3c. Practice: Finding themes and opportunities in your own data

Finding themes and opportunities in your own data

Try it out: Conduct a bottom-up affinity clustering

Using the post-its or small pieces of paper you prepared in a previous module, conduct a bottom-up affinity clustering.

  1. Look for common ideas or themes that are emerging within your data, and create groups accordingly. It can be useful to do this activity on a large tabletop or, if you are working with sticky notes, on a wall or whiteboard.

  2. As groups emerge, give them a title on a separate sticky note, so you can easily remember what the theme or pattern is for each group.

  3. After you’ve placed the majority of your observations into groups, write down insights related to each group, focused on the user. It can help to use different colored sticky notes or pieces of paper to capture your insights. Write statements like: “Users want…” or “Users need…” To help generate insights, you can also ask yourself “Why is this group important?” or “So what?”

  4. After you have generated insights for each group, look at all of your insights and generate a list of problem statements (“How might we…”) and design principles (“The solution should…”) these insights imply.

  5. If you have access to a camera, take a photo of your clusters so you can refer back to them later if needed.

Try it out: Conduct a top-down affinity clustering

Next, reset your materials (take the sticky notes or pieces of paper out of their groups) and conduct a top-down affinity clustering. Set aside the insights you generated from your bottom-up affinity clustering - you won’t need them for this exercise.

  1. Begin by identifying the categories you plan to group data within. We recommend starting with the NOABS framework: Needs, Objectives, Activities, Breakdowns, Solutions.

    1. Needs = things users need

    2. Objectives = things users want to accomplish or achieve

    3. Activities = things users are currently doing

    4. Breakdowns = problems users face, or things that are painful

    5. Solutions = ways users are currently trying to solve their problems

  2. Create a label for each category on a piece of paper or sticky note. It can be useful to do this activity on a large tabletop or, if you are working with sticky notes, on a wall or whiteboard.

  3. Group your data into the relevant categories. If some data points don’t fit into one of the categories, that’s okay. It can be helpful to create sub-groups of similar topics or themes emerging within each category.

  4. As groups emerge, give them a title on a separate sticky note, so you can easily remember what the theme or pattern is for each group.

  5. After you’ve placed the majority of your observations into groups, write down insights related to each group, focused on the user. It can help to use different colored sticky notes or pieces of paper to capture your insights. Write statements like: “Users want…” or “Users need…” To help generate insights, you can also ask yourself “Why is this group important?” or “So what?”

  6. After you have generated insights for each group, look at all of your insights and generate a list of problem statements (“How might we…”) and design principles (“The solution should…”) these insights imply.

  7. If you have access to a camera, take a photo of your clusters so you can refer back to them later if needed.

Try it out: Identify your problem statement and design principles

  1. Take a look at the problem statements you identified during the bottom-up and top-down affinity clustering exercises. Select one that is most important to the users you spoke to. This will be your problem statement for the rest of the course.
  2. Pick 3-5 design principles from your lists that describe what a solution to this problem should be like. These will be your design principles for the rest of the course.

Share and reflect

Share with your problem-specific learning group:

  1. How did the outcome of the bottom-up versus top-down affinity clustering compare? Were the themes that emerged across the two approaches different?
  2. Which problem statement and design principles did you end up selecting?