NOTES FROM MONICAS BOOK
Interpretation will also identify insights, which are provocative statements of truth about people that speak to their lifestyle choices, their aspirations, and their desires. For example, if you observe people using the subway, you’ll see some people working on their laptops and other people reading books or newspapers. You can interpret this behavior in a number of ways. You might guess that people want to be left alone. The boundaries of public and private space are confused in such tight confines, so artificial items like books or headphones create artificial walls to delineate space. Or, you might guess that people are celebrating the focus they’ve achieved. The confines of the space force them to be more productive.
Both interpretations make sense, and both will lead you in different directions, but there is a large amount of inference between your observations and these interpretations. Your observation identifies neither a problem nor a solution, so innovations that are based on these interpretations will carry risk.
Armed with your utterance groupings and your time-based visualizations, you can start the process of insight extraction by making succinct statements about the things you’ve observed. Recall Joe’s observational statement from his research: “People seem concerned by their stressful jobs, but they don’t seem to do anything to fix their situations.” First, note that the statement makes a sweeping generalization about the people he spoke with, but doesn’t make any attempts to qualify the statement as biased. At this point, it’s perfectly fine to be biased; in fact, bias is desired, because it implies a thoughtful depth of interpretation. When you interpret, you assign meaning to data. That assignment is a subjective process. At the same time, don’t lose sight of your objective. You are trying to provoke something new, not predict how a small data set maps to a larger population. This is not a statistics exercise. Next, note that the statement is an observation, not a solution. Joe doesn’t offer a way to help these people yet, nor does he judge the contents. He simply makes a statement. Finally, notice how the statement is subtly concerned with both behavior and time. It points out a state of mind (being concerned), as well as a causal chain that stretches over time (being concerned might lead to fixing the concern). This observational statement is a bridge statement on the way to an insight. Create an observational statement about each group of utterances you’ve identified. You should end up with between eight and ten observational statements.
Now, you can use the observational statements to extract insights. In the context of design and innovation, an insight is a provocative statement of truth about human behavior. Each statement is presented as a fact, but each is actually an inference. Each statement may be factually wrong, so using insights will introduce risk into your process, but this risk has its reward. Insights are the source of innovation: insights are gold. When you get a “hit” with an insight, you’ll tap into some pretty powerful human motivators to help people change their behavior, and you’ll be able to build these motivators into your products. It’s easy to move from a series of signals to an insight. Start with your observational statements. These statements are true, at least for the people you watched and the data you gathered, but are stated as true for a larger population. These statements become the foundation upon which insights are formed. Now, ask and answer the question, “Why?” When you answer the question, you are making an inference. You’ve assigned meaning to the data that you gathered. Your inference may be wrong, because it’s a guess. Joe made this observational statement:
“People seem concerned by their stressful jobs, but they don’t seem to do anything to fix their situations.”
When Joe asks and answers the question, “Why?” he might arrive at any of the following inferences:
Each of these inferences is believable, yet each one could also be completely wrong. But each is stated as if it’s a universal truth. A few data points have been generalized in order to make a broad statement about why people do the things they do. Joe—and you—will move forward as if you’ve identified a causal relationship, even though you obviously haven’t.
Each statement takes an authoritative tone, even though each statement is not a valid inductive argument. These are insight statements. This authoritative tone makes it possible to use the insight as a point of departure to identify product constraints. Insights are about human behavior, as they describe intent, actions, emotions, and other aspects of motivation. Insights are provocative because they act as a logical gatekeeper: based on an insight statement, other things have to logically follow. Joe selected the last statement: