A designerly approach to sense-making
“Yet despite the acknowledged importance of this phase of the design process, there continues to appear something magical about synthesis when encountered in professional practice: because synthesis is frequently performed privately” — Kolko, 2010, p. 15
Steps in sense-making
Clean and "prune" your data
Create preliminary themes
Iterate your themes into findings or observations
Move beyond observations into insights using abductive analysis
Move from insight to recommendation
1. Clean up data, and get it into a stage where you can see it clearly
This might mean organising your data per question or removing unnecessary information. Generally, we put it all up on a wall, or more commonly now, a Miro board.
2. Create preliminary themes
If doing this in Miro use affinity mapping, where you you cluster quotes and observations that are similar. You may want to put one quote or observation in multiple groups, that's fine. In Miro you can duplicate them. You can either write names for the groups as you go, but it can also be interesting to just group them, then name them once you are done. Once you have the names, think about if all the data under that theme still makes sense, you might end up moving some out, or splitting up the themes to be more specific.
3. Iterate your themes into findings or observations
If you are working in Miro, you probably have a bunch of themes, that make sense to you, but wouldn't make sense to anyone else not familiar with the data. The next step is to create some declarative statements that summarise what you have learned from the data.
A declarative statement, as the names suggests declares something For example, "The sky is blue" is a declarative statement because it tells something about the sky without asking a question, giving a command, or expressing strong emotion. It's a finding in the data.
Your declarative statement should be qualified. By this I mean, you can't just state something as fact, you need to qualify it as what it is, which is likely an opinion of some, or all of your research participants. For example "All of the research participants found identified the sky as blue". Now you have a finding or observation. A piece of knowledge
In this process, you may combine themes, or identify that there are actually a few findings in one theme. Feel free to play around here. Not all findings are equally useful, and that's okay at this point, there will be some you don't need to report on for the purposes of your research.
At this stage, you might want to theme your findings. Often what this looks like is a kind of hero finding, which is broader and covers a lot, and then a bunch of sub-findings which go into more detail, or call out more nuance.
4. Move beyond observation into insight
Insights go beyond the obvious observations to reveal underlying reasons why users behave in a certain way or prefer certain solutions. Insights help to bridge the gap between data and actionable decisions.
It can be good here to return to the research objectives. What are we trying to learn here? What is the behaviour, barrier or challenge we are trying to understand?
“finding relationships and patterns between elements, and forcing an external view of things. In all of the methods, it is less important to be “accurate” and more important to give some abstract and tangible form to the ideas, thoughts and reflections. Once externalized, the ideas become “real”—they become something that can be discussed, defined, embraced, or rejected by any number of people, and the ideas become part of a larger process of synthesis.” — Kolko, 2010, p. 18
To get from observation to insight, we need to use what is called abductive analysis. This form of reasoning requires us to take a creative leap and develop a reason why this behaviour might be happening. Abductive analysis is essentially about forming hypotheses based on incomplete or circumstantial evidence. It involves considering various possible explanations and selecting the most plausible one that aligns with what we know. This method is particularly useful in user research and service design, where direct evidence of causation might not be evident, enabling us to bridge gaps in our understanding and innovate solutions effectively.
“Implicit and hidden meanings are uncovered by relating otherwise discrete chunks of data to one another, and positioning these chunks in the context of human behavior.” — Kolko, 2010, p. 19
Insights and observations are not 1:1, again there is some sense-making going on here, where one observation might lead to multiple observations and visa versa.
Here's an example
Observation: Many participants report skipping certain optional fields in online forms when the benefit of providing that information is not clear.
Observation: Some participants were frustrated they had to give personal data to call centre staff before they had asked for any information or assistance.
Insight: People may feel less inclined to share personal information if they are unclear on why it is being asked, and what they will get in return. Giving data needs to be a clear value exchange.
