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Personal Analytics

November 22nd, 2012

We had to spend a bit of time trying to find a better name for what we do than “User Centric Activity Data”. The UCIAD study report settled for “consumer activity data”, in relation to consumer data as considered for example in midata. However, for what concerns the tools that allow us to expose users to their own activity data (such as the UCIAD dashboard), we used the possibly more appropriate term of “personal analytics”. And personal analytics has received quite a lot of attention lately.

It started with Stephen Wolfram posting an article with a lot of analytics about his online activities. This has made quite a buzz, showing how (rather simple) computational techniques and visualisations could be quite revealing to an individual. As a follow-up from that, a personal analytics feature was added to Wolfram|Alpha, using the Wolfram|Alpha engine to provide analyses and visualisation of your activity on Facebook.

Another of such tools, which is currently very much at “prototype” level, is the MOLUTI chrome extension (see on the Chrome webstore). It shows you some interactive visualisations of you web history in the Chrome browser. What is interesting with this is the simple mechanism it provides to filter activities (e.g., on what web site did I look about “child stair gates” last week end?), and also that it makes it possible to share the results of these filters, in the form of what it calls “browse lists” (list of links with a tag cloud).

We argued in the past about the use of such tools, and for the advantages users might have from having access to ways to understand and query their own activities. No doubt that this area has a very bright future, as the need for these personal analytics tools can only grow with the increase in online activities.

The UCIAD User Study: Report

August 14th, 2012

Our goal for the second phase of the UCIAD project was to investigate, through a user study, how people would actually use their own activity data if they were to get access to it and how such access would impact on the organisation that has been collecting the data. We achieved that by building a a technical architecture, collecting data from Open University log systems and exposing them to the corresponding users, through a compelling interface. We collected reactions and thoughts from the users through individual interviews, questionnaires and a “focus group” meeting confronting the varying ideas and opinions about the overall notion of user-centric activity data (or as we might call them, in a simpler way, consumer activity data).

The results of this user study, together with more details on the methodology we employed and the technical platform are now summarised in a complete, self-contained report:


Consumer Activity Data: Usages and Challenges

This report is licensed under Creative Commons Attribution and the source code of most of the technological platform is available as open source software.