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Making good use of data
Data is everywhere – in project reports, survey results, financial spreadsheets and field observations. But data alone does not bring about change. It is only when it is properly analysed, interpreted and translated into concrete decisions that it reveals its true value. Particularly in humanitarian work, where resources are scarce and people’s needs are great, the sensible use of data can make all the difference – between a project that really helps and one that misses the mark when it comes to actual needs. Those who not only collect data but also understand and use it make better decisions, work more efficiently and can convincingly demonstrate the impact of their work.
Why data is so important in project work
Without reliable data, an organisation is left in the dark. It does not know for certain whether its measures are effective, whether the target groups are actually being reached, or whether resources are being allocated to the right areas. Decisions are then made primarily on the basis of gut feeling and assumptions – which works in some situations, but is not a reliable foundation for sustainable project work.
Data provides clarity. It shows what is actually happening – not just what should be happening. It highlights progress, uncovers problems and provides the basis for well-informed adjustments. In a world where funding bodies are increasingly demanding evidence of impact and supporters want to know what their donations are achieving, reliable data is also an important tool for communicating with the outside world.
At the same time, the approach to data should remain realistic. Not every organisation has the capacity for extensive data collection or complex analyses. What matters is not the quantity of data collected, but its quality and relevance. A few, well-chosen indicators often provide more guidance than a glut of figures that nobody really analyses.
Collecting, analysing and using data
The path from data collection to actual use is shorter than many people think – provided it is taken into account from the outset.
Collecting the right data
Before data is collected, it must be clear what questions it is intended to answer. What do we want to know? What do we need to know in order to make good decisions? And how can we collect this information in a way that is realistic and reliable?
The choice of appropriate research methods depends on the context. Surveys, observations, focus groups and the analysis of existing data are the most common tools. Each method has its strengths and weaknesses – it is important that the chosen method is appropriate to the question and that the data collected can actually be interpreted. For example, if you want to know how children experience an educational programme, simply counting the number of participants will not get you very far. In such cases, qualitative methods that delve deeper are required.
Particularly when working with vulnerable groups – such as children living in poverty – ethical principles must be strictly adhered to during data collection. Data protection, informed consent and the protection of personal information are not merely bureaucratic requirements, but a matter of respect for the people concerned.
Analysing and interpreting data
Raw data is not particularly meaningful at first. It is only through analysis and interpretation that it becomes useful knowledge. This means putting figures into context, recognising patterns, establishing connections and drawing conclusions that are relevant to the project work.
It is important not to view data in isolation. A drop in participant numbers for a programme can have many causes – ranging from seasonal factors and changes in the target group’s circumstances to quality issues within the programme itself. Anyone who jumps to conclusions without taking the context into account risks making the wrong decisions. A good data analysis always asks: What explains this result? And what does it mean for our next steps?
The following steps will help you analyse data in a structured way:
- Clean the data and check it for completeness before starting the analysis
- Visualise results, for example in simple charts or tables that can be understood at a glance
- Discuss the results of the analysis as a team and take different perspectives into account
- Set out conclusions in writing and link them to specific recommendations for action
Data as the basis for learning processes
The greatest benefit of data lies not in its collection, but in what an organisation does with it. Organisations that systematically use data to reflect on and improve their work learn more quickly and perform better. They identify what works and build on it. They identify what does not work and make adjustments – before any more resources are wasted.
This learning process requires a culture in which data is shared openly and discussed honestly. If findings that do not fit the desired narrative are swept under the carpet, data analysis loses its purpose. Sustainability in social projects arises precisely where organisations have the courage to look at uncomfortable data – and to draw the right conclusions from it.
Using data effectively is ultimately a question of attitude: the willingness to be guided by facts, rather than twisting them to fit a preconceived opinion. Those who take this to heart make better decisions – and thereby do a better job for the people who depend on that work.