The Data-Driven Revolution: Philanthropy’s path to maximising impact
We are witnessing a rapid transformation within the philanthropic sector regarding its interests and aspirations in harnessing the potential of data for strategic decision-making and day-to-day operations. Not too long ago, engaging philanthropic organisations in discussions about adopting a more data-centric approach often yielded the same response: Other priorities take precedence at this time. However, the landscape has evolved, with an increasing number of management teams and boards now yearning for data-driven insights. The motivations behind this shift are diverse, yet frequently encompass a growing thirst for continual improvement mixed with increasing external demands and expectations from stakeholders and society at large.
A data-driven approach offers substantial benefits for philanthropic organisations:
- Enhanced Impact: Data-driven measurement with clear KPIs allows organisations to align progress with strategic goals, ensuring meaningful change and impact maximisation.
- Informed Decision-Making: Data-based decision-making replaces intuition with evidence, promoting equitable choices. Real-time analytics and reporting align activities with plans.
- Transparency and Bias Mitigation: Transparency in funding decisions is essential for accountability. It mitigates biases and ensures fair resource distribution, improving awareness and visibility among partners and applicants.
Below we highlight common challenges philanthropic foundations face during their journey towards data-driven transformation and offer advice on overcoming these obstacles. We support our guidance with examples from our work with philanthropic organisations that have achieved significant outcomes through data-driven approaches.
The points we present are by no means exhaustive; however, they delineate several crucial components within a roadmap towards becoming data-driven.
Addressing obstacles: Strategies for success
The journey to becoming a data-driven philanthropic organisation entails a profound transformation, that transcends data accumulation alone. It demands the conversion of raw information into structured, systematic data that can be effectively gathered, analysed, visualised, and shared. This transformation is the foundation upon which informed action and decision-making thrive.
Mapping and consolidating data sources
The data landscape within philanthropic organisations is often intricate and fragmented. Data sources are scattered across teams or individuals, and establishing responsibility for data collection, structuring, and analysis can be elusive. Bringing together a diverse group of employees for data-mapping sessions can be an efficient first step. These sessions should aim to map all relevant data located in various parts of the organisation and identify the employees responsible for collecting and storing the data. Once this comprehensive overview is obtained, philanthropic organisations are one step closer to creating a data warehouse, a critical tool that consolidates all data in one place. While it requires some technical development to transform raw data into an analysable format, it simplifies subsequent steps towards data-driven decision-making. Additionally, a data warehouse can be linked to external data sources through an API, enabling the analysis of data not collected by the organisation and facilitating cross-analysis with their own data. This opens numerous opportunities. For example, some organisations have expanded their knowledge about applicants significantly by collecting new information on research outcomes, organisational data, geographic locations, and more from existing public databases, without needing to request this information from applicants. Moreover, a data warehouse facilitates the development of automatically updated dashboards and real-time analytics, providing immediate insights.
Enhancing data quality
Addressing data quality is essential. Data quality and consistency often pose significant challenges, especially as philanthropic organisations undergo strategic shifts or make changes to online application and reporting systems, leading to data inconsistencies. Additionally, data may be stored in formats that hinder easy access or extraction, such as PDF files, limiting its usefulness for decision-making. It is crucial to continually assess and improve data quality, starting with data collection procedures. When partners or grant recipients report data, clarity regarding the format and purpose of the information becomes paramount. Transitioning from analogue to digital data collection methods provides philanthropic organisations with easier access to their collected knowledge, simplifying aggregations and cross-analyses. In some of our collaborations, enhancing data quality has uncovered insightful patterns in grant-making data that were previously obscured due to poor data quality.
Creating effective data asset management
Creating clear data governance, collecting data in a data warehouse, integrating APIs, and improving data quality are all steps in establishing the discipline called data architecture. Data architecture encompasses the structured design and organisation of an organisation’s data, covering storage, processing, and access methods. A robust data architecture is essential for harnessing data’s potential through advanced technologies like artificial intelligence, enabling insights from complex datasets, including extensive textual data.
Data architectures can vary depending on an organisation’s goals. Philanthropic organisations may lack the resources and expertise for an extensive setup. In such cases, starting with simpler solutions and focusing on accessible data sources conducive to analysis is a wise approach. One of the key benefits of establishing a data architecture is the ability to effectively manage all of an organisation’s data assets, aligning them with organisational objectives. This ensures that efforts to develop data maturity are directed towards the organisation’s most critical areas, optimising time and energy.
Cultivating a data-driven culture
Fostering a data-driven culture goes beyond data access; it necessitates a fundamental mindset and practice shift. It requires an organisational commitment to base decisions on data-driven insights instead of subjective perspectives, emotions, or assumptions. This shift entails engaging in challenging internal discussions about how data insights will guide actions. Trust in data quality is paramount, and any doubts about data accuracy should trigger a re-evaluation of data sources and methods. This challenge is significant for philanthropic and other organisations, and it takes time to address. We find that employee engagement and involvement are crucial for achieving more data-driven practices. Implementing training and knowledge-sharing sessions to showcase data’s potential can be beneficial, sparking interest and a sense of ownership among employees. Other initiatives to promote a data-driven culture include pilot projects, educational efforts, experimental initiatives, employee feedback mechanisms, and external inspiration and benchmarking.
In conclusion, the journey towards data-driven philanthropy is a multifaceted endeavour. By recognising and strategically addressing these challenges with commitment, philanthropic organisations can unlock the full potential of data, ultimately contributing to a more equitable and impactful philanthropic sector. Philea is gearing up to publish a study in collaboration with Compagnia di San Paolo exploring the use of data in foundations and sharing insights that promise to reshape the way we approach data in the philanthropic sector.