Gathering customer data

Sensemaking

In March 2011, David Moore published a sensemaking manual to help central intelligence agencies reduce mindlessness, a mode of fixation and relaxation where analysts became “so preoccupied with a few central signals that they largely ignored things in their periphery.”¹

Moore observed that analysts had developed an automatic response to the signals they were seeing, which prevented them from uncovering new insights. To keep up with the wealth of incoming data, they began to focus on specific signals, leaving themselves vulnerable to new threats they hadn’t considered.

As you begin to conduct interviews, surveys, concept value tests, and other experiments, your team will have a flood of data points. It can be hard to gain broad insights from all of these signals and, just like intelligence analysts, your team will be susceptible to focusing on just a few signals, missing out on larger opportunities for your products.

Sensemaking is a process that ensures we organize the data we collect, identify patterns and meaningful insights, and refine them into a story that compels others to action.

The Sensemaking Loop

Sensemaking is a perpetual cycle of collecting data, making sense out of it, and sharing knowledge throughout our teams and organizations. Pirolli and Card refer to this as the “sensemaking loop.”² It essentially breaks down into five procedural components (see Figure 1-1):

• Data sources

• Shoebox

• Evidence file

• Schema

• Stories

Figure 1-1. The sensemaking loop

These components come ...