How can one connect the dots when it comes to sensemaking? - Chapter 12

Sensemaking is the attempt to understand events that have occurred and to anticipate what might happen next.

What is the essence of claim 7?

Claim 7: To make sense of a situation, we draw inferences from the data

Some people picture the mind as an assembly line. Data arrive from the senses, then get refined and processed and combined and polished and assembled until we have insights and conclusions. Claim 7 stems from the assembly line metaphor. It portrays our sensemaking as making more and more inferences from the data we receive. The data get converted into information, then get further enriched to become knowledge, and finally transformed into understanding.

This process illustrates how people and organizations cope with all the data and cues that come at them.

Sensemaking is sometimes explained using the metaphor of a picture puzzle; we treat each message and data point as a piece of a puzzle and find a way to put these pieces together.

Claim 7 runs into several serious difficulties. The assembly-line metaphor fits orderly situations in which you only have to handle explicit knowledge. Once you have defined all the data and described the causes, you can set up filtering rules and do the sorting.

Our expertise is as much about recognizing legitimate dots as about connecting them.

When we want to put data into perspective we need stories. Without stories, the data won’t mean very much.

In judging what counts as a dot we also have to figure out how much detail the dot has to have. It depends on what we are looking for. If forecasters work at too fine a level they’ll miss the large patterns.

There is no ‘right’ level and no standard operating definitions of a “dot”. If there is no right level, the job of designing displays for forecasters becomes tougher. Sometimes the data elements we think are dots may turn out to be irrelevant.

Where do the dots come from?

The notion of connecting dots, deriving inferences from data, conjures up an image of a children’s puzzle that displays all the dots; our task is simply to find the right connections in order to see the picture. These puzzles don’t have any shadows to complicate the task. But when we make sense of events we usually are seeing only some of the dots that complete the picture, trying to remember others, discarding ‘wannabe’ dots, and adjusting dots that have gotten distorted.

Sensemaking seems to depend heavily on seeing connections. We can’t find the connections by exhaustively linking all the elements, because we don’t know in advance which are the relevant elements, the relevant dots. And we would run into an exponential explosion if we connected every combination of dots. That’s where the intuition comes in --- To help us recognize which dots are worth connecting and to remind us which dots we need to complete the picture.

When are we finished making sense of a situation?

Claim 7: To make sense of a situation, we draw inferences of the data. This claim describes a mindless process of churning out more and more inferences. The numbers of data combinations grows exponentially as we add more data elements. We may be able to generate valuable ideas, when we randomly connect data elements and then connect the connections, expecting to emerge with some insights.

But these ideas will be buried in useless inferences. Then we will have the enormous task of sorting through all the inferences to try to find ones that matter. No, this bottom-up account of random inferencing doesn’t make sense.

As Bill Clinton was fond of saying: “That dog won’t hunt” Instead, we are directing the way we form connections, just as we’re actively searching for data. We are trying to explain what is going on around us. We are looking for stories. When we arrive at a plausible story, we’re done.

What if the stories are wrong?

Claim 7 shows people endlessly generating inferences from data. It doesn’t reflect our worries about that the data and the stories might be wrong. If you’re just looking for connections, then any connections will do. Yet an important aspect of sensemaking is sorting out which dots to believe. A lot of sensemaking is double-checking whether the data are accurate. It’s also about judging whether the story rings true.

The assembly-line model of generating inferences doesn’t capture the desperation we feel when we get lost and have to sort out where we are, where we want to be, and what data we can still trust.

If we enforce claim 7: to make sense of a situation, we draw inferences of data, illustrated by the assembly line-model, we’ll filter the data to make sure that decision makers only get pre-digested materials and highly abstracted analyses of the data. However, we use our expertise to see patterns in the data. If we disconnect people from the data, we disconnect them from their expertise.

The assembly-line model takes information search out of the hands of the decision makers. Yet that’s another aspect of their skill. Experienced decision makers know how to search for data. The assembly-line model makes it hard for decision makers to build their own mental models, because they are forced to depend on the dots and the analyses that people at lower levels, with less expertise, are using.

Experts have to build their own mental models. The assembly-line model builds from data elements dots, that have been collected. But what about events that didn’t happen?.

Experts appreciate the significance of these negative cues, but when they are given a set of recommendations and summaries, skilled decision makers don’t have any way to notice what didn’t happen.

Therefore, the assembly model, and claim 7 will disconnect skilled decision makers from their expertise. It will force them to depend on subordinates who have less experience than they do

Replacement

We aren’t passively waiting for data. We are also defining what counts as data. We are questioning stories that don’t seem plausible. We are having insights about ways to connect different types of dots. That’s different from drawing more and more inferences in the hope that eventually it will all fall into place. It depends on taking an active rather than a passive stance.

We make sense of cues and data by fitting them into frames such as stories. The frames we have learned guide our attention and shape what we notice. For example, if we go for a walk in a park, a landscape architect is aware of the way the path provides interesting views. A tree specialist is distinguishing the different species and how well each is growing, and a maintenance worker is getting a feel for whether heavy lawnmowing equipment might get stuck in the muddy ground

Same park three different perspectives, three different experiences. We make sense of cues and data by organizing them into frames, such as stories, scripts, maps and strategies. But the reverse also happens. Our frames determine what we use as data. The data/frame model of sense making shows this is the process of fitting data into a frame and fitting a frame around the data.

Conclusion

We make sense of data elements by fitting them into frames, such as stories, but the reverse also happens. Our frames determine what we use as data.

The claim matters because the claim says that all we do is draw inferences from the data or connect the dots trivializes what we do. It ignores how in complex situations we have to decide what is a dot. It ignores the tacit knowledge that it takes to figure out which dots to connect.

Also claim 7 can disconnect experts from the data patterns they have learned to detect.

Claim 7: To make sense of a situation, we draw inferences from the data

Some people picture the mind as an assembly line. Data arrive from the senses, then get refined and processed and combined and polished and assembled until we have insights and conclusions. Claim 7 stems from the assembly line metaphor. It portrays our sensemaking as making more and more inferences from the data we receive. The data get converted into information, then get further enriched to become knowledge, and finally transformed into understanding.