Two simple ways charities can get more from their data

Many charities collect data, but not all of them feel confident about how to use it. Reviewing responses to NPC’s data diagnostic tool, I saw two practical opportunities for organisations that want to strengthen their learning: make better use of existing evidence and make more of remote data collection. Neither has to be complicated, but both work best when they start with a clear question about what you need to learn.

At NPC, we want to help organisations make the most of the data they already collect. Used well, data can strengthen decision-making, improve services, and support learning. But knowing where to start, and what will be most useful, can be difficult.

That is why we created our data diagnostic tool, a short questionnaire that produces a tailored report on what data to collect and how to collect it.

We recently reviewed responses to the tool to better understand who is using it and where organisations might have opportunities to strengthen their measurement practice. So what did we learn?

What did we learn?

Looking at responses from 770 organisations:

• 22% felt there was little or no research showing that their approach works
• 81% had email addresses for the people they worked with
• 73% had phone numbers for the people they worked with

Taken together, these findings point to two clear opportunities:

1. Making better use of existing evidence
2. Making more of remote data collection methods (such as phone, email, or text)

Let’s look at two practical ways charities can make the most of these opportunities.

1. Making better use of existing evidence

Looking at existing evidence is often an overlooked first step when planning data collection. It can help organisations avoid duplication and focus their efforts where new learning is most needed, rather than spending precious time and resources reinventing the wheel.

Encouragingly, most respondents recognised that some evidence already exists, with 78% believing there was at least some research supporting their approach. The key question, however, is how this evidence is being used to shape evaluation and learning.

A useful starting point is to explore how your work relates to what is already known. Where does your delivery align with established approaches, and where does it differ? You might be testing a proven model in a new context, adapting it for a different target group, or combining different delivery elements in a new and interesting way. Identifying these distinctions can help you focus your data collection on the specific gaps your work is best placed to fill.

For example, if you support people to become more physically active, there is little value in trying to demonstrate that physical activity improves well-being in general. A more useful question might be whether your specific approach leads to sustained increases in activity, or whether participants experience particular wellbeing benefits, such as improved physical health, confidence, or self-esteem.

There are many sources of external evidence to draw on, depending on your area of work. These include academic databases such as Google Scholar and JSTOR, alongside sector specific resources such as the Department for Work and Pensions (DWP) Employment Data Lab and the What Works Centre for Children & Families. The important thing is to start with a focused question, then look for evidence that can help you answer it.

2. Making more of remote data collection methods

The results also suggest opportunities for collecting data remotely. With most organisations holding email addresses and phone numbers, remote methods can play a central role in Monitoring, Evaluation, and Learning (MEL) practice.

Remote data collection can save time and resources, reduce reliance on in‑person activity, and make it easier to follow up after delivery has ended. But it works best when it is designed with service users’ circumstances in mind.

A key first step is understanding digital access and preferences. Some people, such as professionals working at a desk, may prefer responding to emails. Others may be more likely to engage by phone or text. Those with smartphones may welcome a survey link, while others may prefer a call or paper option. Being intentional about these choices can improve accessibility and response rates.

One common challenge with remote data collection is engagement. Most of us who have been involved in data collection know the feeling of sending out surveys and receiving few responses! In our experience, timing and context matter: collecting feedback while participants are already engaged, for example, at the end of a session, can significantly boost completion.

Practical tactics include using QR codes, sharing links in online chat spaces, or setting aside time during sessions to respond together. If data cannot be collected immediately, for example when following up post-delivery to measure longer term outcomes, it is important to consider how relevant your work will remain for participants and how you can keep in touch in a way that feels proportionate.

What should you prioritise?

Whichever data methods you use, prioritisation is key. Collecting a small number of carefully considered datapoints consistently will likely lead to more useful insight than trying to collect too much at once.

This starts with clarity about what you want to learn and why. Mapping out your theory of change can help identify where your most important knowledge gaps sit (e.g. measuring key outcomes or testing underpinning assumptions). Our five types of data framework can also help you map out your current data collection to spot gaps and duplication and prioritise the most appropriate data to collect next.

If you want to strengthen your approach to learning and measurement, start by using NPC’s data diagnostic tool. It can help you reflect on what you already know, what you still need to learn, and which data collection methods are most likely to help.