Compare your data

How can you compare your data to understand it in a wider context?

Like any form of storytelling, data needs context. You must understand the circumstances surrounding your numbers to shed light on what they represent, so you can interpret them. Only then will you be able to turn facts into meaningful information that facilitates 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 that 20% of people who used your service went on to paid employment. But how do you know whether 20% 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 that you can compare yourself to. 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:

This means comparison with similar data from other sources.

  • 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 users change.
  • Comparing to other sources of data (external benchmarking) The UK Data Service publishes government data on outcomes relevant to the charity sector; for example, 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.

If making comparisons, you can make your findings more robust by:

  • Being consistent in your data collection methods: This is relatively easy with internal baselines and benchmarks. Consistency is harder to achieve with external benchmarks, unless you are working together on a shared approach to measurement.
  • Extending your view: For baselines, look at change over a longer time period, and with more than two data collection points to give greater context. For benchmarks, you could make comparisons with more than one organisation, or, if possible, with an average from your sector.
  • Triangulate: Combine your findings with other sources of information like feedback from service users and qualitative research to check whether you are getting a consistent message.

Still confused?

The data diagnostic asks 10 multiple choice questions about what your programme or service is, how it works, and who it targets. It then provides a tailored report recommending what kind of data you should consider collecting and how.

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Quantitative data

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Qualitative data

How can you analyse qualitative data to understand the reasons for change?

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