Data-driven decision-making (DDDM) has emerged as a crucial tool for measuring and enhancing social impact but not only. Within the Proximity and Social Economy ecosystem, there has been an extensive discussion about open data sharing and its benefits. More precisely, to ensure that shared data is not just available but actively used to generate meaningful insights, drive strategic actions, and optimise impact. There has been indicated the benefits of it such as: Enhanced Collaboration, Increased Knowledge and Innovation or Better Resource Allocation among other 1.  

Social impact has long been a key concern for social economy organisations, as it reflects the long-term effects of initiatives aimed at improving societal well-being. Measuring this impact is challenging, as it involves both qualitative and quantitative factors, including intangible elements that are difficult to quantify.

Effective social impact measurement is closely linked to resource allocation, identifying areas for improvement, and maximising overall effectiveness. By analysing data, organisations can transform intangible actions into empirical evidence that clearly demonstrates their impact. However, the reliability of these insights depends entirely on the quality of the data collected. As the saying goes, “garbage in, garbage out”—the accuracy of the analysis is only as good as the data used. Therefore, ensuring robust data collection methods over time is essential for meaningful impact assessment.

And there is more than that: namely challenges of shared data2.  Many Social Economy organisations face significant barriers that hinder their ability to harness the full potential of data. One of the most pressing challenges is lack of connectivity, particularly in remote and rural areas. Without reliable internet access, organisations struggle to share and access critical information, limiting their ability to engage in data-driven decision-making. Beyond connectivity, many organisations lack the necessary data infrastructure to collect, store, and manage information efficiently. Without proper systems in place, valuable insights remain untapped, and the potential for collaboration is stifled. Even when data is available, limited digital literacy among stakeholders can create additional roadblocks.  Another major concern is vendor lock-ins—situations where organisations enter contracts with technology providers that restrict their control over data. When locked into specific platforms, organisations may struggle with interoperability, data migration, and long-term sustainability, making it difficult to adapt to evolving needs. At the heart of these challenges lies the issue of trust and governance. Establishing clear, transparent governance models for data sharing is essential, particularly when multiple stakeholders where diverse interests are involved.  

Data analysis is all about identifying patterns and trends. Training yourself to recognise these patterns—whether in financial reports, beneficiary behaviour, or even daily routines—can help to make sense of complex information. The more practice spotting connections, the more naturally data-driven thinking will become 3. Relying solely on intuition or past experiences can sometimes lead to biased or ineffective decisions. Instead, make it a habit to back up one's choices with data. Before deciding, it's crucial to ask yourself: What information do I have to support this? If no data is available, it’s important to explore ways to collect and analyse relevant insights before moving forward.

For more information about Digital and Data-Driven Solutions empower social economy SMEs within the Proximity and Social Economy (PSE) sector to innovate, scale, and maximise their social impact visit the DO Impact project.  

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