What I Learned By Building
Project Infobox 
| |
|---|---|
| Self researcher(s) | Dawn Nafus |
| Related tools | Data Sense |
| Related topics | Productivity |
Builds on project(s) |
|
| Has inspired | Projects (0) |
| Show and Tell Talk Infobox | |
|---|---|
| Featured image | 
|
| Date | 2014/05/11 |
| Event name | 2014 QS Europe Conference |
| Slides | |
|
This content was automatically imported. See here how to improve it if any information is missing or out outdated. |
What I Learned By Building is a Show & Tell talk by Dawn Nafus that has been imported from the Quantified Self Show & Tell library.The talk was given on 2014/05/11 and is about Productivity.
Description[edit | edit source]
A description of this project as introduced by Quantified Self follows:
Dawn Nafus, an anthropologist, reflects on some observations of what self-trackers actually do when they make sense of data. Dawn's observations led her to ask: what tools might support more diverse ways of working with data? This short talk describes what she's learned while engaging and building tools for the QS community.
Video and transcript[edit | edit source]
A transcript of this talk is below:
Dawn Nafus What I Learned By Building
Hi I’m Dawn. I’m an anthropologist who’s been studying QS for a little while now, and one of the things that happened in doing that is that it changed my assumptions and my stereotypes about what working with data’s really like. So with apologies to Ann the assumption that I started out with is that data is a thing that comes in raw right. And then some cleaning happens and then some filtering happens and out pops a visualization and that visualization is where the meaning part lives. Now one of the things I noticed was that that’s just not also my naivety, right,. that little part is left vague because we don’t often talk about it. And we also don’t talk about what happens when people who have really important stories to tell with data, also don’t have the necessary skills to tell it. Our stories are shaped by our skills. Some of us are really good visualizers, some of us are great at math; those aren’t the same thing. So I became interested more specifically in how people put together show and tell talks right. We let that data cooking process really was if you’ll go with me on that. And I found that that’s actually where a lot of insight happens, right. It’s a process where we wallow in the data and we sit with the mess, and we make judgements about what is and isn’t important about that data. So it’s in that work I think of transforming the raw data into something ‘cooked’ where the really interesting stuff happens and it’s also where our skills are at in the most diverse. It’s where the skills and the training really matters. So this raised the next research question for me, which is, if data cooking really is about judgement and reflection, then what would make it more possible for more people to reflect in a QS kind of a way right, or to kind of use the QS ethos. Now that’s also a graphic questions but it’s also a design question and it’s also a technical question; all three at once, and one you can’t answer in theory right; you have to learn by doing. And in our case it’s learning about building. So I put together this team. Some of whom may be regretting this and we are all interested in finding this out in our own way. So what we’re building we’re calling Data Sense and it’s a way for us to be throwing out ideas right. So one is maybe visualization is the thing where you do the filtering right, that’s the place where you might grab your high’s in your data rather than writing a script right, where else would that go. The second is that you know time isn’t universal, right so how do we handle that. My work week is going to be really different from yours; I work a four day week. My Spring is going to be different from yours if I’m at a university or in a business right. So how do we make it possible to cluster data that actually takes that seriously? Finally what I’m asking is what would make it possible to sort of eyeball really complex data. What happens so that we don’t flatten the complexity of that data but also make it legible in some way right, in kind of a scaffold that if we sort of squint hard enough maybe the right question starts to emerge without telling us what you should think about your data. So I want to end by telling a story about my favorite moment in the development of Data Sense. We were debating about what to call this button here, and in this view the data’s already been thresholded or it’s already been quartiled I’ll but it that way. And Sangetta who’s our data person really didn’t like this button being called threshold, right, so that button would turn on and off the coloring. So she tried to explain to Pete our designer what a threshold was. She was saying a threshold is a number right, above six, below six; six is the threshold. And this is just adding color so we should be accurate about that. and Pete objected, what on earth are you talking about; he’s a designer right. He said, what are you talking about. The color is the threshold. Otherwise your eyes are scanning around and you’ve got nothing. You’ve got nothing to cross over. You have no threshold.
An so I loved this moment. I really loved it because they’re both right and yet they see data in such profoundly different ways. So I’m not going to claim that Data Sense is going to be the tool that everybody uses. But I will say that if it does mean that more people can have that kind of a conversation we will have learned something.
About the presenter[edit | edit source]
Dawn Nafus gave this talk.