TL;DR: In this foundational study, we wanted to understand how people approach digital data preservation and see if supporting backups would be an interesting problem area to look into. But, by running 23 exploratory interviews, we realized that backups are not a key pain point for users. Instead, the more interesting problem area is supporting meaningful decisions on what data to keep or discard over the long-term, with users displaying behaviors on a spectrum between two extremes: hoarding (keeping most data) and minimalism (getting rid of as much data as possible).
Below, I describe my research process and illustrate how I used generative research to go from an initial assumption to unexpected insights.
Role, team, and timeline
- Led research end-to-end: chose methods & approach; recruited participants; led data collection & analysis; wrote the final report & a research paper about the project
- I worked with Izabelle Janzen and Joanna McGrenere, part of my research lab at The University of British Columbia
- The interviews took place over four weeks in June & July 2017
Research questions and initial focus: are backups a thing?
When I studied how people prepare for an operating system upgrade, I was surprised to see that several participants didn’t do a backup beforehand. So I decided to investigate how people take care of their digital “stuff” over time and make sure not to lose it. I wanted to understand whether backups are still a thing in a world dominated by cloud storage platforms and whether this would be a good problem area to address.
I decided to use exploratory interviews to get a sense of people’s attitudes and focused the project on a broad research question, to keep it open-ended and exploratory:
How do people approach digital data preservation in the cloud age?
Study protocol: keeping interviews open-ended
When setting up the study, I asked participants to bring their own devices to the interview session and then asked them to show me their data as they talked through their practices. I asked them how they managed their digital “stuff”, what they consider important, and what they would miss if they lost their devices.
I also had a practical exercise where I asked participants to sketch the history of their data over the years, to see what they had kept and how as they moved from one device to another.
Take a look at the interview script
Identifying an unexpected problem area: hoarding and minimalism
Now, the initial focus of the study was on backups, but because the interviews were semi-structured, I was able to follow interesting leads brought up by participants. And that’s when something interesting happened.
In the middle of the study, I met a participant with a specific approach to data preservation and management. On the participant’s laptop, there was one main folder, called “Life.” No cloud, no smartphone, only one main folder. Everything that mattered was in there, organized in sub-folders. The participant summarised the approach saying:
“It’s very minimal. I try to delete everything that I don’t need as fast as I can.”
At this point, alarm rings started to go off in my mind and my focus started to shift from backups to a broader concept and more interesting problem area.
What struck me was the way this approach differed from what I had seen up until that point. For example, in the interview before, another participant explained the opposite approach:
“I’m a bit of a hoarder. I just keep all the stuff and nothing ever goes away.”
After these two examples, I kept interviewing participants, now alert to the idea of hoarding and minimalism. And it started to become more clear how these two labels can represent two extremes at the heart of an unexplored problem: what drives user decisions about what personal data to keep or discard. I stopped after reaching “thematic saturation” with 23 interviews.
Using thematic analysis to make sense of participants’ patterns
After finishing up data collection, I used thematic analysis to better understand the patterns I had identified. I systematically went through participants’ examples and descriptions of their data, building conceptual codes, categories, and themes.
I realized that participants had a general approach, but also discussed interesting exceptions. This made it hard to identify them as either “hoarders” or “minimalists.” For example, one participant who self-described as a strong minimalist showed me a large collection of New Yorker articles, a practice that seemed to contradict their minimalist approach. Similar examples pointed to the nuanced nature of user behaviors and made it clear that there was still more to learn about this problem area.
The insights we identified from this set of interviews challenged the initial assumption we had made about backups. We discovered that backups had largely become a non-issue thanks to cloud platforms or OS-level tools and did not represent a strong pain point or compelling user need for many participants. Instead, supporting users in making meaningful decisions around what data to keep or discard represented an unexplored problem area, and an unmet need was having good technology support for their tendency in one way or the other.
From these insights, a new line of design and research work started.
This study had a long-lasting impact both within my research team at UBC and outside of it.
Within my team…
- It prompted new research: we gathered more data to create a set of 5 “user types” that extend this initial spectrum.
- It influenced strategy: we created a set of design concepts directly influenced by the patterns we saw in the study.
- It informed design: we built and evaluated a prototype for a new data management tool that is anchored in the foundational knowledge from this study.
Outside of my team at UBC, this project prompted a new collaboration with Simon Fraser University and helped define a problem area that had received little attention before. Our work was one of the first identifying digital hoarding behaviors. Now, a few years later, multiple disciplines have looked into the topic, offering new perspectives and often building on top of our insights.
In addition, the paper on this study that I presented at the CHI 2018 conference received a Best Paper Award. 🏆
Lessons learned: using generative research to bust assumptions
To this day, this is still one of the studies I am most proud of and one I learned a lot from: in the early stage of exploring a topic or domain, there is incredible value in approaching generative research with broad and open-ended goals because you don’t know what you don’t know yet. Research is a way of building that knowledge and busting initial assumptions that can turn out to be not so interesting after all.
In the paper we published at CHI 2018, you’ll find more details about the project methodology, the results, and what past studies in the field have found.