Why do we analyse?

Analysis is the process of using various tools and techniques to organise and interpret data so that you can gain useful insights.

Analysis is quite often the bit people ‘forget’ they have to do. It can be easy to think that the hard work is finished after all the data has been gathered, but without analysis, data is just information that does not really tell you anything. The insights you’re looking for are there, but without analysis you will not be able to find the important information in all the mess.

We analyse data to find actionable insights. Teams need actionable insights from user research because only actionable insights make the next steps clear.

Involving the team

Research is a team sport, and so is analysis.

Working together on data analysis helps teams to make sense of the user research sessions they’ve observed and how it fits into the bigger picture. It’s also easier for your team to absorb, understand, and remember the actionable insights if they’ve been part of the analysis process.

Involving the team also helps to reduce confirmation bias. Confirmation bias occurs when someone has an assumption and sees some data that appears to match their assumption, so they just accept their assumption must be true and move on, perhaps at the expense of more detailed analysis.

When more people are involved, it’s less likely one person’s interpretation of the data will influence the findings and insights.

A common method of data analysis in user research is affinity mapping, which is a collaborative process that works very well in person. We will learn more about this in step 3.11.

Collaborative analysing is a little trickier to do online, as conversations are less organic, but it is still possible and very helpful to do.

Fundamentally, whatever type of data and whatever method of analysis you choose, working collaboratively with your team is the best approach.

Quantitative and qualitative data

As we have already learned, user research methods are divided into quantitative and qualitative. The method you choose will dictate the type of data you will have to analyse.

Quantitative data is based on numbers or statistics. You measure or count something to generate quantitative data. It is the kind of data that answers questions such as:

• how many users do this?
• how often do users do that?

Qualitative data helps us to understand what happens and why. You generate qualitative data by using observational or conversational research methods, such as interviews and focus groups. Qualitative data answers questions such as:

• why do users do this?
• how do users feel when this happens?

As different methods generate different types of data, you need to use different methods of analysis. We will learn more about this in the next steps.

Task

Do you have experience of working with any kind of data? Do you find it easier or more difficult working with different types?

Share your thoughts or experiences with your fellow learners in the comments below.