Good Cycles Data CO-OP
Artificial intelligence, machine learning and other data analytics tools are increasingly being used across the commercial and government sectors bringing a range of benefits to users able to harness the power of data. However, these new tools, skillsets and capabilities are unevenly distributed. With less resources available to build data capability and confidence within their workforce the Not-For-Profit (NFP) sector risks being left behind.
Melbourne-based Good Cycles is a social enterprise that addresses youth unemployment, by providing employment opportunities for young people through a range of quality jobs to ensure their better futures1. The ‘Transitional Employment Program’ is designed to develop life skills and confidence in young people facing barriers to work while providing support pathways to employment. Simultaneously, this NFP builds more sustainable cities through the use of bicycles1. Thus, with a mission to empower at-risk-youth by offering various employment pathways, they improve young people’s futures whilst contributing to a more sustainable future1.
To enable Good Cycles to use their organisational data to tell the story of Good Cycles' work and social impacts, Swinburne’s Social Innovation Research Institute partnered with the Lord Mayor’s Charitable Foundation (LMCF) to deliver the Data for Good Collaboration. With a focus on developing the soft skills needed to navigate the evolving data landscape, we facilitated a series of interactive co-design workshops and created a suite of data capacity-building webinars to promote data literacy amongst our participating NFPs. Using the Social Data Analytics (SoDA) Lab’s Data CO-OP methodology - a collaborative approach to data sharing using a combination of private and public datasets - our team of data scientists worked together with Good Cycles to explore various organisational datasets and co-create data insights and visualisations.
Using the Education and Community (ASGS and LGA, 2011, 2014-2019) dataset from the Australian Bureau of Statistics data collection, the project team also derived three insights. Firstly, in 2016, around 52 percent of Australians (15 years and older) had finished Year 12 or equivalent educational qualifications. Secondly, the number of available jobs for men increased by 8.2 percent as compared to the jobs available for women in 2017. And lastly, in 2016, around 21 percent of youth (aged 15-19) worked part-time jobs while studying full-time.
Our analysis of different Good Cycles datasets revealed a number of insights about the impacts of Good Cycles work highlighting the potential of social enterprise to empower young people and address disadvantage. Firstly, our analysis showed that while the vast majority of Good Cycles trainees had previously experienced multiple barriers to employment, their participation in the Transitional Employment Program had led to measurable improvements in three important life skills; communication, teamwork and problem solving. Our analysis of the organisation’s trainee journey data showed that the use of bicycles rather than cars had resulted in less congestion, improved health and reduced emissions across the City of Greater Melbourne. Participation in the Data for Good Collaboration enabled the Good Cycles team to see how to harness the power of organisational data to communicate the different impacts of their work while also creating opportunities for data sharing and ongoing collaboration within the organisation.