We’ve been convening organisations that steward open data as part of our second peer-learning network with Microsoft. As part of the peer learning network, we recently held a roundtable discussion focusing on how to develop a sustainable business model without compromising commitments to open data. In this blogpost, we share reflections from the roundtable alongside worked examples of our Sustainable Data Access Workbook.
Financial sustainability for open data stewards
When an organisation is premised on provisioning free and open access to data, financial sustainability can be a challenge. The difficult topic of funding opens up the question on how to create revenue while maintaining a commitment to being truly ‘open’ (as described on the data spectrum).
There are many different organisations that have managed to make open data financially sustainable. As part of the ODI’s second peer-learning network, in partnership with Microsoft, we hosted a roundtable to explore the variety of business and revenue models organisations utilise to deliver on their open data commitments. The speakers on this roundtable were representatives of MetaBrainz Foundation, Open Apparel Registry and Wikimedia Foundation – three organisations who are part of the peer-learning network.
In this blogpost, we share the key findings from the session related to the expenses, income, challenges of the three organisations, and how this relates to their respective mission. The three organisations have also completed a copy of our Sustainable Data Access Workbook - showcasing their organisational structure, business model, and revenue model. We hope that sharing these findings will help emerging open data organisations solve the challenge these organisations have come across.
What is the Sustainable Data Access Workbook?
The Sustainable Data Access Workbook is an ODI tool providing a practical set of activities for organisations to undertake to make more informed decisions about their current and future revenue models. The workbook is particularly useful for data institutions – organisations that steward data on behalf of others, often towards public, educational or charitable aims. We recommend organisations learn from the worked examples provided in this blogpost, and utilise the workbook to explore different revenue models while maintaining a commitment to open data.
Open Apparel Registry
Open Apparel Registry (OAR) is a free, open data, open-source map and database of global apparel facilities. Data in the Open Apparel Registry is contributed and used by organisations all over the world, including major global brands, civil society organisations, multi-stakeholder initiatives, certification schemes, factory groups and more. As well as many other efficiency and process benefits, the way the OAR organises and presents data ultimately improves the lives of some of the most vulnerable people working in global supply chains. When everyone working within them enjoys equal access to quality data, opportunities rapidly open up to shift the industry onto a more sustainable and equitable path.
OAR exists as a US non-profit, registered in the State of Delaware, pending 501(c)3 status. OAR operates on a mixed revenue model relying primarily on philanthropic funding, while also selling premium services. This revenue model is key to OAR’s mission as it allows them to remain a neutral actor within the industry.
Expenses:
- Maintenance of the technical platform - largest expense lies in developing and maintaining the OAR platform
- Staff cost - small (and growing) team of five full-time employees with key consideration made to stakeholder engagement - allowing OAR to build trusted relationships within the industry necessary for data access
- Data moderation - trust in the quality of data is key for OAR, rather than relying on a volunteer community to maintain data, OAR has internal staff resourced to moderate and maintain data. (Users do report data issues to the OAR team for moderation, and consideration is being given as to how to better tap into this resource as the volume of data in the tool grows)
- Communications and marketing - OAR prioritise storytelling to help people understand how to use data, which in turn helps philanthropists understand the value of OAR - making them a more attractive value proposition to obtain funding
Income:
- A key part of OAR’s mission to exist as a public good to the apparel industry relies on preserving neutrality and maintaining trust with stakeholders within the industry.
- OAR has been very fortunate to receive philanthropic funding from day one, with a further 5 years core funding awarded in 2021, providing stability and allowing them to remain neutral within the industry
- OAR doesn’t want to be reliant on grant funding, and has developed paid-for services, such as their API connector and embedded map plug-in
Challenges:
- Keeping data open and free while generating income - as OAR investigates more technical ways of working with data, users that are accustomed to accessing data for free are reluctant to pay for increased services
- Selling services is costly - although OAR convey that paid-for services are a crucial source of income, the lead times to convert interest into a sale are slow, and the initial setup of connection is costly as there is always work that needs to be done by in-house developer teams on the client side to be ready for integration
MetaBrainz Foundation
The MetaBrainz Foundation (MetaBrainz) is set up to build community maintained databases and make them available in the public domain or under Creative Commons licences. One such database is MusicBrainz, an open music encyclopaedia project to create a collaborative database about artists, songs and albums.
MetaBrainz is a 501(c)(3) tax-exempt non-profit corporation, and operates on a mixed revenue model relying primarily on commercial supporters and also community donations. While not mandated, MetaBrainz instils a moral obligation to financially contribute from large-scale commercial users of the data - allowing the foundation to continue their mission with financial oversight and without the need for inconsistent sources of funding.
Expenses:
- Staff costs - largest expense lies in team of 10 employees across core areas of development and engineering, community engagement, customer services, and administrative support
- Additional costs include overheads (banking fees, power and electricity, and ad-hoc requirements), office payments, and hosting
Income:
- Metabrainz does not invest in marketing, rather focusing on a ‘build it and they will come’ approach in an industry with very few competitors
- The significant sources of income come from user donations and commercial sponsorships - while all non-commercial use of this data is free, MetaBrainz encourages all commercial users to support them
Challenges:
- Initial progress was slow, and building the infrastructure took several years - seven years to develop the database and four years of the foundation existing for large companies to think the data was of sufficient quality to start using and paying for it
Wikimedia Foundation
The Wikimedia Foundation (Wikimedia) is the non-profit organisation dedicated to encouraging the growth, development and distribution of free, multilingual content, and to providing the full content of these wiki-based projects to the public free of charge.
Wikimedia Foundation is a 501(c)(3) registered public charity and operates on a mixed revenue model, relying primarily on reader donations and various forms of philanthropic funding. By relying primarily on the goodwill of individual donors who read Wikipedia, Wikimedia creates accountability to its readers and their knowledge needs.
Expenses:
- Website and application(s) maintenance - largest expense spanning the salaries, hardware, software, and data centre costs associated with maintaining Wikimedia’s offerings.
- Supporting communities - through direct grants and legal support to community members, global chapters, and affiliate organisations actively trying to grow content and participation in the projects
- Staff and organisational costs - administration, HR, recruiting, IT, office, and finance
- Fundraising - expenses associated with annual fundraising campaigns
Income:
- Individual donors - largest component of income generated from average $10-15 donations from readers derived from fundraising campaigns and recurring donations
- Philanthropic funding - from philanthropic organisations, and corporates via CSR programmes
- Endowment - funded primarily by major donors and planned giving
- Recently launched Wikimedia Enterprise, a paid for API/data access service for high volume commercial users of Wikipedia content - primarily driven by fairness and equity, but will be helpful to have incremental source of funding
Challenges:
- Lack of awareness that Wikimedia is a non-profit organisation - attempting to communicate that Wikimedia is a non-profit and requires funding is an ongoing challenge
- Societal and technological shifts - in the last few years the internet landscape has changed and people are relying on different forms of content and new platforms to access information. This may have an impact on on-platform fundraising and requires continuing evolution of the product and diversification of the funding model (e.g., Endowment, Enterprise)
Learn more about becoming a sustainable data institution
This blogpost has showcased three models for financial sustainability from three mature open data institutions. There are many other models and mechanisms to financial sustainability, and the ODI has done previous research with other organisations fulfilling similar roles. For emerging organisations, we recommend reviewing our Designing sustainable data institutions and
Data institutions: reducing costs and improving sustainability reports, and using our Sustainable Data Access Workbook.
If you have any feedback on how to improve this tool to further support emerging open data organisations, please send all feedback to [email protected]