Principle 9
Leverage the power of data
Leveraging data in philanthropy is crucial for effective decision-making, measuring social impact. Despite its importance, using data effectively, responsibly, and pragmatically has often been challenging for philanthropic organisations. Issues include the lack of seeing data as an asset, standardisation, consistency, and quality in data collection techniques, as well as data protection and cybersecurity risks. Foundations should commit to developing feasible data collection strategies, internal capacities for analysis and a data-driven culture, and contribute to data commons and other collective intelligence efforts. View this as a journey toward building capabilities and learning over time. Additionally, organisations should collaborate with grantees and local stakeholders to capture real human stories as part of the data collection process. Consider how data is used for learning, measurement and decision-making, but also that it can be an impactful intervention or solution in itself. By doing so, organisations can allocate resources efficiently and strategically to address the most pressing social issues and focus on effective strategies to achieve positive social outcomes.
Data – defined for our purposes as information, facts, stories and statistics – should consistently be a powerful resource for philanthropic organisations. Advances in digital technologies, such as the development of powerful new algorithms or communication platforms, are creating greater opportunities to leverage data (where it is available or gatherable) for evidence-based philanthropy. Using data responsibly has therefore become a crucial principle for philanthropy to be effective and impactful, particularly because high-quality data informs and improves decision-making and impact measurement. Thus, informed use of programmatic data can support efforts to maximise the effectiveness of philanthropic activities. By using data and feedback to identify the most pressing social issues and ways to address them, organisations can allocate resources efficiently and strategically. Furthermore, data supports organisations in measuring their progress and success, learning from their successes and mistakes, and making informed decisions that lead to sustainable social impact. Beyond this, philanthropic organisations should consider forming data sharing partnerships with like-minded funders and grantees to build collective intelligence, and use data effectively for storytelling.
Leveraging the power of data requires embracing a more evidence-based approach to philanthropy. It involves integrating good quality and robust data collection into your organisation’s decision-making process, ensuring that philanthropic activities are grounded in data-driven insights. Using data allows philanthropy organisations to identify and target their resources towards the areas of greatest need, and focus on the most effective strategies to address social problems. At the same time, data collection can in itself be overwhelming, and even after an intensive process it may be that there is insufficient data to provide a complete understanding of an issue. We must work together to prioritise key questions to be answered to ensure we are collecting data that is relevant, useful and serves our stakeholder communities. These questions include:
1· Who is the data serving?
2· What kind of data are we collecting (qualitative and quantitative) and for what purpose?
3· Who owns and who contributes to this data?
4· Is it living knowledge and data?
5· Is it publicly and easily accessible and shareable?
These are important questions to reflect on as they can have an outsized impact on data strategies and infrastructure that may need to be developed going forward.
While the importance of leveraging data is widely recognised in theory, in practice using data effectively and responsibly has presented a major challenge for the philanthropy sector. For some smaller and medium-sized philanthropic organisations, collecting data, analysing it, and using it to inform change can be a long and resource-intensive process. Larger organisations, on the other hand, may have greater resources, but still grapple with the legacy systems or complexities of gathering, managing and analysing suitable data to assess their programmes – especially when it comes to making impact-centred assessments over longer durations. Furthermore, the burden of data collection can fall on partners and grantees with limited support or insufficient time and funding to do it well. Additionally, some may argue that over-reliance on data brings risks, particularly if it leads us to overlook the important role of intuition, lived experiences, and human judgement in philanthropy. Organisations should embrace ‘human-in-the-loop’ approaches, where data is used to augment human judgement in decision-making, rather than replace it completely.
One of the main challenges of leveraging data is the lack of standardisation, consistency, multi-national equivalency, and quality when it comes to data collection techniques, which can make it extremely challenging to compare and analyse data effectively across organisations and the sector. This is further compounded by the complexities of defining and measuring social impact, or the difficulty of collecting data in certain geographical, social or cultural contexts. Additionally, should the data be digitised, data collection increases the organisation’s exposure to data protection and cybersecurity risks. As discussed in Principle 1, organisations may hold sensitive data about vulnerable communities or organisations, and data breaches would pose significant risk of harm. These risks may create tensions with the need for data-sharing with collaborative partners, or for accountability and transparency to stakeholders.
