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“chair” or “regrettably tasteless table lamp.”
    Pattern finding, or stereotyping, is of course both necessary and ordinarily quite beneficial. We literally couldn’t get through the day without it. But stereotypes and patterns can also obscure a great deal of potentially relevant data that might open our minds if only we were aware of them. Silver’s colleagues at 3M probably had a clear stereotype of what a “bad adhesive” looked like: like the thing that Silver had just produced. For them this was something to be thrown away, not to be paraded around the lab and company. Silver, in contrast, found it curious and worthy of investigation.
    Limiting the Universe of “Relevant” Data
    We exercise another kind of selectivity in our perceptions when we short-circuit the search for relevant data. Here the problem is not that we fail to see what is right in front of us, but rather that we go too far in limiting where we look.
    Seeking out and processing data beyond our usual sources of information carry a cost, even if the data are available elsewhere in the building. To avoid incurring these search costs, people commonly start out by gathering the familiar data they understand to be needed based on a stereotyped definition of the problem. Recall that when Art Fry finally convinced the 3M marketing department to gather market data for the Post-it, it set up the market test the way it always sets up market tests. This was efficient, as the department already had test markets established and already knew how to interpret the data. Unfortunately, because this product needed to be tried before people would be convinced of its value, these were the wrong kind of data to answer the question. Only by insisting on an alternative data-gathering method was Fry able to generate the insights he needed to understand the value of the product in the consumer’s mind and to go about convincing the consumer of that value.
    The kind of data we have access to may also be limited by organizational policy or even organizational culture. For instance, we may arbitrarily or unconsciously limit data gathering to data that are already available inside our organization, perhaps because of organizational policies that discourage revealing ideas to the public or to competitors. Even Fry confined early “market testing” inside 3M by providing samples to colleagues. In addition, some kinds of information may be tightly guarded and inaccessible to the innovator, such as information on financial performance, market size, clientele, and product failures. Organizations may also limit the types of outside data you can buy or subscribe to. In one consumer products company, the marketing director had to go to her local university to gain access to important market-share information because her company “was too cheap to buy it.” Only after proving the value of insights she had gleaned from the data was she able to convince the company of the wisdom of purchasing a legitimate subscription.
    Not Getting Close to the Data
    Your physical environment also affects the perceptions you have and the data you gather. Look around your work environment and think about the things you’ve used as inspiration or as sources of information. I recently gained insight into my own reliance on my environment for data. Instead of using my small office to meet with student project teams for a course I teach, I tried conducting the meetings in a nearby conference room. The conference room was larger and had a bigger whiteboard, more comfortable seating, and better climate control; yet the meetings in the conference room felt oddly unsatisfactory. I couldn’t figure out why. Then, because of a scheduling glitch, I was forced to move the meetings back into my office. It was then that I realized that I relied heavily on the many books on my shelves for inspiration and as memory aids. Simply glancing at them on the shelves