis, visualisation & dissemination

We have an in-house data science team to interrogate the data that we oversee. Underpinned by our “useful trumps interesting” mantra, we’re driven to ensure that our findings have real-world applications. Some example outputs:

Following a year-long research project into governance practices in the not-for-profit sector, we compiled the major findings collected from the aggregate responses, and published a roadmap for the sector, including action points.

Download the report → See the interactive graphic →

A new report on LGBTIQ+ funding in Australia, published in the form of an interactive data graphic, paints a bleak picture of the state of funding for rainbow communities. Using data collected by Our Community’s SmartyGrants platform, the report reveals that only 0.07% of federal government grants are going towards LGBTIQ+ causes. This number is similarly low for local and state governments: 0.37% and 0.1%, respectively.

See the interactive graphic →

An analysis of $100m in donations to online platform GiveNow since 2001 shows women give more often, but men chip in larger amounts.

The study also reveals gender differences in the causes we support; for example, women are three times more likely as men to give to animal welfare causes, while men are twice as likely as women to donate to gay and lesbian causes.

See the interactive chart →

In 2016 we conducted an in-depth review of donation data in our GiveNow platform and launched Who Gives?, a ground-breaking report that can be used by community groups to inform their fundraising strategies.

The analysis of more than half a million donations over 15 years allowed us to uncover emerging trends in donation patterns, as well as sector-specific and gender-specific donation patterns that not-for-profits can use to tailor their approach.

Read the Who Gives? report →

Our yearly Grants in Australia report combines the analytical prowess of our data science team with the deep grantmaking domain knowledge in our organisation, allowing us to chart historical trends and create practical takeaways for grantmakers.

The survey has also enabled us to develop a useful benchmarking tool for grantseekers, allowing them to compare themselves to organisations by size, sector and state (see example to the right). To access the the benchmarking tool, you must be a member of the Funding Centre.

Download the latest Grants in Australia report →


In mid-2017, we launched SmartyGrants dashboard functionality to deliver instant insights to grantmakers and allow them to visualise their data.

Dashboard widgets encourage grantmakers to think more strategically about how they administer their programs and carefully interrogate their funding patterns.

SmartyGrants users: log in to see your dashboard →

Mapping tools

We also built SmartyGrants Maps to help grantmakers analyse their activities and improve their practices. SmartyGrants users can see where grant funds were requested, where they were allocated, and to help identify areas that have been overlooked.

More information about SmartyGrants Maps →

Smarter feature design

We use data analysis as input into our feature design. For example, we use techniques such as A/B testing to better understand user behaviour and to assess what options are the most appealing/effective.

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