When you’re presenting data, it is important to put it into context, so the reader understands the story you are trying to tell. Explain the circumstances surrounding the data to shed light on what they represent. Only then will you be able to turn facts into meaningful information that enables positive decision-making at your organisation.
Here we explore how to place your data in context.
How to give your data context?
You may discover, for example, that 20% of people who used your service went on to paid employment. But how do you know whether that percentage is good, average, or poor? A simple step is to talk to colleagues, service users and others about what ‘good’ might look like for your organisation, and consider your results against this.
Even better, find or collect data with which you can compare your findings. There are two main types of comparison:
1. Using a baseline:
This means comparison over time. A common approach is collecting data before someone uses a service and afterwards, to see if there is a change. Baselining can also be used at an institutional level; for example, to show if your results are improving across the whole organisation over time.
2. Using a benchmark:
There are two main ways you can use benchmarking to compare your data:
- Comparing different groups of users (internal benchmarking): Comparing different user groups within your data can reveal insights about how they respond; for example, different age groups may respond differently. You can follow up with qualitative research to better understand these differences. The range of experiences and outcomes can be illuminating, so you could look at how individual users change as well as how groups of users change.
- Comparing to other sources of data (external benchmarking): For example, The UK Data Service publishes government data on outcomes relevant to the charity sector on topics such as wellbeing, school results, and reoffending rates. This can be used to put your work into context. Remember, your users may not always be comparable to the national average. For example, a charity that runs programmes with students at risk of being excluded may have an average exclusion rate that is much higher than the national average.