SenseMaking (Data Visualisation) - Computerphile
What this covers
Turning data into something visual helps humans make sense of things. Kai explains and shows an open source tool for tracking your research.
More information about SenseMap: https://vis4sense.github.io/sensemap/ GitHub repository for HistoryMap (the new version of the chrome extension): https://github.com/vis4sense/historymap
This video was a reupload due to some audio glitches on the original copy.
https://www.facebook.com/computerphile https://twitter.com/computer_phile This video was filmed and edited by Sean Riley. Computer Science at the University of Nottingham: https://bit.ly/nottscomputer Computerphile is a sister project to Brady Haran's Numberphile. More at http://www.bradyharan.com
Source description (no synthesized summary yet).
Data visualization and sense-making are complementary but distinct processes; while visualization helps identify patterns in data, sense-making is the broader exploratory and iterative process of understanding complex information to make decisions, and tools should support the full sense-making workflow, not just data presentation.
- John Snow's cholera map demonstrates visualization's power to reveal patterns, but real-world decision-making requires ongoing exploration beyond single visualizations
- Sense-making is iterative and goal-unclear (like navigating a maze) whereas data analysis has defined goals, requiring different tools and approaches
- Current tools support only early stages (collection, evidence marking) but not deeper understanding and hypothesis formation across complex topics
This asset isn't compiled yet
You're seeing its claims, ranked. Compile it to build the argument threads, weight them, and check each claim against your library — the full view.
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.
“all these are kind of very easy to see retrospectfully but at the very beginning when they look just at the patient and record they just couldn't figure out what was the problem for a very long time”
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.
“this has happened in a cholera epidemic I think was in London they plotted every case on the map each bar is one case and that's where the address that was detected by looking at the map you can see that certainly is kind of center of the disease happening and they eventually located as well with the water was contaminated”
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 examples is like a planning holiday and find a place to live or maybe apply for University degree”
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 cholera map is a good example in terms of you showing one picture of the data set and and you get a very important Insight after it and but in many cases in the actual kind of real life and it's maybe just a part of a bigger process and which I would call sense making”
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.
“It's you probably not going to finish that in one session you might say you spend now on it you didn't quite you make some progress but didn't find the perfect camera yet and you wait a few days until the weekend you want to continue but then that's often okay how do you continue the process you did two two days ago”
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.
“in terms of difference from data analysis so usually the goal is not well defined and the process is very open incense is explosive and iterative”
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.
“this is usually after you look at the things you've collected in the shoebox and the schema ties you're kind of starting to understanding the problem better and what is relevant to you see and there's a megapixel used for or maybe the aperture is actually more relevant in the case you take baby photos because you need a five I say faster shutter speeds and that's when the aperture comes into play”
Sense-making can be understood through the analogy of navigating a maze: you have a clear destination (the goal) but no map of the path, so you must explore, feel your way through, possibly repeat paths before finding the right direction, making it fundamentally an exploratory process.
“the kind of a not very good analogy I use is kind of the maze you have a clear goal you want to get into the center of the maze or you want to find the crime in the tweets you want to find the degree that you want to study and but you don't know the maze so you have to explore feel your way through hopefully eventually you would get to the middle and it's kind of explore process you might have to repeat the same past a few times before you find sent the right way to the center”
The Chrome extension visualizes exploration as a graph (shown on the right) that is superior to simple browser history for managing complex exploration, because it can show branching paths and allow users to highlight and mark important resources.
“I call this a tree or a graph saying on the right hand side and it can also be a way help you to navigate back to any previous opened tabs yeah sometimes even just being able to tell you that's something you look at the before reminded you some ways quite useful obviously this is kind of a simple example when you get more tabs it becomes more and more useful”
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.
“and then the yellow one is evidence file so this is more initially was kind of created more like an intelligence analysis context so that's why the word is but to me it's kind of this text you highlighted in the browser that's kind of Nakita information that's important”
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.
“I have a second example which is more kind of daily sense making tasks and here is a find the best cameras for 500 pounds so the context will be we just had a newborn baby so I think we should get a new camera so my wife gave me 500 pound budget so I need to say okay what the best we can get and then you started doing some online research”
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.
“in that process we might came across some technical terms you never heard before then you might do more research just to understand what those things are for example what are the micro forces or mirrorless or DSLR what does that mean”
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.
“the shoot boxer means the things you find interesting you might want to go back to it and look at later or in the process say doing the camera research you might find an article recommending good cameras okay you think that something you would say bookmark in your browser otherwise and you put into your shoe boxes”
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.
“I think usually you people would focusing on the analyzing Parts which what the data analysis or data science is but all other parts are very important and also sometimes you might have to get additional information to help you the process I'm probably very biased but I think we do sense making all the time but I think at least two saying we just do this more often than you think”
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.
“and finally the presentation is explaining not just the answers but actually the process to someone else rather than say have the person to do the same goes through the same process as you did mostly have to done manually by the person”
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.
“so this is kind of a step towards that goal which is a Chrome extension and you can actually download it from the Chrome web store and play but of the usual academic software quality so what it does is it try to visualize the way you have been exploring so far as the graph and rather than say just a list of it's usually seeing a history but also allow you to do other things for example you can highlight images or pictures which you find important”
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.
“and then the hypothesis part is where you can have come up with a few models since within your budget it all looks promising you just need to do a more kind of detailed comparison between them”
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 typical thing is so if you have a browser with open 20 30 Windows that's kind of indication you are doing some sense making yeah”
The Chrome extension currently covers only the early stages of the sense-making process (collection and evidence marking), but has not yet extended to supporting deeper understanding and hypothesis formation for complex topics, which remain largely manual user activities.
“for the particular an extension I just showed so the currently kind of cover these first straight boxes but I haven't quite go beyond say how people understanding a very complex topics that's still very much done manually by the user so the goal will be eventually cover the entire process”
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.
“one of the biggest changes now lots of these visualizations are created automatically using computers obviously in the 1844 sorry 1854 it has to be done manually but and sometimes I think the 1854 one actually looks better”
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.
“we are actually using data validation a lot already so I think if you use the Microsoft Excel you create some bar charts or line charts before and you are doing databization so that's a kind of most common form when people have been doing this for a long time”
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.
“the other very common type of and validation we see a lot now is the dashboard which essentially just a collection of smaller or simpler validations as we're getting more data and becomes more complex or also depends on maybe the task we want to address becomes more complex we need these more”
The text mentions 'red stairs with red and football is green' as a 'terrible example' because memory must be manually allocated and released, introducing malloc as a programming concept.
“red stairs with red and football is green right it's a terrible example because you wanted memory you had to ask for it and release it yourself so um there are two Primitives here one's called malloc and malloc says give me some quantity”
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.
“certainly lost Works quite a bit work needed so if anyone how happy to do some research or collaborate on this will be very useful I'm very much interested to make more this one available to more people and as I mentioned this is kind of typical academic software quality at the moment so definitely needs a bit more software engineering or maintenance”