This type of reasoning is the cornerstone of design, it is how we create "design knowledge". Designers will draw on past experiences and sector knowledge to generate these insights. For this reason, it's often thought of as "design intuition", but really it is a form of reasoning. These insights are not designed to represent scientific knowledge that is proved beyond a shadow of a doubt, it is used to help us frame and reframe the challenge we are trying to solve, and find new angles to come up with potential solutions. These solutions will be prototyped and tested, which will generate new insights and new ways to frame the problem. It's an ongoing conversation between problem and solutions
Move from insight to recommendation
There is a difference between a solution and a recommendation. Solutioning, or ideating, is a whole other topic. But, that being said, giving some recommendations to go along with the insight can help make it more tangible. That is, it can be easier to understand the problem by seeing some different ways it could be solved.
Based on our insight from the last section, here are some example recommendations.
Clear Communication of Benefits: Explicitly communicate the reason we need the information, and how it will allow us to help them.
Personalise the Ask: Use the information you already have to tailor the request for further data. Showing users that you know them well enough to not ask for information redundantly can build trust and relevance.
Incremental Disclosure: Request information in stages, asking for more personal details only when necessary.
I like to add a "This might look" under each recommendation. This keeps the solution possibility space open, while making the recommendations more tangible.
For example
Clear Communication of Benefits: Explicitly communicate reason we need the information, and how it will allow us to help them.
This might look like:
Explaining the data we collect and why during hold time
Providing context on data being asked on tool tips in forms
Prefacing asking for personal information by explaining how it helps with funding and improving programs
Thematic analysis using QDA
If you are analysing the data in a Qualitative Data Analysis tool (QDA) like NVivo or Dovetail, it is a somewhat similar, but more rigorous and time consuming process. For more depth on this check out the thematic analysis links in the references.
References
Kolko, Jon. “Abductive Thinking and Sensemaking: The Drivers of Design Synthesis.” Design Issues 26, no. 1 (January 2010): 15–28. https://doi.org/10.1162/desi.2010.26.1.15.
Thematic Analysis
Braun, Virginia, and Victoria Clarke. “One Size Fits All? What Counts as Quality Practice in (Reflexive) Thematic Analysis?” Qualitative Research in Psychology 18, no. 3 (July 3, 2021): 328–52. https://doi.org/10.1080/14780887.2020.1769238.
Braun, Virginia, and Victoria Clarke. “Using Thematic Analysis in Psychology.” Qualitative Research in Psychology 3, no. 2 (January 2006): 77–101. https://doi.org/10.1191/1478088706qp063oa.
Summary of steps
1: Clean and Organise Your Data for Analysis
Group Data: Organise your data per question or topic to manage it efficiently.
Prune Unnecessary Info: Remove data that doesn't contribute to your objectives.
Visual Tools: Utilise tools like Miro boards to visually map out your data for clearer understanding and easier access.
2: Creating Preliminary Themes with Affinity Mapping
Cluster Similar Data: Use affinity mapping to group similar observations and quotes. Affinity mapping is a technique where data points that share common themes are grouped together visually.
Avoid Early Labelling: Initially, focus on forming groups; name them later to avoid early bias.
Refine and Rename: Once grouped, refine the clusters and assign names that accurately reflect the underlying themes.
3: Turning Themes into Findings
Formulate Declarative Statements: Summarise themes into clear, concise statements. A declarative statement is a sentence that conveys information assertively without a query or strong emotion.
Qualify Findings: Ensure each finding is backed by data and reflects participant perspectives.
Reassess and Adjust: Continuously reassess the themes and findings for relevance and accuracy.
4: Deriving Insights from Observations via Abductive Analysis
Hypothesise Reasons: Use abductive reasoning to hypothesise why certain behaviours or patterns occur. Abductive analysis is a method of logical inference which starts from an observation then seeks the simplest and most likely explanation.
Seek Plausible Explanations: Choose the most plausible explanations for behaviours observed in your data.
Link Insights to Objectives: Relate your insights back to the research objectives to ensure alignment and relevance.
5: From Insights to Actionable Recommendations
Translate Insights: Convert insights into practical, actionable recommendations.
Communicate Benefits: Clearly communicate the reasons for data collection and how it benefits users.
Implement Gradually: Introduce information requests incrementally, building trust and understanding over time.
