"Data Thinking" - I love that idea for a course. I've also been struggling with the same gap in knowledge I see in students in a visualization course. Like what you're trying, I'm adding in a module that has no visualization, just asking questions of data first. But there's only so much time of a course that is supposed to be on visualization and communication that I feel I can realistically devote to "data thinking," especially given where the course is supposed to fit within student's overall program/curriculum. Hoping to work in some more of it via feedback on their projects during the course. Eager to see what you come up with, and how you sell it to others/convince others of the need for such a course. Whenever we want to add something, we have to let go of something else in the program.
In my opinion the points, connected with data representation, are two: on one side there is “how we think”, what method we use to create knowledge through hypotheses and confirm them through data.
On the other side, there is the “what we look at”: scientific thinking may not be enough if the reference frame is a linear and reductionist worldview. The effort required is therefore double: an awareness of which tools we use to think, and the ones used by those who present us a thesis; and the comprehension of the basic characteristics of complex thinking, necessary to interpret a complex world.
We need technical skills for data visualization, but the framework should be the understanding of the complexity paradigm, to think before many “right questions” and to place then our results in a wider systemic view.
None is to blame. The idea I get reading this and reflecting on my experience is that people see data visualization as a mere information design problem. Also, there is the other extreme: people doing dataviz without any background whatsoever in information design. Couldn't it be nice to meet in the middle?
I am curious about the first steps in the process you outlined: 1) formulate a data analysis and presentation goal and 2) generate a series of “data questions." How do they arrive at that goal? Are they thinking of the audience and their information needs? This may be part of step 1, but I think it could also be an initial step in itself, maybe Step 0: Identify the audience, their information needs (what questions do they want/need answered), the problem/challenge they are facing, their data proficiency, etc. I think that can help clarify the goal(s) and question(s) and subsequent visualizations. From this perspective, I would also say that much of the time the intended audience's question(s) ARE the data questions. However, the audience may not know how to formulate these well and the visualization designer or analyst will need to prompt them for clarity and precision and revision. This is an iterative, time consuming process.
I agree that people don't intuitively know how to formulate good data questions, and this is not something that is taught (well or at all) in K-12 education. Students' and working professionals' initial data questions are often vague, not measurable, and/or not able to be answered. I think people need to be taught the characteristics of good data questions (e.g., well-defined, answerable, actionable, unbiased, relevant, etc.), with examples, and given opportunities to critique and improve sample data questions, as well as their own. They should also be taught how to dialogue with the audience/stakeholder so they can refine a bad data question into a good one.
I have found in my own teaching, as you have, that a lot of the time there is a lack of alignment between the audience needs/stated purpose, questions, and visualization/analysis. I am a stickler for alignment. I think it's useful to give students samples (descriptions of audience, statements of purpose, questions, data visualizations), some that are aligned, and some that lack alignment, along with a rubric or set of criteria, and ask them to critique the samples first (and justify their evaluations), and then use the same rubric/criteria to evaluate their own work. This can help them internalize what this looks like and apply it their work.
These are just a few of my ideas and some things that I have done in my own work. Not sure if there's anything new or different here, but I thought I'd share them.
I think the lack of data thinking is really a lack of (or limited) data literacy. Data literacy is not just a technical skill; it’s also a way of thinking about and with data. It’s key to successfully working successfully with data (including visualizing it).
I can't say I'm too surprised to read of the challenges students have thinking first about data in an analytical way - that's a graduate-level skill that is practiced usually when students start generating their own data and exploring it (at least in a research context). We can quickly take for granted how difficult this non-technical part of the skillset is.
What sounds like is missing is the more 'exploratory' aspect of the analytical approach, where one 'probes' a dataset using a combination of scatterplots, bar charts and frequency histograms.
There's an analogy to draw with academic science papers here, often scientists (especially of an older generation) don't have much training in graphic design, but are yet able to craft compelling and convincing stories, building narratives from a limited number of graph types.
Maybe there's an exercise here .. before trying to design a perfect visualization, the student can show that they've exhaustively asked the right questions using only scatterplots and histograms, before any design takes place?
