The Magical Part of Design
There has been a few occasions over the course of my design career when I’m asked how I come up with a design solution and know it’s the right one. I’ve never had a satisfactory answer for this because it’s too complex to explain succinctly, so I usually answer with something as simple as “creativity and validation,” which is not far off the mark, but rather vague. A better answer, at least in the context of product, software, and website design is synthesis, which is the magical part of design that uses insights from data to generate epiphanies and inform design decisions.
Stepping Away From the Problem
As magical as synthesis can be it is often the invisible part of the design process that team members and clients don’t see because it occurs in isolation: in the designer’s mind, on our computers, or by incubation. The latter especially make synthesis unpredictable, difficult to anticipate, and challenging to time-box. But incubation is also intuitive and natural—we all use it to solve all types of problems from relationships with people to important life-changing decisions. Incubation is the act of stepping away from the problem to allow time to pass so ideas can percolate. This is one reason why time-boxing design doesn’t always yield the right solution; it produces the best solution that can be accomplished within a confined period of time, but limits the amount of synthesis that can occur. However, even though synthesis does rely on incubation, it can be structured by using inference-based sense-making methods to help generate ideas more quickly and enable team members and clients to participate in the process as well.
Synthesis & Ethnography
The essence of design is to solve problems and the best way to achieve that is by having a process that includes synthesis because it bridges gaps between different phases of learning and problem solving. Another word for design research is ethnography, which enables a designer to immerse themselves in a problem, culture, or domain in order to understand it. This is often observational and achieved by studying people’s behavior, but can also be done by conducting interviews and asking people questions or by contextual inquiry in order to understand how people solve problems in their own setting or workplace. Ethnography gathers real data on real people and attempts to understand their relationship to a problem instead of a designer or stakeholder making assumptions based on personal points of view and mental models.
Inference-based Sense Making & Logic
Once we have gathered data we can flag the most profound insights and use inference-based sense-making to formulate logical ways of thinking about the problem. There are three forms of logic (or reasoning) that are true for us as human beings that we can use as designers. These are:
Deductive logic is a self contained logic. Its reasoning is if the premise is true then the conclusion is true. A simple example of deductive reasoning is:
Since all humans are mortal
and I am a human
then I am mortal
As humans we are really good at using deductive logic. For example, we use it all the time to pick apart a movie or a book that sometimes breaks the premises it has set forth.
Inductive Logic is evidence based reasoning that uses the formula that it is true until it’s not. It is logic that recognizes there is a probable chance of inaccuracy. Here is a simple example of inductive reasoning:
All basketball players I saw at the game were tall
therefore all basketball players must be tall
We know this logic breaks down when we go to a high school basketball game or pickup game at the local court, but we hold onto this logic until a situation presents itself to prove our reasoning is wrong. Both deductive and inductive reasoning form the backdrop for how most scientific studies are done.
Abductive logic is inference based and relies on circumstance, experience, and observation. Unlike deductive reasoning the premises do not guarantee the conclusion. However, its power comes from using its own reasoning combined with deductive and inductive logic to form a series of statements. Here’s an example:
I’m driving on the freeway and suddenly traffic comes to a halt.
A mile away I see police lights.
There must have been an accident.
Traffic continues to be at a crawl for 15 minutes.
It must have been an accident.
I hear more sirens and see an ambulance head towards the scene.
The accident must be serious and just recently occurred, which means I may have to wait awhile.
(Inductive and abductive reasoning)
Throughout my whole life experience whenever something similar happened it was an accident.
I know from experience that if I’m patient and wait this through I will eventually drive pass the accident and traffic will become normal again.
Another option is that if there is an exit coming up I can get off the freeway and it will save me time.
I see an exit and decide that since this is a serious accident that has just occurred it may take awhile to clear up so I’ll get off at the next exit and take a local route to my destination.
(One possible solution to the problem)
We use abductive logic all the time throughout out our daily lives and are mostly unconscious of it, but synthesis relies heavily on this form of logic to draw conclusions in order to make a logical progression and generate solutions to a problem.
Synthesis As Learning Process
As designers we use sense-making in our process to make connections between insights (data) that may not have been connected before to forge new relationships, meaning, and concepts. Another way of saying the same thing is that synthesis provides us with a learning process—we learn about a problem so we can adequately solve it.
