If you are wondering where to start with data collection, you are not alone. In our experience, the most useful information usually falls into one of these five types of data.

• User data: Is your service effective at reaching the intended target group?
• Engagement data: How effective is your service at continuing to engage your target service users?
• Feedback data: What do people think about the service?
• Outcomes data: How have people been influenced or helped by your service in the short-term?
• Impact data: Have the outcomes achieved (above) helped people to change their lives for the better?

Together, these five types of data help you build a clearer picture of who you reach, how people experience your service, and what changes your work helps create. You do not need to collect all five types to manage impact well.

For most organisations, strong user, engagement, and feedback data provides a solid foundation for learning and improvement. Outcomes data adds valuable insight into short-term change, while impact data should only be collected when it is realistic, proportionate, and meaningful.

By collecting the right data at the right time and bringing different types together, you can generate richer insights, improve your services, and better understand the difference your work makes.

The five types of data

So, what are these five types of data? Below is a summary of what each type covers, along with some key points to consider.

Please note: this is a summary resource. Our guide Understanding Impact goes into more detail on why each type of data matters, key considerations, and approaches to data collection. It is the next resource we recommend after this introduction.

User data

What it is: information about the characteristics of the people who use or sign up to your service, including:

• Demographic data: gender, age, ethnicity, educational level, housing status, income.
• Attitudinal data: current attitudes and aspirations.

How to collect it: user data is usually collected early in your relationship with people, ideally at referral or sign-up.

Why it matters: it helps you understand whether you are reaching the right people, giving you an early indication of success.

Things to consider:

• collecting user data can take time
• think carefully about why you are asking for each type of demographic information. For example, you should only ask about someone’s disability status if it is relevant. Collect only what you need, explain why you are asking, and make sure people feel comfortable opting out.

Engagement data

What it is: data about how people use your services and whether you are effectively retaining and engaging them. This includes:

• Objective data: frequency, timing, duration of engagement, and the mix of activities people take part in.
• Subjective data: the quality of engagement—how people respond, whether they are actively participating or just present, and the kind of relationship you establish with them.

How to collect it: engagement data usually relies on staff or volunteers making observations during or shortly after interactions with service users.

Why it matters: engagement data gives an early indication of success. People need to engage with your work for it to have any chance of making a difference. If engagement is lower than expected, something may need attention.

Things to consider:

• Subjective observations can vary between staff. Simple prompts or checklists can help ensure consistency.
• Low engagement is a signal to explore what might not be working. Do you have the right mechanisms in place to respond?

Feedback data

What it is: feedback data captures what people think about your service and whether it is experienced as intended. It also gives people a voice and allows them to share their views in their own words.

How to collect it: feedback can be gathered from:

• Service users
• Staff and volunteers
• Other stakeholders
• Nonusers or people who drop out (these groups can provide key insights on how to improve)

Collecting representative feedback takes effort, especially from people who do not usually volunteer their views. Short, relevant, and accessible feedback tools can help increase participation.

Why it matters: feedback helps you understand whether your service is being delivered as intended and highlights areas for improvement.

Things to consider:

• Feedback often comes from those who feel very positive or very negative. Aim to reach those in the middle.
• Always ‘close the loop’: let people know how their feedback has been used and what changes have been made as a result.

What happens when you combine these types of data?

A brief pause to reflect- gathering user, engagement, and feedback data is already a strong start.

But having a system that lets you analyse these types of data together is even more powerful. Pairing data helps you understand not just what is happening, but why. Bringing different types of data together adds context and nuance, helping you better understand how what you deliver is experienced by different people.

For example:

Outcomes data

What it is:

Outcomes data show how people have been influenced or helped in the short-term. Outcomes are changes in:

• Attributes: knowledge, attitudes, behaviours
• Resources/assets: for example, income, shelter

These are changes people retain within a short time of your service (for example, days, weeks or months).

How to collect it:

Collecting outcomes data often involves follow-up questions, surveys, or conversations. It may require reaching people who are no longer in regular contact with you. Methods should be chosen carefully to capture internal or less visible changes.

Why it matters:

Outcomes data show whether you are making a difference. If your theory of change is backed by strong evidence linking outcomes to long-term impact, demonstrating outcomes can give confidence that people may go on to experience positive long-term change. Funders and donors are often especially interested in this.

Things to consider:

Outcomes data is harder to collect than user, engagement, or feedback data. People may not realise how they have changed or may not want to share it. Participation can be low. Choose methods that are proportionate and sensitive to people’s time and circumstances.

Impact data

What it is:

Impact data tells you what lasting or sustained change has occurred for people.

How to collect it:

Impact measurement may involve long-term follow-up, comparison groups, or drawing on existing research. Some organisations can use external evidence; others may need to gather their own data.

Why it matters:

Impact data helps you understand the long-term difference your service contributes to. It can demonstrate how short-term outcomes translate into meaningful life changes.

Things to consider:

True impact measurement is complex. Impacts are long-term changes influenced by many factors beyond your intervention. Collect impact data only when it is realistic and proportionate. For many organisations, strong user, engagement, feedback, and outcomes data already provides a robust picture of effectiveness.

Summary

Ultimately, collecting and using data well is about learning:

• who you reach
• how people experience your service
• what changes you help bring about

The five types of data: user, engagement, feedback, outcomes, and impact, offer a practical framework for building that understanding in a structured and proportionate way. Not every organisation needs to collect all five, and not every project requires the same level of depth. What matters most is choosing the types of data that genuinely help you improve your work and understand the difference you make.

By focusing on the essentials, collecting data ethically and purposefully, and bringing different data types together in analysis, you can generate richer insights and stronger evidence. This supports better decision-making, more responsive services, and a clearer picture of the value your organisation creates. Data is not an end in itself. It is a tool to help you deliver the best possible outcomes for the people and communities you serve.

Data is not an end in itself. It is a tool to help you deliver the best possible outcomes for the people and communities you serve.