Beyond using data as a means for evidence-based decision making and impact measurement, it can also be used as a potential solution or intervention itself in some circumstances. For example, a project funded by Vodafone Foundation and the William and Flora Hewlett Foundation in Ghana works in collaboration with Flowminder and the Ghana Statistical Service. By analysing anonymised and aggregated mobile data, the programme generated valuable insights to help the government track epidemics and prevent widespread outbreaks.
Organisations seeking to maximise the use of data must commit time and resources towards developing not only a feasible data collection strategy, but also policies and capabilities around data management, storage, and analysis. This will include providing training for staff to develop new data literacy capabilities, and investing in systems that can expand capacity. Crucially, the human element must be maintained at appropriate points – data collection, for instance, should be done in collaboration with grantees and local stakeholders in order to capture the real human stories at the crux of the work.
Recommended practices for implementation
How to get started:
- Establish what your organisation has done to date in terms of data gathering.
- This involves monitoring and evaluation processes, conversations with community stakeholders, impact assessments, or post-funding reports.
- Embrace creating a data strategy as a journey.
- Define an organisational aspiration and develop a roadmap that builds up from what you are realistically able to do now based on your current data collection models (if any).
- Identify key priority areas where data can have the most impact and focus on how to collect and store data in a manner consistent with your values, the change you seek to create and regulations.
- If your foundation lacks the internal capacity to pursue data collection, collaborate with partner organisations or data experts to leverage existing data structures.
- Dialog with and learn from peer organisations in philanthropy.
- Consider joining a data collective for data sharing amongst allies.
- Evaluate outcomes comprehensively, ensuring consideration of programme-wide impact over project-specific interventions.
- Tell compelling, human-centric stories and tangible narratives of impact to expand reach, increase empathy and inspire support.
- Lead by example in sharing positive stories that might not otherwise be heard.
- Commit to continuously increasing transparency and accountability through data and evidence.
- Invest in building the organisation’s data capacity by training staff about data collection and use and raising awareness of their role in these processes.
- Ensure that data collection does not become an extractive or burdensome process for partners and grantees.
To go beyond:
- Design and build philanthropy interventions based on data analysis.
- Use evidence-based approaches to identify specific market needs and gaps before intervening.
- Enhance monitoring, evaluation and learning methodologies to move from output to impact-based assessment.
- Collaborate on research with other like-minded organisations to develop and enhance data-driven monitoring, evaluation and learning methodologies
- Consider pooling funds with other philanthropic peers to focus on data points that can create collective impact in a specific geography or thematic area.
- Share these stories of collective impact within a specific geography or thematic area.
- Manage data as a valuable asset by investing in organisational capabilities to effectively collect, store, share and use data responsibly.
- Build better practices for data quality, completeness, accountability and protection of sensitive information, especially for vulnerable constituents.
- Apply a strategic perspective that prioritises long-term impact, beyond only immediate results, using data to provide visibility.
- Consider other ways on how data can not only be an enabler for measurement and decision-making, but also as an intervention or solution itself to drive impact.
- Consider investing in the digital infrastructures and skills your grantees, partners, and their communities need to achieve digital equity so that all data can be represented.
- Invest in digital and data infrastructure, and in organisations that develop data.
Potential obstacles
Suggested solutions
Data collection is often a long, time and resource intensive process which is discouraging for philanthropic organisations that must produce regular upward reporting.
Cultures around robust data collection methods in philanthropy are steadily improving. Use examples of best practice to demonstrate to stakeholders that patience is key. Consider how to combine different methods of data collection, such as surveys, interviews, existing data sets, and observations. Organisations like TechSoup, 360 Giving, Feedback Labs and DataKind can also help foundations organise and leverage their data.
Collecting data on programmes may reveal that a long-running philanthropic intervention is not nearly as impactful as anticipated, damaging reputational clout and future funding opportunities.
As explored in Principle 2, embracing failure and unexpected results should be an integral part of your organisation’s strategy. There will inevitably be times when the data demonstrates that an intervention was less effective than planned, but this serves as an opportunity to refine and improve the programme. It reinforces philanthropy’s role as a risk-taker and enables organisations to embody this role fully.
Low-resource organisations will be unlikely to have data-collection expertise or analysts on hand to provide the needed support.
Where possible, organisations can hire fixed-term personnel to support data-collection activities from specialist organisations, use existing available data sets, or enrol in a number of training programmes. Barring this, organisations can pool resources with peers in the space to bring in external researchers or reach out to internal experts within the network.