"Data Thinking" - I love that idea for a course. I've also been struggling with the same gap in knowledge I see in students in a visualization course. Like what you're trying, I'm adding in a module that has no visualization, just asking questions of data first. But there's only so much time of a course that is supposed to be on visualization and communication that I feel I can realistically devote to "data thinking," especially given where the course is supposed to fit within student's overall program/curriculum. Hoping to work in some more of it via feedback on their projects during the course. Eager to see what you come up with, and how you sell it to others/convince others of the need for such a course. Whenever we want to add something, we have to let go of something else in the program.
Hi Enrico, I agree with you.
I wrote few months ago this article https://medium.com/@ciemme.25/complex-visualizations-and-visualized-complexity-how-can-we-interpret-the-world-around-us-122a76de807a
In my opinion the points, connected with data representation, are two: on one side there is “how we think”, what method we use to create knowledge through hypotheses and confirm them through data.
On the other side, there is the “what we look at”: scientific thinking may not be enough if the reference frame is a linear and reductionist worldview. The effort required is therefore double: an awareness of which tools we use to think, and the ones used by those who present us a thesis; and the comprehension of the basic characteristics of complex thinking, necessary to interpret a complex world.
We need technical skills for data visualization, but the framework should be the understanding of the complexity paradigm, to think before many “right questions” and to place then our results in a wider systemic view.
None is to blame. The idea I get reading this and reflecting on my experience is that people see data visualization as a mere information design problem. Also, there is the other extreme: people doing dataviz without any background whatsoever in information design. Couldn't it be nice to meet in the middle?
I am curious about the first steps in the process you outlined: 1) formulate a data analysis and presentation goal and 2) generate a series of “data questions." How do they arrive at that goal? Are they thinking of the audience and their information needs? This may be part of step 1, but I think it could also be an initial step in itself, maybe Step 0: Identify the audience, their information needs (what questions do they want/need answered), the problem/challenge they are facing, their data proficiency, etc. I think that can help clarify the goal(s) and question(s) and subsequent visualizations. From this perspective, I would also say that much of the time the intended audience's question(s) ARE the data questions. However, the audience may not know how to formulate these well and the visualization designer or analyst will need to prompt them for clarity and precision and revision. This is an iterative, time consuming process.
I agree that people don't intuitively know how to formulate good data questions, and this is not something that is taught (well or at all) in K-12 education. Students' and working professionals' initial data questions are often vague, not measurable, and/or not able to be answered. I think people need to be taught the characteristics of good data questions (e.g., well-defined, answerable, actionable, unbiased, relevant, etc.), with examples, and given opportunities to critique and improve sample data questions, as well as their own. They should also be taught how to dialogue with the audience/stakeholder so they can refine a bad data question into a good one.
I have found in my own teaching, as you have, that a lot of the time there is a lack of alignment between the audience needs/stated purpose, questions, and visualization/analysis. I am a stickler for alignment. I think it's useful to give students samples (descriptions of audience, statements of purpose, questions, data visualizations), some that are aligned, and some that lack alignment, along with a rubric or set of criteria, and ask them to critique the samples first (and justify their evaluations), and then use the same rubric/criteria to evaluate their own work. This can help them internalize what this looks like and apply it their work.
These are just a few of my ideas and some things that I have done in my own work. Not sure if there's anything new or different here, but I thought I'd share them.
I think the lack of data thinking is really a lack of (or limited) data literacy. Data literacy is not just a technical skill; it’s also a way of thinking about and with data. It’s key to successfully working successfully with data (including visualizing it).
Hi Enrico, thanks for this thoughtful discussion.
I can't say I'm too surprised to read of the challenges students have thinking first about data in an analytical way - that's a graduate-level skill that is practiced usually when students start generating their own data and exploring it (at least in a research context). We can quickly take for granted how difficult this non-technical part of the skillset is.
What sounds like is missing is the more 'exploratory' aspect of the analytical approach, where one 'probes' a dataset using a combination of scatterplots, bar charts and frequency histograms.
There's an analogy to draw with academic science papers here, often scientists (especially of an older generation) don't have much training in graphic design, but are yet able to craft compelling and convincing stories, building narratives from a limited number of graph types.
Maybe there's an exercise here .. before trying to design a perfect visualization, the student can show that they've exhaustively asked the right questions using only scatterplots and histograms, before any design takes place?