Analysis is the process of turning your raw data into meaningful insights. Depending on the complexity of your project this could be a big or small task, and there is no right or wrong way to go about it. The aim of analysis is to make sense of the data by looking for patterns and themes that emerged through your research. Refer to your research objectives and analyse the extent to which your observations answer the key research questions.
Aim to do your analysis as soon as possible after the sessions so the information is still fresh in your mind. It can be helpful to involve anyone that observed the research to gather group consensus and to ensure you eliminate researcher bias, that is, only focusing on what you think is important.
Begin your analysis by grouping your raw data into common themes using a method known as affinity mapping. This is often performed using post it notes and pens, but it can also be done digitally using a variety of online tools. Once you have your main themes, the next step is to determine what the findings mean. Think about why you think each data theme is important and what it really means.
Depending on your research objectives, you may need to calculate task completion scores and other metrics you captured such as the system usability score (SUS). If you captured SUS during your sessions this calculator will help you estimate where your service sits on the system usability scale (SUS).
Document your findings
After you analyse what happened, record and document your findings. How you choose to document your findings will depend on your specific team and requirements, as well as the individual project. Where appropriate, prioritise each finding or recommendation, so that it’s clear at a glance which are critical and which are minor. Be sure to include information on your research methods, who you spoke to, and how you collected the information, as this adds credibility and trust in your insights.
Reviewed 14 November 2019