The process of learning has four phases:
These phases of learning do not always occur as a linear progression. Any of these four can occur during different phases of design because often the process is like a spiral as we iterate and loop through phases. However, we do aim to cross one phase of learning to the next by bridging gaps between them. This benefits the designer because by the very act of bridging we connect various insights (obtained by research and ethnography) that have the potential to be epiphanies, those “aha moments” when something profound is discovered.
Working With Data
As a designer I will often begin a project by doing research and looking at data. Some of this data might come from the client during the discovery phase. They might provide wireframes, powerpoint decks, marketing segmentation reports, strategic positioning statements, product specifications, interaction models, and analytics—all this data they’ve gathered is mostly quantitative and can be used to create hypotheses.
Sometimes client data is based on a handful of stakeholder’s personal views and assumptions about how things should work and how they will solve problems for people they’ve most likely never talked to or don’t clearly understand. The problem with relying on stakeholder data is it can end up solving the wrong problem or it can solve the correct problem the wrong way.
On the other hand there is human data that can be obtained by research and ethnography, which allows designers to understand real people’s needs, pain points, and mental models so we can solve the right problem successfully.
One of my main objectives as a designer is to understand stakeholders’ points of view and their business goals and also get data from people who use the product or have a need for it. The challenging part of this process is bridging gaps of information. This is mostly a problem because of either a lack of data, a lack of time, or both. But it’s also a problem because it can be overwhelming trying to work with the data you do get. Synthesis provides a way to work with data so we can move forward to other learning phases. One point I want to stress is that it’s more important to get user data because it gives you the best chances for success—meaning you will actually solve the problem the right way for the right people.
Different Synthesis Techniques
When designers bridge the gap between the four phases of learning we are actually synthesizing data so we can connect insights, generate epiphanies, and develop hypotheses. There are different techniques to do this, which I’ll describe below for each learning phase.
Synthesizing Data to Information
The best thing to do in this phase is get data out of our computers and onto a wall so we can move it around and start to form relationships. By doing this we start to see data from a bird’s eye view rather than having tunnel vision that computer screens limit us to. It allows us to see patterns and themes and helps us understand problems more clearly.
We can also turn data into information by making diagrams which force us to look at relationships and make design decisions. These can be thinking sketches, flow charts, or mind maps. We can interpret the data and therefore assign it meaning. We can use the technique of asking “why?” over and over again. This is abductive reasoning, the seed of a new idea that may or may not be correct, but still allows us to consider the problem in a way that may not have been apparent before.
Some methods that can be used to move data to information are:
- Affinity diagramming
- Experience mapping
- Flow diagramming
Synthesizing Information to Knowledge
This is mainly concerned with conveying the user experience, which is achieved by understanding people’s behaviors, attitudes, and pain points when they try to complete their goals using a product. The best way to have this understaning is to first make sure we have data based on user interviews, contextual inquiries, or using various techniques of testing such as exploratory or usability testing.
Another synthesis method that can be used is to change perspective by zooming in and out of the problem. In software design we do this by shifting the problem from high to granular levels. We might make a product flow chart to capture how the overall software works and a wireframe to understand a specific area’s information architecture, UI, and content. We can also shift context to consider the different states the product can be experienced such as a first time user versus power user. Furthermore, we can consider error states and anticipate how a product might scale over time.
Some other methods that can be used to move data from information to knowledge are:
- Concept Mapping
- Persona Development
- Scenario Development
Synthesizing Knowledge to Wisdom
This phase of learning utilizes what if? thinking and is generative. This is when blue sky ideas can emerge because we’ve studied the data and now we can use all forms of logic, especially abductive reasoning. We can also use lateral thinking as a method of considering the problem in a different light. The more diverse and cross functional a team is on a project the better chances lateral thinking will be effective because people’s various cultural backgrounds and interest can bridge relationships between meaning. We also cross that bridge by having empathy, which is provided by the data we’ve gathered on people to understand their desires, needs, pain points, and mental models.
Some other methods that can be used are:
- Insight combination
- Participatory design
Understanding Data to Solve Problems
Synthesis is the magical part of the design process that allows us to understand data so we can solve problems for businesses and people. It relies heavily on abductive reasoning, which is based on sense-making and can draw on deductive and inductive logic. Synthesis is the process of learning, which provides us with insights that bridge relationships between data, information, knowledge and wisdom. It ultimately allows us to understand the problem, domain, or culture we are concerned with and create a point of reference and rational whole so we can develop new concepts.