Hi, I am Francesco Vitale and I am going to present our paper titled "Data Dashboard:
Exploring Centralization and Customization in Personal Data Curation."
This is work I conducted with my colleagues Janet Chen, William Odom, and Joanna McGrenere
from The University of British Columbia and Simon Fraser University.
When we talk about personal data today, we have to consider a wide range of types: documents,
photos, emails, files, but also mobile applications, tracking data, logs, and many more. The range
and amount of personal data that people create, save, and share every day continues to grow.
And with this growth, it becomes hard to curate data, decide what to keep or discard, and
how to manage kept things.
ones we focus on in this paper, so that we can design technologies that better support
These activities are what researchers define as personal data curation and they are the
There are two key challenges for designing tools that can support personal data curation.
user needs.
and cloud platforms. It is often hard for users to know what they own and where it is
The first challenge is data fragmentation. Personal data is spread across many devices
stored. This makes curation decisions more challenging than they should be.
The second challenge for design are the strong individual preferences that users display.
Data management and curation are very subjective activities. A tool that works for one user
might not work for another.
To overcome these challenges, we focused our key research questions on two complementary
design approaches: centralization and customization. We asked: "Can providing a central overview
of data from different devices and platforms in one single place help people curate their
data?"
And: "Can a personalized and customizable interface meet the needs of different users?"
Our work shows that yes, combining centralization and customization is a promising design approach
that can balance conflicting user needs, as long as it respects some key constraints.
How did we get to this conclusion?
We took a Research through Design approach and we developed Data Dashboard, a prototype
system that embodies both centralization and customization as key design ideas. Then, we
used the prototype in a user study with 18 participants as a tool to elicit reactions.
Our focus was not on finding usability issues, but on eliciting deeper reactions to the two
key design ideas behind the prototype, centralization and customization.
Now I'll walk you through the Data Dashboard prototype in detail, and then I will briefly
touch on the user study and analysis.
The Data Dashboard prototype consists of four main sections: Activity, Explore Your Data,
Quick Actions, Settings.
The main idea behind the prototype is that it provides an aggregated overview of all
to different items.
personal data that a user might have on different devices and cloud platforms, providing links
In the first section, Activity, users can see an overview of data that they have created,
saved, or interacted with recently, like documents, messages, applications, shared data, and so
on. They can filter the data by time.
In Explore Your Data, users can see an overview of all of their data grouped into different
types, like photos, contacts, bookmarks, tracking data. They can also customize the different
types they see in this section.
And then they can use a set of filters on the left to sort through different items.
For example, they can see data to get rid of, data to organize, private information,
data to back up or sync. When they click on one of the options, the system will show a
list of items and more details.
The next section, Quick Actions, instead, provides suggestions for quick actions that
users can take. For example, renaming documents, removing duplicates, archiving message conversations,
or reviewing private information. Similar to Explore Your Data, users can filter the
We designed Quick Actions for users who want proactive technology for curating their data,
suggestions by using the options on the left.
as opposed to users who want more granular control and might prefer something like Explore
Then, users can customize how the filters from Quick Actions and Explore Your Data work
Your Data.
Here they can change the default options for all the different filters and they can also
by going into Settings.
create their own advanced combinations for different types of data and different criteria.
They can also add or remove different devices and accounts.
So that was the prototype. We evaluated the Data Dashboard prototype using interviews
with 18 participants who had different approaches for curating their data.
The interviews focused on 5 different use scenarios for the prototype, ranging from
privacy management to everyday data curation.
In our analysis of the interviews, we identified a key idea to understand reactions to the
prototype: the idea of data boundaries, an idea that past work already mentions.
Data boundaries are a metaphor: invisible, personal lines that prescribe where and how
to store data. We did not have this idea in mind when designing the prototype, but we
derived it from participants' reactions and then traced it back to related work.
By looking at participants' reactions, we understood how important data boundaries are
in driving curation behaviors. For example, one participant explained: “I have my data
compartmentalized: the tablet is for reading, the laptop for everything school related."
With this metaphor in mind, we saw that centralization can blur boundaries, so participants had slightly
more negative reactions to it and wanted explicit safety guarantees for it.
Customization, on the other hand, upholds boundaries, so it was easier to accept.
But when considered together, centralization and customization represent a promising design
Based on these results, we discuss some key design directions that future work can focus
approach, one that can balance conflicting user needs
on.
First, integrating data boundaries explicitly into design, by creating adaptive mechanisms
that can support contextual user behaviors. And we provide some examples in the paper.
Then, rethinking the language of personal data by promoting data literacy through consistency
and standards. We saw how user interface language was key for supporting curation behaviors
in the paper.
and we think that more work should go into it. Again, we provide more examples of this
Finally, envisioning a post-cloud future focused on data interoperability, decentralization,
and the idea of social storage.
Altogether, we believe that these directions will help in building innovative tools that
can help people have more control and better curate their data.
You'll find more details about the prototype, a set of rich qualitative results from our
Thanks for your attention!
analysis, and an outline of our key design directions in the paper.