While the cholera map exemplifies visualization's power to extract a single important insight from one complete picture of a dataset, in real-world applications visualization is typically only one part of a larger sense-making process that includes collecting, analyzing, and understanding information iteratively.

The 'evidence file' phase of sense-making (originated in intelligence analysis) involves highlighting and extracting specific text from collected materials that contains information relevant to the decision at hand, distinguishing important details from surrounding context.

Data visualization and data analysis are commonly practiced, with Excel bar charts and line charts being a standard form that many people already use without explicitly calling it visualization.

The current Chrome extension tool has academic software quality (limited polish, maintenance, and user experience design), and the speaker seeks collaborators to improve software engineering and make the tool more widely available.

A concrete example of sense-making is researching and selecting a camera with a £500 budget after the birth of a child; this process involves researching, encountering unfamiliar technical terms, investigating those terms separately, taking breaks, and resuming across multiple sessions.

The sense-making process is often incomplete within a single session; the searcher may spend time researching, make progress but not reach a final decision, wait several days, and then resume—at which point the challenge becomes how to efficiently reconnect with the previous exploration thread.

In 1854, during a cholera epidemic in London, plotting each case on a map allowed epidemiologists to identify the geographic center of disease concentration and eventually trace the source to contaminated water, demonstrating that visualization can reveal patterns invisible in raw patient records.

What appears easy to recognize in retrospect (the cholera map pattern) was extremely difficult to discern when looking only at patient records, suggesting visualization's power lies in making existing patterns cognitively accessible rather than revealing entirely new information.

A major shift in data visualization is that visualizations are now created automatically using computers, whereas in 1854 they had to be created manually; however, the speaker observes that sometimes the manually created 1854 visualization actually looks better than modern automated versions.

Dashboards are a very common form of data visualization that consists of collections of smaller or simpler visualizations, and their use increases as data becomes more complex and as the tasks being addressed become more complex.

While people focus on the analyzing parts (data analysis or data science), the other parts of sense-making are very important but often overlooked; the speaker claims this is something people do much more often than they realize.

Sense-making occurs in diverse everyday contexts including planning holidays, finding places to live, and choosing university degrees, demonstrating that it is a universal cognitive process, not limited to professional data analysis.

The key difference between sense-making and data analysis is that in sense-making the goal is not well-defined and the process is open-ended, iterative, and exploratory, whereas data analysis has clear objectives and defined methodologies.

During the camera research process, the searcher encounters unfamiliar technical terms (mirrorless, DSLR, macro forces) and must conduct additional research to understand what these terms mean before continuing to evaluate cameras, illustrating that sense-making is non-linear and includes recursive learning phases.

A practical indicator that someone is engaged in sense-making is having 20-30 open browser tabs, which represents their accumulated exploration paths and information collection.

The sense-making process includes a 'shoebox' phase where interesting items found during research are bookmarked or saved for later review and potential use in further analysis.

During the evidence file phase, the researcher learns which information is relevant to the problem being solved; for example, in camera selection, the researcher discovers that megapixel count may not be the relevant metric, but aperture is more important when taking baby photos because it enables faster shutter speeds.

The 'hypothesis' phase of sense-making involves formulating multiple candidate solutions from the collected evidence that all appear promising within the constraints, and then conducting detailed comparisons between them to reach a final decision.

The speaker has developed a Chrome extension tool that visualizes the exploration process as a graph (rather than just a sequential browser history list), allowing users to see their exploration path and highlighting important images or pictures they found during research.

The 'presentation' phase of sense-making involves explaining both the final answer and the exploration process that led to it, rather than having others independently traverse the same sense-making journey, representing a knowledge transfer goal.