Design Analysis: It all comes down to this
In Design Decisions I outlined the 4 types of decisions I focus on in design projects - project definition, research planning, design analysis and concept development. This post focuses on design analysis decisions - turning design research data into actionable project knowledge.
Design teams can easily create mountains of raw data while conducting design research. The task of making sense of this data is often daunting, but it is a critical part of cutting through the ambiguity that is part of any design or innovation project. Good design analysis decisions create clarity by finding the important information in the mountain of data and translating it into the actionable knowledge often referred to as “insights”. This actionable knowledge is the result of many decisions and when done well is a solid platform for brainstorming and prototyping that can lead to user-centered innovations. In my view, creating this actionable knowledge out of observed facts from the world is the fundamental and defining act of design thinking.
For those new to design thinking, design analysis is often viewed as mystical and the sole domain of design geniuses. It’s not clear how it works and the process seems to feel different from one project to another. I think there is an understandable, consistent, but latent pattern in design analysis decision-making that spans projects, teams, organizations and domains. I also believe anyone can contribute to this critical part of the design process once they understand how it works.
A designer is able to wade confidently into a mass of design research data because they know what they are looking for. She must decide what is important from the research data and how it is important for her project. To create actionable insights from the research data, designers look for and construct patterns in the data. Deciding what stories from the research data fit into this pattern is what I call design analysis. The consistent pattern designers look for has 3 components: Stories, Description of harm or opportunity and Solution criteria and strategies.
An example of design analysis...
While at IDEO I worked with a team to help Fresh Express expand their offering into single serve convenience meals. Fresh Express was the first to offer convenient pre-washed salad greens and they wanted to build on this history of innovation by providing entire salad meals on-the-go instead of just the ingredients.
As part of this project we observed on-the-go salad eaters to understand the problems and opportunities that could serve as the foundation for our project. We developed over a dozen key insights during design analysis that served as the foundation for brainstorming in our project. Interspersed below is one example that illustrates how design analysis creates actionable insight from design research data.
Share stories
Design research data is made up of stories. Designers look for stories in the data that represent some form of problem or opportunity in people’s experience of a product or service. These factual observations are the foundation of a user-centered insight.
I prefer to share stories amongst a team via post-it notes (as do many designers) because the format forces you to be concise and capture the essence of an observation. If you use post-its you’ll likely end up with a wall full of them from just a handful of user interviews or observations. Photos, videos and artifacts also play a critical role by adding color for team members who were not present during the research.
Often multiple stories from more than one source point to the same issue. Clustering these related stories into groups helps the team create shared understanding and see a problem from more than one angle. Post-its are useful again here because you can rearrange the stories easily into these clusters.
Describe the harm or opportunity
Once a group of stories is clustered the task shifts from recognizing patterns to interpretation and construction of meaning for the project. While the stories describe observable facts in the world, this new type of information is generated by the team and reflects the team’s judgement of how an aspect of life can be improved. This is a critical juncture because judgement and interpretation enter the process. Not all teams would look at the same cluster of observations and see the same opportunity for improvement.
A cluster of observations can be interesting in some way without being evidence of a problem or opportunity. If the harm or opportunity cannot be summarized then it’s not a useful observation for the project and should be put to the side. Inevitably, 80% of research data collected isn’t useful for a project. Perhaps later the harm or opportunity will be become evident but until then I prefer not to use team energy on these observation clusters.
One form of “swirl” or “analysis paralysis” occurs when teams try to fit all the collected research data into some kind of all-encompassing framework. In my view this is wasted energy because the most useful information for brainstorming are observed problems and opportunities.
Develop solution criteria and strategies
The statement of harm or opportunity can then be translated into solution criteria. This helps the team to begin to think of new ideas and is the first step in new concept development. With a rich set of well formed criteria the team now understands the problems to be solved with much greater detail. These user experience criteria are a critical component of future decision making because clearly articulated criteria can be prioritized, weighted and used as part of the framework for evaluating ideas once the team focuses energy on developing solutions.
With clearly articulated criteria, initial ideas almost present themselves. This makes the transition to brainstorming and concept development seamless and natural and brings us to the next type of design decision making - concept development (which I plan to cover in a future post).
Project shorthand
Providing a title helps the team begin to create a “project language” or shorthand. The title makes each insight from user research easier to share outside of the project team and also gives the team a language for discussing research findings more efficiently.
It’s not linear
I’ve talked about this before but touching on it again here is important. While each component of a well constructed insight links to the next, project information is not generated in a linear manner. The title of the insight may come first through an intuitive leap. Alternatively, a solution criteria may present itself before there is a concrete observation to substantiate it - and there’s nothing wrong with that. It can be useful to “work backwards” sometimes. For instance a team may first develop a solution criteria and then “backfill” the insight with stories and a problem definition. This can help ensure the solution criteria is relevant and well understood within the team.
It’s not magic
Design projects are fraught with ambiguity. It can generate a lot of anxiety to try to find meaning in a mountain of post-it notes. Tolerating the ambiguity can be tough. Through experience, designers develop a high tolerance for ambiguity - because we are always immersed in it. Also through experience, designers develop a faith in the largely tacit design analysis process that makes sense of design research. The uninitiated don’t share this confidence and this can prevent their meaningful participation in a design process. Additionally, a largely intuitive approach to design analysis breaks down at a large scale and prevents design teams from tackling our biggest problems.
I see design analysis decisions as the fundamental act of design thinking. By translating observed facts about the world into actionable knowledge and strategies, design analysis decisions create a platform for envisioning a better future. In my work I try to take this ability of the designer to boil mountains of data into concise actionable knowledge and make it more explicit. This allows a much broader set of collaborators to engage and allows teams to take on much larger and more complex problems. I am also confident that the diversity of perspectives that can be leveraged contributes to better project outcomes.
